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  • 1.
    Andersson, Robin
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Bruder, Carl E G
    Piotrowski, Arkadiusz
    Menzel, Uwe
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Nord, Helena
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Sandgren, Johanna
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper.
    Hvidsten, Torgeir R
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    de Ståhl, Teresita Diaz
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Dumanski, Jan P
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    A Segmental Maximum A Posteriori Approach to Genome-wide Copy Number Profiling2008Inngår i: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 24, nr 6, s. 751-758Artikkel i tidsskrift (Annet vitenskapelig)
    Abstract [en]

    MOTIVATION: Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis. RESULTS: We introduce a novel Segmental Maximum A Posteriori approach, SMAP, for DNA copy number profiling. Our method is based on discrete-index Hidden Markov Modeling and incorporates genomic distance and overlap between clones. We exploit a priori information through user-controllable parameterization that enables the identification of copy number deviations of various lengths and amplitudes. The model parameters may be inferred at a genome-wide scale to avoid overfitting of model parameters often resulting from chromosome-wise model inference. We report superior performances of SMAP on synthetic data when compared with two recent methods. When applied on our new experimental data, SMAP readily recognizes already known genetic aberrations including both large-scale regions with aberrant DNA copy number and changes affecting only single features on the array. We highlight the differences between the prediction of SMAP and the compared methods and show that SMAP accurately determines copy number changes and benefits from overlap consideration.

  • 2.
    Andersson, Robin
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Enroth, Stefan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Rada-Iglesias, Alvaro
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Nucleosomes are well positioned in exons and carry characteristic histone modifications2009Inngår i: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 19, nr 10, s. 1732-1741Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The genomes of higher organisms are packaged in nucleosomes with functional histone modifications. Until now, genome-wide nucleosome and histone modification studies have focused on transcription start sites (TSSs) where nucleosomes in RNA polymerase II (RNAPII) occupied genes are well positioned and have histone modifications that are characteristic of expression status. Using public data, we here show that there is a higher nucleosome-positioning signal in internal human exons and that this positioning is independent of expression. We observed a similarly strong nucleosome-positioning signal in internal exons of C. elegans. Among the 38 histone modifications analyzed in man, H3K36me3, H3K79me1, H2BK5me1, H3K27me1, H3K27me2 and H3K27me3 had evidently higher signal in internal exons than in the following introns and were clearly related to exon expression. These observations are suggestive of roles in splicing. Thus, exons are not only characterized by their coding capacity but also by their nucleosome organization, which seems evolutionary conserved since it is present in both primates and nematodes.

  • 3.
    Andersson, Robin
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Centrum för bioinformatik.
    Vitoria, Aida
    Maluszynski, Jan
    Komorowski, Jan
    RoSy: A Rough Knowledge Base System2005Inngår i: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 10th International Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, 2005, Proceedings, Part II, 2005, s. 48-58Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a user-oriented view of RoSy, a Rough Knowledge Base System. The system tackles two problems not fully answered by previous research: the ability to define rough sets in terms of other rough sets and incorporation of domain or expert knowledge. We describe two main components of RoSy: knowledge base creation and query answering. The former allows the user to create a knowledge base of rough concepts and checks that the definitions do not cause what we will call a model failure. The latter gives the user a possibility to query rough concepts defined in the knowledge base. The features of RoSy are described using examples. The system is currently available on a web site for online interactions.

  • 4.
    Baltzer, Nicholas
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    ||-ROSETTAManuskript (preprint) (Annet vitenskapelig)
  • 5.
    Baltzer, Nicholas
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Sundström, Karin
    Nygård, Jan
    Nygård, Mari
    Dillner, Joakim
    Risk Stratification in Cervical Cancer Screening – Validation and Generalization of a Data-driven  Screening Recall ModelManuskript (preprint) (Annet vitenskapelig)
  • 6.
    Baltzer, Nicholas
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm County, Sweden.
    Sundström, Karin
    Karolinska Inst, Dept Lab Med, Stockholm, Stockholm Count, Sweden..
    Nygård, Jan F.
    Canc Registry Norway, Dept Registry Informat, Oslo, Oslo County, Norway..
    Dillner, Joakim
    Karolinska Inst, Dept Lab Med, Stockholm, Stockholm Count, Sweden..
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, Warsaw, Warsaw County, Poland..
    Risk stratification in cervical cancer screening by complete screening history: Applying bioinformatics to a general screening population2017Inngår i: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 141, nr 1, s. 200-209Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Women screened for cervical cancer in Sweden are currently treated under a one-size-fits-all programme, which has been successful in reducing the incidence of cervical cancer but does not use all of the participants' available medical information. This study aimed to use women's complete cervical screening histories to identify diagnostic patterns that may indicate an increased risk of developing cervical cancer. A nationwide case-control study was performed where cervical cancer screening data from 125,476 women with a maximum follow-up of 10 years were evaluated for patterns of SNOMED diagnoses. The cancer development risk was estimated for a number of different screening history patterns and expressed as Odds Ratios (OR), with a history of 4 benign cervical tests as reference, using logistic regression. The overall performance of the model was moderate (64% accuracy, 71% area under curve) with 61-62% of the study population showing no specific patterns associated with risk. However, predictions for high-risk groups as defined by screening history patterns were highly discriminatory with ORs ranging from 8 to 36. The model for computing risk performed consistently across different screening history lengths, and several patterns predicted cancer outcomes. The results show the presence of risk-increasing and risk-decreasing factors in the screening history. Thus it is feasible to identify subgroups based on their complete screening histories. Several high-risk subgroups identified might benefit from an increased screening density. Some low-risk subgroups identified could likely have a moderately reduced screening density without additional risk.

  • 7.
    Barrenäs, Fredrik
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Univ Washington, Dept Microbiol, Seattle, WA 98195 USA.
    Raehtz, Kevin
    Univ Pittsburgh, Dept Med, Div Infect Dis, Pittsburgh, PA USA;Univ Pittsburgh, Sch Med, Dept Microbiol & Mol Genet, Pittsburgh, PA USA.
    Xu, Cuiling
    Univ Pittsburgh, Sch Med, Dept Pathol, Pittsburgh, PA USA.
    Law, Lynn
    Univ Washington, Dept Immunol, Seattle, WA 98195 USA;Univ Washington, Ctr Innate Immun & Immune Dis, Seattle, WA 98195 USA.
    Green, Richard R.
    Univ Washington, Dept Immunol, Seattle, WA 98195 USA;Univ Washington, Ctr Innate Immun & Immune Dis, Seattle, WA 98195 USA.
    Silvestri, Guido
    Emory Univ, Dept Pathol & Lab Med, Atlanta, GA 30322 USA;Emory Univ, Yerkes Natl Primate Res Ctr, Div Microbiol & Immunol, Atlanta, GA 30322 USA.
    Bosinger, Steven E.
    Emory Univ, Dept Pathol & Lab Med, Atlanta, GA 30322 USA;Emory Univ, Yerkes Natl Primate Res Ctr, Div Microbiol & Immunol, Atlanta, GA 30322 USA.
    Nishida, Andrew
    Univ Washington, Dept Microbiol, Seattle, WA 98195 USA.
    Li, Qingsheng
    Univ Nebraska, Sch Biol Sci, Nebraska Ctr Virol, Lincoln, NE USA.
    Lu, Wuxun
    Univ Nebraska, Sch Biol Sci, Nebraska Ctr Virol, Lincoln, NE USA.
    Zhang, Jianshui
    Univ Nebraska, Sch Biol Sci, Nebraska Ctr Virol, Lincoln, NE USA.
    Thomas, Matthew J.
    Univ Washington, Dept Immunol, Seattle, WA 98195 USA;Univ Washington, Washington Natl Primate Res Ctr, Seattle, WA 98195 USA.
    Chang, Jean
    Univ Washington, Dept Immunol, Seattle, WA 98195 USA;Univ Washington, Ctr Innate Immun & Immune Dis, Seattle, WA 98195 USA.
    Smith, Elise
    Univ Washington, Dept Immunol, Seattle, WA 98195 USA;Univ Washington, Ctr Innate Immun & Immune Dis, Seattle, WA 98195 USA.
    Weiss, Jeffrey M.
    Univ Washington, Dept Microbiol, Seattle, WA 98195 USA.
    Dawoud, Reem A.
    Emory Univ, Dept Pathol & Lab Med, Atlanta, GA 30322 USA.
    Richter, George H.
    Univ Pittsburgh, Sch Med, Dept Pathol, Pittsburgh, PA USA.
    Trichel, Anita
    Univ Pittsburgh, Div Lab Anim Resources, Pittsburgh, PA USA.
    Ma, Dongzhu
    Univ Pittsburgh, Dept Orthoped Surg, Pittsburgh, PA USA.
    Peng, Xinxia
    North Carolina State Univ, Dept Mol Biomed Sci, Raleigh, NC 27695 USA.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Apetrei, Cristian
    Univ Pittsburgh, Dept Med, Div Infect Dis, Pittsburgh, PA USA;Univ Pittsburgh, Sch Med, Dept Microbiol & Mol Genet, Pittsburgh, PA USA.
    Pandrea, Ivona
    Univ Pittsburgh, Sch Med, Dept Microbiol & Mol Genet, Pittsburgh, PA USA;Univ Pittsburgh, Sch Med, Dept Pathol, Pittsburgh, PA USA.
    Gale, Michael, Jr.
    Univ Washington, Dept Immunol, Seattle, WA 98195 USA;Univ Washington, Ctr Innate Immun & Immune Dis, Seattle, WA 98195 USA;Univ Washington, Washington Natl Primate Res Ctr, Seattle, WA 98195 USA.
    Macrophage-associated wound healing contributes to African green monkey SIV pathogenesis control2019Inngår i: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 10, artikkel-id 5101Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Natural hosts of simian immunodeficiency virus (SIV) avoid AIDS despite lifelong infection. Here, we examined how this outcome is achieved by comparing a natural SIV host, African green monkey (AGM) to an AIDS susceptible species, rhesus macaque (RM). To asses gene expression profiles from acutely SIV infected AGMs and RMs, we developed a systems biology approach termed Conserved Gene Signature Analysis (CGSA), which compared RNA sequencing data from rectal AGM and RM tissues to various other species. We found that AGMs rapidly activate, and then maintain, evolutionarily conserved regenerative wound healing mechanisms in mucosal tissue. The wound healing protein fibronectin shows distinct tissue distribution and abundance kinetics in AGMs. Furthermore, AGM monocytes exhibit an embryonic development and repair/regeneration signature featuring TGF-beta and concomitant reduced expression of inflammatory genes compared to RMs. This regenerative wound healing process likely preserves mucosal integrity and prevents inflammatory insults that underlie immune exhaustion in RMs.

    Fulltekst (pdf)
    FULLTEXT01
  • 8.
    Bergström, Ulrika
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi, Ekotoxikologi. Ekotoxikologi.
    Olsson, Jan A
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi, Ekotoxikologi. Ekotoxikologi.
    Hvidsten, Torgeir R
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi, Ekotoxikologi.
    Brandt, Ingvar
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi, Ekotoxikologi. Ekotoxikologi.
    Altered gene expression in the olfactory bulb following exposure to 2,6-dichlorophenyl methylsulfone2005Inngår i: Toxicology Letters 158 Supp 1., 2005, s. 61-Konferansepaper (Annet vitenskapelig)
  • 9. Birney, Ewan
    et al.
    Stamatoyannopoulos, John A.
    Dutta, Anindya
    Guigó, Roderic
    Gingeras, Thomas R.
    Margulies, Elliott H.
    Weng, Zhiping
    Snyder, Michael
    Dermitzakis, Emmanouil T.
    Thurman, Robert E.
    Kuehn, Michael S.
    Taylor, Christopher M.
    Neph, Shane
    Koch, Christoph M.
    Asthana, Saurabh
    Malhotra, Ankit
    Adzhubei, Ivan
    Greenbaum, Jason A.
    Andrews, Robert M.
    Flicek, Paul
    Boyle, Patrick J.
    Cao, Hua
    Carter, Nigel P.
    Clelland, Gayle K.
    Davis, Sean
    Day, Nathan
    Dhami, Pawandeep
    Dillon, Shane C.
    Dorschner, Michael O.
    Fiegler, Heike
    Giresi, Paul G.
    Goldy, Jeff
    Hawrylycz, Michael
    Haydock, Andrew
    Humbert, Richard
    James, Keith D.
    Johnson, Brett E.
    Johnson, Ericka M.
    Frum, Tristan T.
    Rosenzweig, Elizabeth R.
    Karnani, Neerja
    Lee, Kirsten
    Lefebvre, Gregory C.
    Navas, Patrick A.
    Neri, Fidencio
    Parker, Stephen C.
    Sabo, Peter J.
    Sandstrom, Richard
    Shafer, Anthony
    Vetrie, David
    Weaver, Molly
    Wilcox, Sarah
    Yu, Man
    Collins, Francis S.
    Dekker, Job
    Lieb, Jason D.
    Tullius, Thomas D.
    Crawford, Gregory E.
    Sunyaev, Shamil
    Noble, William S.
    Dunham, Ian
    Denoeud, France
    Reymond, Alexandre
    Kapranov, Philipp
    Rozowsky, Joel
    Zheng, Deyou
    Castelo, Robert
    Frankish, Adam
    Harrow, Jennifer
    Ghosh, Srinka
    Sandelin, Albin
    Hofacker, Ivo L.
    Baertsch, Robert
    Keefe, Damian
    Dike, Sujit
    Cheng, Jill
    Hirsch, Heather A.
    Sekinger, Edward A.
    Lagarde, Julien
    Abril, Josep F.
    Shahab, Atif
    Flamm, Christoph
    Fried, Claudia
    Hackermüller, Jörg
    Hertel, Jana
    Lindemeyer, Manja
    Missal, Kristin
    Tanzer, Andrea
    Washietl, Stefan
    Korbel, Jan
    Emanuelsson, Olof
    Pedersen, Jakob S.
    Holroyd, Nancy
    Taylor, Ruth
    Swarbreck, David
    Matthews, Nicholas
    Dickson, Mark C.
    Thomas, Daryl J.
    Weirauch, Matthew T.
    Gilbert, James
    Drenkow, Jorg
    Bell, Ian
    Zhao, XiaoDong
    Srinivasan, K. G.
    Sung, Wing-Kin
    Ooi, Hong Sain
    Chiu, Kuo Ping
    Foissac, Sylvain
    Alioto, Tyler
    Brent, Michael
    Pachter, Lior
    Tress, Michael L.
    Valencia, Alfonso
    Choo, Siew Woh
    Choo, Chiou Yu
    Ucla, Catherine
    Manzano, Caroline
    Wyss, Carine
    Cheung, Evelyn
    Clark, Taane G.
    Brown, James B.
    Ganesh, Madhavan
    Patel, Sandeep
    Tammana, Hari
    Chrast, Jacqueline
    Henrichsen, Charlotte N.
    Kai, Chikatoshi
    Kawai, Jun
    Nagalakshmi, Ugrappa
    Wu, Jiaqian
    Lian, Zheng
    Lian, Jin
    Newburger, Peter
    Zhang, Xueqing
    Bickel, Peter
    Mattick, John S.
    Carninci, Piero
    Hayashizaki, Yoshihide
    Weissman, Sherman
    Hubbard, Tim
    Myers, Richard M.
    Rogers, Jane
    Stadler, Peter F.
    Lowe, Todd M.
    Wei, Chia-Lin
    Ruan, Yijun
    Struhl, Kevin
    Gerstein, Mark
    Antonarakis, Stylianos E.
    Fu, Yutao
    Green, Eric D.
    Karaöz, U.
    Siepel, Adam
    Taylor, James
    Liefer, Laura A
    Wetterstrand, Kris A.
    Good, Peter J.
    Feingold, Elise A.
    Guyer, Mark S.
    Cooper, Gregory M.
    Asimenos, George
    Dewey, Colin N.
    Hou, Minmei
    Nikolaev, Sergey
    Montoya-Burgos, Juan I.
    Löytynoja, Ari
    Whelan, Simon
    Pardi, Fabio
    Massingham, Tim
    Huang, Haiyan
    Zhang, Nancy R.
    Holmes, Ian
    Mullikin, James C.
    Ureta-Vidal, Abel
    Paten, Benedict
    Seringhaus, Michael
    Church, Deanna
    Rosenbloom, Kate
    Kent, W. James
    Stone, Eric A.
    Batzoglou, Serafim
    Goldman, Nick
    Hardison, Ross C.
    Haussler, David
    Miller, Webb
    Sidow, Arend
    Trinklein, Nathan D.
    Zhang, Zhengdong D.
    Barrera, Leah
    Stuart, Rhona
    King, David C.
    Ameur, Adam
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Enroth, Stefan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Bieda, Mark C.
    Kim, Jonghwan
    Bhinge, Akshay A.
    Jiang, Nan
    Liu, Jun
    Yao, Fei
    Vega, Vinsensius B.
    Lee, Charlie W.
    Ng, Patrick
    Shahab, Atif
    Yang, Annie
    Moqtaderi, Zarmik
    Zhu, Zhou
    Xu, Xiaoqin
    Squazzo, Sharon
    Oberley, Matthew J.
    Inman, David
    Singer, Michael A.
    Richmond, Todd A.
    Munn, Kyle J.
    Rada-Iglesias, Alvaro
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Fowler, Joanna C.
    Couttet, Phillippe
    Bruce, Alexander W.
    Dovey, Oliver M.
    Ellis, Peter D.
    Langford, Cordelia F.
    Nix, David A.
    Euskirchen, Ghia
    Hartman, Stephen
    Urban, Alexander E.
    Kraus, Peter
    Van Calcar, Sara
    Heintzman, Nate
    Kim, Tae Hoon
    Wang, Kun
    Qu, Chunxu
    Hon, Gary
    Luna, Rosa
    Glass, Christopher K.
    Rosenfeld, M. Geoff
    Aldred, Shelley Force
    Cooper, Sara J.
    Halees, Anason
    Lin, Jane M.
    Shulha, Hennady P.
    Zhang, Xiaoling
    Xu, Mousheng
    Haidar, Jaafar N.
    Yu, Yong
    Ruan, Yijun
    Iyer, Vishwanath R.
    Green, Roland D.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Farnham, Peggy J.
    Ren, Bing
    Harte, Rachel A.
    Hinrichs, Angie S.
    Trumbower, Heather
    Clawson, Hiram
    Hillman-Jackson, Jennifer
    Zweig, Ann S.
    Smith, Kayla
    Thakkapallayil, Archana
    Barber, Galt
    Kuhn, Robert M.
    Karolchik, Donna
    Armengol, Lluis
    Bird, Christine P.
    de Bakker, Paul I.
    Kern, Andrew D.
    Lopez-Bigas, Nuria
    Martin, Joel D.
    Stranger, Barbara E.
    Woodroffe, Abigail
    Davydov, Eugene
    Dimas, Antigone
    Eyras, Eduardo
    Hallgrí­msdóttir, Ingileif B.
    Huppert, Julian
    Zody, Michael C.
    Abecasis, G. R.
    Estivill, Xavier
    Bouffard, Gerard G.
    Guan, Xiaobin
    Hansen, Nancy F.
    Idol, Jacquelyn R.
    Maduro, Valerie V.
    Maskeri, Baishali
    McDowell, Jennifer C.
    Park, Morgan
    Thomas, Pamela J.
    Young, Alice C.
    Blakesley, Robert W.
    Muzny, Donna M.
    Sodergren, Erica
    Wheeler, David A.
    Worley, Kim C.
    Jiang, Huaiyang
    Weinstock, George M.
    Gibbs, Richard A.
    Graves, Tina
    Fulton, Robert
    Mardis, Elaine R.
    Wilson, Richard K.
    Clamp, Michele
    Cuff, James
    Gnerre, Sante
    Jaffe, David B.
    Chang, Jean L.
    Lindblad-Toh, Kerstin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi.
    Lander, Eric S.
    Koriabine, Maxim
    Nefedov, Mikhail
    Osoegawa, Kazutoyo
    Yoshinaga, Yuko
    Zhu, Baoli
    de Jong, Pieter J.
    Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project2007Inngår i: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 447, nr 7146, s. 799-816Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

  • 10.
    Bornelöv, Susanne
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Enroth, Stefan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Visualization of Rules in Rule-Based Classifiers2012Inngår i: INTELLIGENT DECISION TECHNOLOGIES (IDT'2012), VOL 1, 2012, Vol. 15, s. 329-338Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Interpretation and visualization of the classification models are important parts of machine learning. Rule-based classifiers often contain too many rules to be easily interpreted by humans, and methods for post-classification analysis of the rules are needed. Here we present a strategy for circular visualization of sets of classification rules. The Circos software was used to generate graphs showing all pairs of conditions that were present in the rules as edges inside a circle. We showed using simulated data that all two-way interactions in the data were found by the classifier and displayed in the graph, although the single attributes were constructed to have no correlation to the decision class. For all examples we used rules trained using the rough set theory, but the visualization would by applicable to any sort of classification rules. This method for rule visualization may be useful for applications where interaction terms are expected, and the size of the model limits the interpretability.

  • 11.
    Bornelöv, Susanne
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi.
    Different distribution of histone modifications in genes with unidirectional and bidirectional transcription and a role of CTCF and cohesin in directing transcription2015Inngår i: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 16, artikkel-id 300Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Several post-translational histone modifications are mainly found in gene promoters and are associated with the promoter activity. It has been hypothesized that histone modifications regulate the transcription, as opposed to the traditional view with transcription factors as the key regulators. Promoters of most active genes do not only initiate transcription of the coding sequence, but also a substantial amount of transcription of the antisense strand upstream of the transcription start site (TSS). This promoter feature has generally not been considered in previous studies of histone modifications and transcription factor binding.

    Results: We annotated protein-coding genes as bi- or unidirectional depending on their mode of transcription and compared histone modifications and transcription factor occurrences between them. We found that H3K4me3, H3K9ac, and H3K27ac were significantly more enriched upstream of the TSS in bidirectional genes compared with the unidirectional ones. In contrast, the downstream histone modification signals were similar, suggesting that the upstream histone modifications might be a consequence of transcription rather than a cause. Notably, we found well-positioned CTCF and RAD21 peaks approximately 60-80 bp upstream of the TSS in the unidirectional genes. The peak heights were related to the amount of antisense transcription and we hypothesized that CTCF and cohesin act as a barrier against antisense transcription.

    Conclusions: Our results provide insights into the distribution of histone modifications at promoters and suggest a novel role of CTCF and cohesin as regulators of transcriptional direction.

    Fulltekst (pdf)
    fulltext
  • 12.
    Bornelöv, Susanne
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Marillet, Simon
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Ciruvis: a web-based tool for rule networks and interaction detection using rule-based classifiers2014Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 15, s. 139-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: The use of classification algorithms is becoming increasingly important for the field of computational biology. However, not only the quality of the classification, but also its biological interpretation is important. This interpretation may be eased if interacting elements can be identified and visualized, something that requires appropriate tools and methods. Results: We developed a new approach to detecting interactions in complex systems based on classification. Using rule-based classifiers, we previously proposed a rule network visualization strategy that may be applied as a heuristic for finding interactions. We now complement this work with Ciruvis, a web-based tool for the construction of rule networks from classifiers made of IF-THEN rules. Simulated and biological data served as an illustration of how the tool may be used to visualize and interpret classifiers. Furthermore, we used the rule networks to identify feature interactions, compared them to alternative methods, and computationally validated the findings. Conclusions: Rule networks enable a fast method for model visualization and provide an exploratory heuristic to interaction detection. The tool is made freely available on the web and may thus be used to aid and improve rule-based classification.

    Fulltekst (pdf)
    fulltext
  • 13.
    Bornelöv, Susanne
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Saaf, Annika
    Melen, Erik
    Bergstrom, Anna
    Moghadam, Behrooz Torabi
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Pulkkinen, Ville
    Acevedo, Nathalie
    Pietras, Christina Orsmark
    Ege, Markus
    Braun-Fahrlaender, Charlotte
    Riedler, Josef
    Doekes, Gert
    Kabesch, Michael
    van Hage, Marianne
    Kere, Juha
    Scheynius, Annika
    Soderhall, Cilla
    Pershagen, Goran
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Rule-Based Models of the Interplay between Genetic and Environmental Factors in Childhood Allergy2013Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, nr 11, s. e80080-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Both genetic and environmental factors are important for the development of allergic diseases. However, a detailed understanding of how such factors act together is lacking. To elucidate the interplay between genetic and environmental factors in allergic diseases, we used a novel bioinformatics approach that combines feature selection and machine learning. In two materials, PARSIFAL (a European cross-sectional study of 3113 children) and BAMSE (a Swedish birth-cohort including 2033 children), genetic variants as well as environmental and lifestyle factors were evaluated for their contribution to allergic phenotypes. Monte Carlo feature selection and rule based models were used to identify and rank rules describing how combinations of genetic and environmental factors affect the risk of allergic diseases. Novel interactions between genes were suggested and replicated, such as between ORMDL3 and RORA, where certain genotype combinations gave odds ratios for current asthma of 2.1 (95% CI 1.2-3.6) and 3.2 (95% CI 2.0-5.0) in the BAMSE and PARSIFAL children, respectively. Several combinations of environmental factors appeared to be important for the development of allergic disease in children. For example, use of baby formula and antibiotics early in life was associated with an odds ratio of 7.4 (95% CI 4.5-12.0) of developing asthma. Furthermore, genetic variants together with environmental factors seemed to play a role for allergic diseases, such as the use of antibiotics early in life and COL29A1 variants for asthma, and farm living and NPSR1 variants for allergic eczema. Overall, combinations of environmental and life style factors appeared more frequently in the models than combinations solely involving genes. In conclusion, a new bioinformatics approach is described for analyzing complex data, including extensive genetic and environmental information. Interactions identified with this approach could provide useful hints for further in-depth studies of etiological mechanisms and may also strengthen the basis for risk assessment and prevention.

    Fulltekst (pdf)
    fulltext
  • 14.
    Bysani, Madhu Sudhan Reddy
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Bornelöv, Susanne
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Zatloukal, Kurt
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    ChIP-seq in steatohepatitis and normal liver tissue identifies candidate disease mechanisms related to progression to cancer2013Inngår i: BMC Medical Genomics, ISSN 1755-8794, E-ISSN 1755-8794, Vol. 6, s. 50-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Steatohepatitis occurs in alcoholic liver disease and may progress to liver cirrhosis and hepatocellular carcinoma. Its molecular pathogenesis is to a large degree unknown. Histone modifications play a key role in transcriptional regulations as marks for silencing and activation of gene expression and as marks for functional elements. Many transcription factors (TFs) are crucial for the control of the genes involved in metabolism, and abnormality in their function may lead to disease. Methods: We performed ChIP-seq of the histone modifications H3K4me1, H3K4me3 and H3K27ac and a candidate transcription factor (USF1) in liver tissue from patients with steatohepatitis and normal livers and correlated results to mRNA-expression and genotypes. Results: We found several regions that are differentially enriched for histone modifications between disease and normal tissue, and qRT-PCR results indicated that the expression of the tested genes strongly correlated with differential enrichment of histone modifications but is independent of USF1 enrichment. By gene ontology analysis of differentially modified genes we found many disease associated genes, some of which had previously been implicated in the etiology of steatohepatitis. Importantly, the genes associated to the strongest histone peaks in the patient were over-represented in cancer specific pathways suggesting that the tissue was on a path to develop to cancer, a common complication to the disease. We also found several novel SNPs and GWAS catalogue SNPs that are candidates to be functional and therefore needs further study. Conclusion: In summary we find that analysis of chromatin features in tissue samples provides insight into disease mechanisms.

  • 15.
    Bysani, Madhusudhan Reddy
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Bornelöv, Susanne
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Zatloukal, Kurt
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    ChIP-seq in steatohepatitis and normal liver tissue identifies candidate disease mechanisms related to progression to cancerManuskript (preprint) (Annet vitenskapelig)
  • 16.
    Carlevaro-Fita, Joana
    et al.
    Univ Hosp, Dept Med Oncol, Inselspital, CH-3010 Bern, Switzerland;Univ Hosp, Dept Med Oncol, Inselspital, CH-3010 Bern, Switzerland;Univ Bern, CH-3010 Bern, Switzerland;Univ Bern, CH-3010 Bern, Switzerland;Univ Bern, Dept BioMed Res, CH-3008 Bern, Switzerland;Univ Bern, Dept BioMed Res, CH-3008 Bern, Switzerland;Univ Bern, Grad Sch Cellular & BioMed Sci, CH-3012 Bern, Switzerland;Univ Bern, Grad Sch Cellular & BioMed Sci, CH-3012 Bern, Switzerland.
    Lanzos, Andres
    Univ Hosp, Dept Med Oncol, Inselspital, CH-3010 Bern, Switzerland;Univ Hosp, Dept Med Oncol, Inselspital, CH-3010 Bern, Switzerland;Univ Bern, CH-3010 Bern, Switzerland;Univ Bern, CH-3010 Bern, Switzerland;Univ Bern, Dept BioMed Res, CH-3008 Bern, Switzerland;Univ Bern, Dept BioMed Res, CH-3008 Bern, Switzerland;Univ Bern, Grad Sch Cellular & BioMed Sci, CH-3012 Bern, Switzerland;Univ Bern, Grad Sch Cellular & BioMed Sci, CH-3012 Bern, Switzerland.
    Feuerbach, Lars
    Deutsch Krebsforschungszentrum, Appl Bioinformat, DE-69120 Heidelberg, Germany;Deutsch Krebsforschungszentrum, Appl Bioinformat, DE-69120 Heidelberg, Germany.
    Hong, Chen
    Deutsch Krebsforschungszentrum, Appl Bioinformat, DE-69120 Heidelberg, Germany;Deutsch Krebsforschungszentrum, Appl Bioinformat, DE-69120 Heidelberg, Germany.
    Mas-Ponte, David
    Barcelona Inst Sci & Technol, Ctr Gen Regulat CRG, Dr Aiguader 88, E-08003 Barcelona, Spain;Univ Pompeu Fabra UPF, Barcelona, Spain;Inst Hosp Mar Invest Med IMIM, Dr Aiguader 88, E-08003 Barcelona, Spain.
    Pedersen, Jakob Skou
    Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark;Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark.
    Johnson, Rory
    Univ Hosp, Dept Med Oncol, Inselspital, CH-3010 Bern, Switzerland;Univ Bern, CH-3010 Bern, Switzerland;Univ Bern, Dept BioMed Res, CH-3008 Bern, Switzerland;Univ Bern, Grad Sch Cellular & BioMed Sci, CH-3012 Bern, Switzerland.
    Abascal, Federico
    Wellcome Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England.
    Amin, Samirkumar B.
    Univ Texas MD Anderson Canc Ctr, Dept Gen Med, Houston, TX 77030 USA;Jackson Lab Gen Med, Farmington, CT 06032 USA;Baylor Coll Med, Quantitat & Computat Biosci Grad Program, Houston, TX 77030 USA.
    Bader, Gary D.
    Univ Toronto, Dept Mol Genet, Toronto, ON M5S 1A8, Canada.
    Barenboim, Jonathan
    Ontario Inst Canc Res, Computat Biol Program, Toronto, ON M5G 0A3, Canada.
    Beroukhim, Rameen
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Dana Farber Canc Inst, Dept Med Oncol, Boston, MA 02115 USA;Harvard Med Sch, Boston, MA 02115 USA.
    Bertl, Johanna
    Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark;Aarhus Univ, Dept Math, DK-8000 Aarhus, Denmark.
    Boroevich, Keith A.
    RIKEN Ctr Integrat Med Sci, Lab Med Sci Math, Yokohama, Kanagawa 2300045, Japan;RIKEN Ctr Integrat Med Sci, Yokohama, Kanagawa 2300045, Japan.
    Brunak, Soren
    Tech Univ Denmark, DK-2800 Lyngby, Denmark;Univ Copenhagen, DK-2200 Copenhagen, Denmark.
    Campbell, Peter J.
    Wellcome Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England;Univ Cambridge, Dept Haematol, Cambridge CB2 2XY, England.
    Chakravarty, Dimple
    Univ Texas MD Anderson Canc Ctr, Dept Genitourinary Med Oncol Res, Div Canc Med, Houston, TX 77030 USA.
    Chan, Calvin Wing Yiu
    German Canc Res Ctr, Div Theoret Bioinformat, DE-69120 Heidelberg, Germany;Heidelberg Univ, Fac Biosci, DE-69120 Heidelberg, Germany.
    Chen, Ken
    Univ Texas MD Anderson Canc Ctr, Houston, TX 77030 USA.
    Choi, Jung Kyoon
    Korea Adv Inst Sci & Technol, Daejeon 34141, South Korea.
    Deu-Pons, Jordi
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain.
    Dhingra, Priyanka
    Weill Cornell Med, Dept Physiol & BioPhys, New York, NY 10065 USA;Weill Cornell Med, Inst Computat BioMed, New York, NY 10021 USA.
    Diamanti, Klev
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Fink, J. Lynn
    Barcelona SuperComp Ctr, E-08034 Barcelona, Spain;Univ Queensland, Inst Mol BioSci, Queensland Ctr Med Gen, St Lucia, Qld 4072, Australia.
    Fonseca, Nuno A.
    Univ Porto, Res Ctr Biodivers & Genet Resources, CIBIO InBIO, P-4485601 Vairao, Portugal;European Bioinformat Inst EMBLEBI, European Mol Biol Lab, Wellcome Genome Campus, Cambridge CB10 1SD, England.
    Frigola, Joan
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain.
    Gambacorti-Passerini, Carlo
    Univ Milano Bicocca, I-20052 Monza, Italy.
    Garsed, Dale W.
    Peter MacCallum Canc Ctr, Melbourne, Vic 3000, Australia;Univ Melbourne, Sir Peter MacCallum Dept Oncol, Melbourne, Vic 3052, Australia.
    Gerstein, Mark
    Princeton Univ, Dept Comp Sci, Princeton, NJ 08540 USA;Yale Univ, Dept Comp Sci, New Haven, CT 06520 USA;Yale Univ, Dept Mol BioPhys & Biochem, New Haven, CT 06520 USA;Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Getz, Gad
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Harvard Med Sch, Boston, MA 02115 USA;Massachusetts Gen Hosp, Ctr Canc Res, Boston, MA 02129 USA;Massachusetts Gen Hosp, Dept Pathol, Boston, MA 02115 USA.
    Gonzalez-Perez, Abel
    Inst Hosp Mar Invest Med IMIM, Dr Aiguader 88, E-08003 Barcelona, Spain;Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain.
    Guo, Qianyun
    Aarhus Univ, Bioinformat Res Ctr BiRC, DK-8000 Aarhus, Denmark.
    Gut, Ivo G.
    Univ Pompeu Fabra UPF, Barcelona, Spain;Barcelona Inst Sci & Technol BIST, Ctr Gen Regulat CRG, CNAG CRG, E-08028 Barcelona, Spain.
    Haan, David
    Univ Calif, BioMol Engn Dept, Santa Cruz, CA 95064 USA.
    Hamilton, Mark P.
    Stanford Univ, Dept Internal Med, Stanford, CA 94305 USA.
    Haradhvala, Nicholas J.
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Massachusetts Gen Hosp, Boston, MA 02114 USA.
    Harmanci, Arif O.
    Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA;Univ Texas Hlth Sci Ctr, Ctr Precis Hlth, Sch BioMed Informat, Houston, TX 77030 USA.
    Helmy, Mohamed
    Univ Toronto, Donnelly Ctr, Toronto, ON M5S 3E1, Canada.
    Herrmann, Carl
    German Canc Res Ctr, Div Theoret Bioinformat, DE-69120 Heidelberg, Germany;Univ Clin, Hlth Data Sci Unit, DE-69120 Heidelberg, Germany;Heidelberg Univ, Inst Pharm & Mol Biotechnol & BioQuant, DE-69120 Heidelberg, Germany.
    Hess, Julian M.
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Massachusetts Gen Hosp Ctr Canc Res, Charlestown, MA 02129 USA.
    Hobolth, Asger
    Aarhus Univ, Dept Math, DK-8000 Aarhus, Denmark;Aarhus Univ, Bioinformat Res Ctr BiRC, DK-8000 Aarhus, Denmark.
    Hodzic, Ermin
    Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada.
    Hornshoj, Henrik
    Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark.
    Isaev, Keren
    Ontario Inst Canc Res, Computat Biol Program, Toronto, ON M5G 0A3, Canada;Univ Toronto, Dept Med BioPhys, Toronto, ON M5S 1A8, Canada.
    Izarzugaza, Jose M. G.
    Tech Univ Denmark, DK-2800 Lyngby, Denmark.
    Johnson, Todd A.
    RIKEN Ctr Integrat Med Sci, Lab Med Sci Math, Yokohama, Kanagawa 2300045, Japan.
    Juul, Malene
    Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark.
    Juul, Randi Istrup
    Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark.
    Kahles, Andre
    Mem Sloan Kettering Canc Ctr, Computat Biol Ctr, New York, NY 10065 USA;Swiss Fed Inst Technol, Dept Biol, CH-8093 Zurich, Switzerland;Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland;SIB Swiss Inst Bioinformat, CH-1015 Lausanne, Switzerland;Univ Hosp Zurich, CH-8091 Zurich, Switzerland.
    Kahraman, Abdullah
    Swiss Inst Bioinformat, Clin Bioinformat, CH-1202 Geneva, Switzerland;Univ Hosp Zurich, Inst Pathol & Mol Pathol, CH-8091 Zurich, Switzerland;Univ Zurich, Inst Mol Life Sci, CH-8057 Zurich, Switzerland.
    Kellis, Manolis
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;MIT, MIT Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA.
    Khurana, Ekta
    Weill Cornell Med, Dept Physiol & BioPhys, New York, NY 10065 USA;Weill Cornell Med, Inst Computat BioMed, New York, NY 10021 USA;Weill Cornell Med, Englander Inst Precis Med, New York, NY 10065 USA;Weill Cornell Med, Meyer Canc Ctr, New York, NY 10065 USA.
    Kim, Jaegil
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA.
    Kim, Jong K.
    Natl Canc Ctr Korea, Res Core Ctr, Goyangsi 410769, South Korea.
    Kim, Youngwook
    Sungkyunkwan Univ Sch Med, Dept Hlth Sci & Technol, Seoul 06351, South Korea;Samsung Genome Inst, Seoul 06351, South Korea.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland.
    Korbel, Jan O.
    European Bioinformat Inst EMBLEBI, European Mol Biol Lab, Wellcome Genome Campus, Cambridge CB10 1SD, England;European Mol Biol Lab EMBL, Genome Biol Unit, DE-69117 Heidelberg, Germany.
    Kumar, Sushant
    European Bioinformat Inst EMBLEBI, European Mol Biol Lab, Wellcome Genome Campus, Cambridge CB10 1SD, England;Univ Milano Bicocca, I-20052 Monza, Italy.
    Larsson, Erik
    Mem Sloan Kettering Canc Ctr, Computat Biol Ctr, New York, NY 10065 USA.
    Lawrence, Michael S.
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;RIKEN Ctr Integrat Med Sci, Lab Med Sci Math, Yokohama, Kanagawa 2300045, Japan;Massachusetts Gen Hosp Ctr Canc Res, Charlestown, MA 02129 USA.
    Lee, Donghoon
    Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Lehmann, Kjong-Van
    Mem Sloan Kettering Canc Ctr, Computat Biol Ctr, New York, NY 10065 USA;Swiss Fed Inst Technol, Dept Comp Sci, CH-8092 Zurich, Switzerland;SIB Swiss Inst Bioinformat, CH-1015 Lausanne, Switzerland;Univ Hosp Zurich, CH-8091 Zurich, Switzerland;Swiss Fed Inst Technol, Dept Biol, Wolfgang-Pauli-Str 27, CH-8093 Zurich, Switzerland.
    Li, Shantao
    Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Li, Xiaotong
    Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Lin, Ziao
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Harvard Univ, Cambridge, MA 02138 USA.
    Liu, Eric Minwei
    Weill Cornell Med, Dept Physiol & BioPhys, New York, NY 10065 USA;Weill Cornell Med, Inst Computat BioMed, New York, NY 10021 USA;Mem Sloan Kettering Canc Ctr, New York, NY 10065 USA.
    Lochovsky, Lucas
    Jackson Lab Gen Med, Farmington, CT 06032 USA;Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA;Dept Mol BioPhys & Biochem, New Haven, CT 06520 USA;Yale Univ, New Haven, CT 06520 USA.
    Lou, Shaoke
    Yale Univ, Dept Mol BioPhys & Biochem, New Haven, CT 06520 USA;Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Madsen, Tobias
    Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark.
    Marchal, Kathleen
    Univ Ghent, Dept Informat Technol, B-9000 Ghent, Belgium;Univ Ghent, Dept Plant Biotechnol & Bioinformat, B-9000 Ghent, Belgium.
    Martincorena, Inigo
    Wellcome Sanger Inst, Wellcome Genome Campus, Cambridge CB10 1SA, England.
    Martinez-Fundichely, Alexander
    Weill Cornell Med, Dept Physiol & BioPhys, New York, NY 10065 USA;Weill Cornell Med, Inst Computat BioMed, New York, NY 10021 USA;Weill Cornell Med, Englander Inst Precis Med, New York, NY 10065 USA.
    Maruvka, Yosef E.
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Massachusetts Gen Hosp, Boston, MA 02114 USA;Massachusetts Gen Hosp Ctr Canc Res, Charlestown, MA 02129 USA.
    McGillivray, Patrick D.
    Yale Univ, Dept Mol BioPhys & Biochem, New Haven, CT 06520 USA.
    Meyerson, William
    Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA;Yale Univ, Yale Sch Med, New Haven, CT 06520 USA.
    Muinos, Ferran
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain.
    Mularoni, Loris
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain.
    Nakagawa, Hidewaki
    RIKEN Ctr Integrat Med Sci, Yokohama, Kanagawa 2300045, Japan.
    Nielsen, Morten Muhlig
    Aarhus Univ Hosp, Dept Mol Med, Palle Juul-Jensens Blvd 99, DK-8200 Aarhus, Denmark.
    Paczkowska, Marta
    Ontario Inst Canc Res, Computat Biol Program, Toronto, ON M5G 0A3, Canada.
    Park, Keunchil
    Sungkyunkwan Univ Sch Med, Samsung Med Ctr, Div Hematol Oncol, Seoul 06351, South Korea;Sungkyunkwan Univ Sch Med, Samsung Adv Inst Hlth Sci & Technol, Seoul 06351, South Korea.
    Park, Kiejung
    Sangmyung Univ, Cheonan Ind Acad Collaborat Fdn, Cheonan 31066, South Korea.
    Pich, Oriol
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain.
    Pons, Tirso
    Spanish Natl Canc Res Centre, E-28029 Madrid, Spain.
    Pulido-Tamayo, Sergio
    Univ Ghent, Dept Informat Technol, B-9000 Ghent, Belgium;Univ Ghent, Dept Plant Biotechnol & Bioinformat, B-9000 Ghent, Belgium.
    Raphael, Benjamin J.
    Princeton Univ, Dept Comp Sci, Princeton, NJ 08540 USA.
    Reimand, Juri
    Ontario Inst Canc Res, Computat Biol Program, Toronto, ON M5G 0A3, Canada;Univ Toronto, Dept Med BioPhys, Toronto, ON M5S 1A8, Canada.
    Reyes-Salazar, Iker
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain.
    Reyna, Matthew A.
    Princeton Univ, Dept Comp Sci, Princeton, NJ 08540 USA.
    Rheinbay, Esther
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Harvard Med Sch, Boston, MA 02115 USA;Massachusetts Gen Hosp, Boston, MA 02114 USA.
    Rubin, Mark A.
    Univ Bern, Dept BioMed Res, CH-3008 Bern, Switzerland;Weill Cornell Med, Meyer Canc Ctr, New York, NY 10065 USA;Univ Bern, Univ Hosp Bern, Bern Ctr Precis Med, CH-3008 Bern, Switzerland;Weill Cornell Med, Englander Inst Precis Med, New York, NY 10021 USA;NewYork Presbyterian Hosp, New York, NY 10021 USA;Weill Cornell Med Coll, Pathol & Lab, New York, NY 10021 USA.
    Rubio-Perez, Carlota
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain;Vall Hebron Inst Oncol VHIO, E-08035 Barcelona, Spain.
    Sabarinathan, Radhakrishnan
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain;Tata Inst Fundamental Res, Natl Ctr Biol Sci, Bangalore 560065, India.
    Sahinalp, S. Cenk
    Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada;Indiana Univ, Bloomington, IN 47405 USA;Vancouver Prostate Ctr, Vancouver, BC V6H 3Z6, Canada.
    Saksena, Gordon
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA.
    Salichos, Leonidas
    Yale Univ, Dept Mol BioPhys & Biochem, New Haven, CT 06520 USA;Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Sander, Chris
    Mem Sloan Kettering Canc Ctr, Computat Biol Ctr, New York, NY 10065 USA;Harvard Med Sch, Dana Farber Canc Inst, cBio Ctr, Boston, MA 02115 USA;Dana Farber Canc Inst, Boston, MA 02215 USA;Harvard Med Sch, Dept Cell Biol, Boston, MA 02115 USA.
    Schumacher, Steven E.
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Dana Farber Canc Inst, Dept Canc Biol, Boston, MA 02215 USA.
    Shackleton, Mark
    Univ Melbourne, Sir Peter MacCallum Dept Oncol, Melbourne, Vic 3052, Australia;Univ Melbourne, Melbourne, Vic 3000, Australia;Peter MacCallum Canc Inst, Melbourne, Vic 3000, Australia.
    Shapira, Ofer
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Harvard Med Sch, Dana Farber Canc Inst, cBio Ctr, Boston, MA 02115 USA.
    Shen, Ciyue
    Harvard Med Sch, Dana Farber Canc Inst, cBio Ctr, Boston, MA 02115 USA;Harvard Med Sch, Dept Cell Biol, Boston, MA 02115 USA.
    Shrestha, Raunak
    Vancouver Prostate Ctr, Vancouver, BC V6H 3Z6, Canada.
    Shuai, Shimin
    Univ Toronto, Dept Mol Genet, Toronto, ON M5S 1A8, Canada;Ontario Inst Canc Res, Computat Biol Program, Toronto, ON M5G 0A3, Canada.
    Sidiropoulos, Nikos
    Univ Copenhagen, Biotech Res & Innovat Ctr BRIC, DK-2200 Copenhagen, Denmark;Univ Copenhagen, Finsen Lab, DK-2200 Copenhagen, Denmark.
    Sieverling, Lina
    Heidelberg Univ, Fac Biosci, DE-69120 Heidelberg, Germany;Univ Porto, Res Ctr Biodivers & Genet Resources, CIBIO InBIO, P-4485601 Vairao, Portugal.
    Sinnott-Armstrong, Nasa
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA;Stanford Univ Sch Med, Dept Genet, Stanford, CA 94305 USA.
    Stein, Lincoln D.
    Univ Toronto, Dept Mol Genet, Toronto, ON M5S 1A8, Canada;Ontario Inst Canc Res, Computat Biol Program, Toronto, ON M5G 0A3, Canada.
    Stuart, Joshua M.
    Univ Calif, BioMol Engn Dept, Santa Cruz, CA 95064 USA.
    Tamborero, David
    Barcelona Inst Sci & Technol, Inst Res BioMed IRB Barcelona, E-8003 Barcelona, Spain;Univ Pompeu Fabra, Res Program BioMed Informat, E-08002 Barcelona, Spain.
    Tiao, Grace
    Broad Inst MIT & Harvard, Cambridge, MA 02142 USA.
    Tsunoda, Tatsuhiko
    RIKEN Ctr Integrat Med Sci, Lab Med Sci Math, Yokohama, Kanagawa 2300045, Japan;Japan Sci & Technol Agcy, CREST, Tokyo, Tokyo 1130033, Japan;Tokyo Med & Dent Univ, Med Res Inst, Dept Med Sci Math, Bunkyo Ku, Tokyo 1138510, Japan;Univ Tokyo, Grad Sch Sci, Dept Biol Sci, Lab Med Sci Math,Bunkyo Ku, Tokyo 1130033, Japan.
    Umer, Husen Muhammad
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Karolinska Inst, Dept Oncol & Pathol, Sci Life Lab, S-17121 Stockholm, Sweden.
    Uuskula-Reimand, Liis
    Tallinn Univ Technol, Dept Gene Technol, EE-12616 Tallinn, Estonia;Hosp Sick Children, SickKids Res Inst, Genet & Genome Biol Program, Toronto, ON M5G 1X8, Canada.
    Valencia, Alfonso
    Barcelona SuperComp Ctr, E-08034 Barcelona, Spain;Inst Catalana Recerca & Estudis Avancats ICREA, E-08010 Barcelona, Spain.
    Vazquez, Miguel
    Barcelona SuperComp Ctr, E-08034 Barcelona, Spain;Norwegian Univ Sci & Technol, Fac Med & Hlth Sci, Dept Clin & Mol Med, N-7030 Trondheim, Norway.
    Verbeke, Lieven P. C.
    Univ Ghent, Dept Plant Biotechnol & Bioinformat, B-9000 Ghent, Belgium;Univ Ghent, Dept Informat Technol, Interuniv Microelectron Centrum IMEC, B-9000 Ghent, Belgium.
    Wadelius, Claes
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Wadi, Lina
    Ontario Inst Canc Res, Computat Biol Program, Toronto, ON M5G 0A3, Canada.
    Wang, Jiayin
    Xi'an Jiaotong Univ, Sch Comp Sci & Technol, Xian 710048, Peoples R China;Xi'an Jiaotong Univ, Sch Elect & Informat Engn, Xian 710048, Peoples R China;McDonnell Genome Inst Washington Univ, St. Louis, MO 63108 USA.
    Warrell, Jonathan
    Yale Univ, Dept Mol BioPhys & Biochem, New Haven, CT 06520 USA;Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Waszak, Sebastian M.
    European Mol Biol Lab EMBL, Genome Biol Unit, DE-69117 Heidelberg, Germany.
    Weischenfeldt, Joachim
    European Mol Biol Lab EMBL, Genome Biol Unit, DE-69117 Heidelberg, Germany;Univ Copenhagen, Biotech Res & Innovat Ctr BRIC, DK-2200 Copenhagen, Denmark;Univ Copenhagen, Finsen Lab, DK-2200 Copenhagen, Denmark;Charite Univ Med Berlin, Dept Urol, DE-10117 Berlin, Germany.
    Wheeler, David A.
    Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA;Baylor Coll Med, Human Genome Sequencing Ctr, Houston, TX 77030 USA.
    Wu, Guanming
    Oregon Hlth & Sci Univ, Portland, OR 97239 USA.
    Yu, Jun
    Chinese Univ Hong Kong, Dept Med & Therapeut, Hong Kong, Peoples R China;Second Mil Med Univ, Shanghai 200433, Peoples R China.
    Zhang, Jing
    Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA.
    Zhang, Xuanping
    Xi'an Jiaotong Univ, Sch Comp Sci & Technol, Xian 710048, Peoples R China;Univ Texas Hlth Sci Ctr Houston, Houston, TX 77030 USA.
    Zhang, Yan
    Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA;Ohio State Univ, Coll Med, Dept BioMed Informat, Columbus, OH 43210 USA;Ohio State Univ Comprehens Canc Ctr OSUCCC James, Columbus, OH 43210 USA.
    Zhao, Zhongming
    Univ Texas Hlth Sci Ctr Houston, Sch BioMed Informat, Houston, TX 77030 USA.
    Zou, Lihua
    Northwestern Univ, Feinberg Sch Med, Dept Biochem & Mol Genet, Chicago, IL 60637 USA.
    von Mering, Christian
    Univ Zurich, Inst Mol Life Sci, CH-8057 Zurich, Switzerland;Univ Zurich, Inst Mol Life Sci, CH-8057 Zurich, Switzerland;Univ Zurich, Swiss Inst Bioinformat, CH-8057 Zurich, Switzerland.
    Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis2020Inngår i: COMMUNICATIONS BIOLOGY, ISSN 2399-3642, Vol. 3, nr 1, artikkel-id 56Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Joana Carlevaro-Fita, Andres Lanzos et al. present the Cancer LncRNA Census (CLC), a manually curated dataset of 122 long noncoding RNAs (lncRNAs) with experimentally-validated functions in cancer based on data from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. CLC lncRNAs have unique gene features, and a number display evidence for cancer-driving functions that are conserved from humans to mice. Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis.

    Fulltekst (pdf)
    FULLTEXT01
  • 17.
    Cavalli, Marco
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Baltzer, Nicholas
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Pan, Gang
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Walls, Jose Ramon Barcenas
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Garbulowska, Karolina Smolinska
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Kumar, Chanchal
    AstraZeneca, Gothenburg, Sweden.
    Skrtic, Stanko
    AstraZeneca, Gothenburg, Sweden.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, Warsaw, Poland.
    Wadelius, Claes
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Studies of liver tissue identify functional gene regulatory elements associated to gene expression, type 2 diabetes, and other metabolic diseases2019Inngår i: HUMAN GENOMICS, ISSN 1473-9542, Vol. 13, artikkel-id 20Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background:

    Genome-wide association studies (GWAS) of diseases and traits have found associations to gene regions but not the functional SNP or the gene mediating the effect. Difference in gene regulatory signals can be detected using chromatin immunoprecipitation and next-gen sequencing (ChIP-seq) of transcription factors or histone modifications by aligning reads to known polymorphisms in individual genomes. The aim was to identify such regulatory elements in the human liver to understand the genetics behind type 2 diabetes and metabolic diseases.

    Methods:

    The genome of liver tissue was sequenced using 10X Genomics technology to call polymorphic positions. Using ChIP-seq for two histone modifications, H3K4me3 and H3K27ac, and the transcription factor CTCF, and our established bioinformatics pipeline, we detected sites with significant difference in signal between the alleles.

    Results:

    We detected 2329 allele-specific SNPs (AS-SNPs) including 25 associated to GWAS SNPs linked to liver biology, e.g., 4 AS-SNPs at two type 2 diabetes loci. Two hundred ninety-two AS-SNPs were associated to liver gene expression in GTEx, and 134 AS-SNPs were located on 166 candidate functional motifs and most of them in EGR1-binding sites.

    Conclusions:

    This study provides a valuable collection of candidate liver regulatory elements for further experimental validation.

    Fulltekst (pdf)
    FULLTEXT01
  • 18.
    Cavalli, Marco
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Baltzer, Nicholas
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Umer, Husen Muhammad
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Grau, Jan
    Martin Luther Univ Halle Wittenberg, Inst Comp Sci, Halle, Germany.
    Lemnian, Ioana
    Martin Luther Univ Halle Wittenberg, Inst Comp Sci, Halle, Germany.
    Pan, Gang
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wallerman, Ola
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Spalinskas, Rapolas
    KTH Royal Inst Technol, Div Gene Technol, Sci Life Lab, Stockholm, Sweden.
    Sahlen, Pelin
    KTH Royal Inst Technol, Div Gene Technol, Sci Life Lab, Stockholm, Sweden.
    Grosse, Ivo
    Martin Luther Univ Halle Wittenberg, Inst Comp Sci, Halle, Germany;German Ctr Integrat Biodivers Res iDiv, Leipzig, Germany.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, Warsaw, Poland.
    Wadelius, Claes
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases2019Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, artikkel-id 2695Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes.

    Fulltekst (pdf)
    FULLTEXT01
  • 19.
    Cavalli, Marco
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Diamanti, Klev
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Pan, Gang
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Rapolas, Spalinskas
    Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology.
    Kumar, Chanchal
    Translational Science & Experimental Medicine, Early Cardiovascular, Renal and Metabolism, 12 BioPharmaceuticals R&D, AstraZeneca; Karolinska Institutet/AstraZeneca Integrated CardioMetabolic Center (KI/AZ ICMC), Department of Medicine.
    Deshmukh, Atul Shahaji
    Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Clinical Proteomics Group.
    Mann, Matthias
    Novo Nordisk Foundation Center for Protein Research, Proteomics Program, Clinical Proteomics Group.
    Sahlén, Pelin
    Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Wadelius, Claes
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Single Nuclei Transcriptome Analysis of Human Liver with Integration of Proteomics and Capture Hi-C Bulk Tissue DataInngår i: Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The liver is the largest solid organ and a primary metabolic hub. In recent years, intact cell nuclei were used to perform single-nuclei RNA-seq (snRNA-seq) for tissues difficult to dissociate and for flash-frozen archived tissue samples to discover unknown and rare cell sub-populations. In this study, we performed snRNA-seq of a liver sample to identify sub-populations of cells based on nuclear transcriptomics. In 4,282 single nuclei we detected on average 1,377 active genes and we identified seven major cell types. We integrated data from 94,286 distal interactions (p<0.05) for 7,682 promoters from a targeted chromosome conformation capture technique (HiCap) and mass spectrometry (MS) proteomics for the same liver sample. We observed a reasonable correlation between proteomics and in silico bulk snRNA-seq (r=0.47) using tissue-independent gene-specific protein abundancy estimation factors. We specifically looked at genes of medical importance. The DPYD gene is involved in the pharmacogenetics of fluoropyrimidines toxicity and some of its variants are analyzed for clinical purposes. We identified a new putative polymorphic regulatory element, which may contribute to variation in toxicity. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and we investigated all known risk genes. We found a complex regulatory network for the SLC2A2 gene with 16 candidate enhancers. Three of them harbor somatic motif breaking and other mutations in HCC in the Pan Cancer Analysis of Whole Genomes dataset and are candidates to contribute to malignancy. Our results highlight the potential of a multi-omics approach in the study of human diseases.

  • 20.
    Dabrowski, Michal J.
    et al.
    Polish Acad Sci, Inst Comp Sci, Warsaw, Poland..
    Draminski, Michal
    Polish Acad Sci, Inst Comp Sci, Warsaw, Poland..
    Diamanti, Klev
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Stepniak, Karolina
    Nencki Inst Expt Biol, Warsaw, Poland..
    Mozolewska, Magdalena A.
    Polish Acad Sci, Inst Comp Sci, Warsaw, Poland..
    Teisseyre, Pawel
    Polish Acad Sci, Inst Comp Sci, Warsaw, Poland..
    Koronacki, Jacek
    Polish Acad Sci, Inst Comp Sci, Warsaw, Poland..
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, Warsaw, Poland.
    Kaminska, Bozena
    Nencki Inst Expt Biol, Warsaw, Poland..
    Wojtas, Bartosz
    Nencki Inst Expt Biol, Warsaw, Poland..
    Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival2018Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, artikkel-id 4390Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. The input features were ranked according to their importance in predicting patients' longer (400+ days) or shorter (<= 400 days) survival without prior classification of the patients. Interestingly, out of the 65 most important features found, 63 are methylation sites, and only two mRNAs. Moreover, 61 out of the 63 methylation sites are among those detected by the 450 k array technology, while being absent in the HumanMethylation27. The most important methylation feature (cg15072976) overlaps with the RE1 Silencing Transcription Factor (REST) binding site, and was confirmed to intersect with the REST binding motif in human U87 glioma cells. Six additional methylation sites from the top 63 overlap with REST sites. We found that the methylation status of the cg15072976 site affects transcription factor binding in U87 cells in gel shift assay. The cg15072976 methylation status discriminates <= 400 and 400+ patients in an independent dataset from TCGA and shows positive association with survival time as evidenced by Kaplan-Meier plots.

    Fulltekst (pdf)
    fulltext
  • 21.
    de Ståhl, Teresita Díaz
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Sandgren, Johanna
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper.
    Piotrowski, Arkadiusz
    Nord, Helena
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Menzel, Uwe
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Bogdan, Adam
    Thuresson, Ann-Charlotte
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Poplawski, Andrzej
    von Tell, Desiree
    Hansson, Caisa M.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Elshafie, Amir I.
    Elghazali, Gehad
    Imreh, Stephan
    Nordenskjöld, Magnus
    Upadhyaya, Meena
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Bruder, Carl E. G.
    Dumanski, Jan P.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Profiling of copy number variations (CNVs) in healthy individuals from three ethnic groups using a human genome 32 K BAC-clone-based array2008Inngår i: Human Mutation, ISSN 1059-7794, E-ISSN 1098-1004, Vol. 29, nr 3, s. 398-408Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To further explore the extent of structural large-scale variation in the human genome, we assessed copy number variations (CNVs) in a series of 71 healthy subjects from three ethnic groups. CNVs were analyzed using comparative genomic hybridization (CGH) to a BAC array covering the human genome, using DNA extracted from peripheral blood, thus avoiding any culture-induced rearrangements. By applying a newly developed computational algorithm based on Hidden Markov modeling, we identified 1,078 autosomal CNVs, including at least two neighboring/overlapping BACs, which represent 315 distinct regions. The average size of the sequence polymorphisms was approximately 350 kb and involved in total approximately 117 Mb or approximately 3.5% of the genome. Gains were about four times more common than deletions, and segmental duplications (SDs) were overrepresented, especially in larger deletion variants. This strengthens the notion that SDs often define hotspots of chromosomal rearrangements. Over 60% of the identified autosomal rearrangements match previously reported CNVs, recognized with various platforms. However, results from chromosome X do not agree well with the previously annotated CNVs. Furthermore, data from single BACs deviating in copy number suggest that our above estimate of total variation is conservative. This report contributes to the establishment of the common baseline for CNV, which is an important resource in human genetics.

  • 22.
    Diamanti, Klev
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Cavalli, Marco
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Pan, Gang
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Pereira, Maria J
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk diabetologi och metabolism.
    Kumar, Chanchal
    AstraZeneca, R&D BioPharmaceut, Translat Sci & Expt Med, Early Cardiovasc Renal & Metab, Gothenburg, Sweden;Karolinska Inst, AstraZeneca Integrated CardioMetab Ctr KI AZ ICMC, Dept Med, Huddinge, Sweden.
    Skrtic, Stanko
    AstraZeneca AB, Pharmaceut Technol & Dev, Gothenburg, Sweden;Sahlgrens Univ Hosp, Dept Med, Gothenburg, Sweden.
    Grabherr, Manfred
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi.
    Risérus, Ulf
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för folkhälso- och vårdvetenskap, Klinisk nutrition och metabolism.
    Eriksson, Jan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk diabetologi och metabolism.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, Warsaw, Poland.
    Wadelius, Claes
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes2019Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, artikkel-id 9653Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Type 2 diabetes (T2D) mellitus is a complex metabolic disease commonly caused by insulin resistance in several tissues. We performed a matched two-dimensional metabolic screening in tissue samples from 43 multi-organ donors. The intra-individual analysis was assessed across five key metabolic tissues (serum, visceral adipose tissue, liver, pancreatic islets and skeletal muscle), and the inter-individual across three different groups reflecting T2D progression. We identified 92 metabolites differing significantly between non-diabetes and T2D subjects. In diabetes cases, carnitines were significantly higher in liver, while lysophosphatidylcholines were significantly lower in muscle and serum. We tracked the primary tissue of origin for multiple metabolites whose alterations were reflected in serum. An investigation of three major stages spanning from controls, to pre-diabetes and to overt T2D indicated that a subset of lysophosphatidylcholines was significantly lower in the muscle of pre-diabetes subjects. Moreover, glycodeoxycholic acid was significantly higher in liver of pre-diabetes subjects while additional increase in T2D was insignificant. We confirmed many previously reported findings and substantially expanded on them with altered markers for early and overt T2D. Overall, the analysis of this unique dataset can increase the understanding of the metabolic interplay between organs in the development of T2D.

    Fulltekst (pdf)
    FULLTEXT01
  • 23.
    Diamanti, Klev
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Umer, Husen M.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Kruczyk, Marcin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Dabrowski, Michal J.
    Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland..
    Cavalli, Marco
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland..
    Maps of context-dependent putative regulatory regions and genomic signal interactions2016Inngår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 44, nr 19, s. 9110-9120Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Gene transcription is regulated mainly by transcription factors (TFs). ENCODE and Roadmap Epigenomics provide global binding profiles of TFs, which can be used to identify regulatory regions. To this end we implemented a method to systematically construct cell-type and species-specific maps of regulatory regions and TF-TF interactions. We illustrated the approach by developing maps for five human cell-lines and two other species. We detected similar to 144k putative regulatory regions among the human cell-lines, with the majority of them being similar to 300 bp. We found similar to 20k putative regulatory elements in the ENCODE heterochromatic domains suggesting a large regulatory potential in the regions presumed transcriptionally silent. Among the most significant TF interactions identified in the heterochromatic regions were CTCF and the cohesin complex, which is in agreement with previous reports. Finally, we investigated the enrichment of the obtained putative regulatory regions in the 3D chromatin domains. More than 90% of the regions were discovered in the 3D contacting domains. We found a significant enrichment of GWAS SNPs in the putative regulatory regions. These significant enrichments provide evidence that the regulatory regions play a crucial role in the genomic structural stability. Additionally, we generated maps of putative regulatory regions for prostate and colorectal cancer human cell-lines.

    Fulltekst (pdf)
    fulltext
  • 24.
    Diamanti, Klev
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Visvanathar, Robin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper.
    Pereira, Maria J
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk diabetologi och metabolism.
    Cavalli, Marco
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Pan, Gang
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Kumar, Chanchal
    Translational Science & Experimental Medicine, Early Cardiovascular, Renal and Metabolism, R&D BioPharmaceuticals, AstraZeneca; Karolinska Institute/AstraZeneca Integrated CardioMetabolic Centre (KI/AZ ICMC), Department of Medicine.
    Stanko, Stanko
    Pharmaceutical Technology & Development, AstraZeneca AB; Department of Medicine, Sahlgrenska University Hospital, Gothenburg.
    Ingelsson, Martin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för folkhälso- och vårdvetenskap, Geriatrik.
    Fall, Tove
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Molekylär epidemiologi. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Kardiologi.
    Lind, Lars
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Molekylär medicin. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centrum för klinisk forskning, Gävleborg. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Uppsala kliniska forskningscentrum (UCR). Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Kardiologi. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för folkhälso- och vårdvetenskap, Geriatrik. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för folkhälso- och vårdvetenskap, Klinisk nutrition och metabolism. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Molekylär epidemiologi. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk epidemiologi.
    Risérus, Ulf
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för folkhälso- och vårdvetenskap, Klinisk nutrition och metabolism. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för folkhälso- och vårdvetenskap, Geriatrik. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper.
    Eriksson, Jan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk diabetologi och metabolism.
    Kullberg, Joel
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för folkhälso- och vårdvetenskap, Geriatrik. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Wadelius, Claes
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Ahlström, Håkan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Radiologi.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Integration of whole-body PET/MRI with non-targeted metabolomics provides new insights into insulin sensitivity of various tissuesManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Background: Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific alterations is challenging and requires a multi-omics approach. In this study, we aimed at discovering associations of metabolites from subcutaneous adipose tissue (SAT) and plasma with the volume, the fat fraction (FF) and the insulin sensitivity (Ki) of specific tissues using [18F]FDG PET/MRI.

    Materials and Methods: In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body-mass-index (BMI) we calculated associations between parameters of whole-body FDG PET/MRI during clamp and non-targeted metabolomics profiling for SAT and blood plasma. We also used a rule-based classifier to identify a large collection of prevalent patterns of co-dependent metabolites that characterize non-diabetes (ND) and T2D.

    Results: The plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Ki in visceral adipose tissue (VAT) and SAT, was positively associated with several species of lysophospholipids while the opposite applied to branched-chain amino acids (BCAA) and their intermediates. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. On the contrary, bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Finally, we presented a transparent machine-learning model that predicted ND or T2D in “unseen” data with an accuracy of 78%.

    Conclusions: Novel associations of several metabolites from SAT and plasma with the FF, volume and insulin senstivity of various tissues throughout the body were discovered using PET/MRI and a new integrative multi-omics approach. A promising computational model that predicted ND and T2D with high certainty, suggested novel non-linear interdependencies of metabolites.

  • 25. Dramiński, Michał
    et al.
    Da̧browski, Michał J.
    Diamanti, Klev
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Koronacki, Jacek
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Discovering Networks of Interdependent Features in High-Dimensional Problems2016Inngår i: Big Data Analysis: New Algorithms for a New Society / [ed] Japkowicz, Nathalie; Stefanowski, Jerzy, Cham: Springer, 2016, s. 285-304Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The availability of very large data sets in Life Sciences provided earlier by the technological breakthroughs such as microarrays and more recently by various forms of sequencing has created both challenges in analyzing these data as well as new opportunities. A promising, yet underdeveloped approach to Big Data, not limited to Life Sciences, is the use of feature selection and classification to discover interdependent features. Traditionally, classifiers have been developed for the best quality of supervised classification. In our experience, more often than not, rather than obtaining the best possible supervised classifier, the Life Scientist needs to know which features contribute best to classifying observations (objects, samples) into distinct classes and what the interdependencies between the features that describe the observation. Our underlying hypothesis is that the interdependent features and rule networks do not only reflect some syntactical properties of the data and classifiers but also may convey meaningful clues about true interactions in the modeled biological system. In this chapter we develop further our method of Monte Carlo Feature Selection and Interdependency Discovery (MCFS and MCFS-ID, respectively), which are particularly well suited for high-dimensional problems, i.e., those where each observation is described by very many features, often many more features than the number of observations. Such problems are abundant in Life Science applications. Specifically, we define Inter-Dependency Graphs (termed, somewhat confusingly, ID Graphs) that are directed graphs of interactions between features extracted by aggregation of information from the classification trees constructed by the MCFS algorithm. We then proceed with modeling interactions on a finer level with rule networks. We discuss some of the properties of the ID graphs and make a first attempt at validating our hypothesis on a large gene expression data set for CD4+ T-cells. The MCFS-ID and ROSETTA including the Ciruvis approach offer a new methodology for analyzing Big Data from feature selection, through identification of feature interdependencies, to classification with rules according to decision classes, to construction of rule networks. Our preliminary results confirm that MCFS-ID is applicable to the identification of interacting features that are functionally relevant while rule networks offer a complementary picture with finer resolution of the interdependencies on the level of feature-value pairs.

  • 26.
    Enroth, Stefan
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Genomik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Andersson, Claes R.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Gustafsson, Mats G.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    A strand specific high resolution normalization method for chip-sequencing data employing multiple experimental control measurements2012Inngår i: Algorithms for Molecular Biology, ISSN 1748-7188, E-ISSN 1748-7188, Vol. 7, s. 2-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: High-throughput sequencing is becoming the standard tool for investigating protein-DNA interactions or epigenetic modifications. However, the data generated will always contain noise due to e. g. repetitive regions or non-specific antibody interactions. The noise will appear in the form of a background distribution of reads that must be taken into account in the downstream analysis, for example when detecting enriched regions (peak-calling). Several reported peak-callers can take experimental measurements of background tag distribution into account when analysing a data set. Unfortunately, the background is only used to adjust peak calling and not as a preprocessing step that aims at discerning the signal from the background noise. A normalization procedure that extracts the signal of interest would be of universal use when investigating genomic patterns.

    Results: We formulated such a normalization method based on linear regression and made a proof-of-concept implementation in R and C++. It was tested on simulated as well as on publicly available ChIP-seq data on binding sites for two transcription factors, MAX and FOXA1 and two control samples, Input and IgG. We applied three different peak-callers to (i) raw (un-normalized) data using statistical background models and (ii) raw data with control samples as background and (iii) normalized data without additional control samples as background. The fraction of called regions containing the expected transcription factor binding motif was largest for the normalized data and evaluation with qPCR data for FOXA1 suggested higher sensitivity and specificity using normalized data over raw data with experimental background.

    Conclusions: The proposed method can handle several control samples allowing for correction of multiple sources of bias simultaneously. Our evaluation on both synthetic and experimental data suggests that the method is successful in removing background noise.

    Fulltekst (pdf)
    fulltext
  • 27.
    Enroth, Stefan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Bysani, Madhusudhan Reddy
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Tuch, Brian
    De la Vega, Fransisco
    Heldin, Carl-Henrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwiginstitutet för cancerforskning.
    Moustakas, Aristidis
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwiginstitutet för cancerforskning.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Nucleosome regulatory dynamics in response to TGF-beta treatment in HepG2 cells2014Inngår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 42, nr 11, s. 6921-6934Artikkel i tidsskrift (Fagfellevurdert)
  • 28.
    Enroth, Stefan
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Genomik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Bysani, Madhusudhan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Termén, Stefan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwiginstitutet för cancerforskning. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Tuch, Brian B
    Applied Biosystems, part of Life Technologies, Foster City, CA 94404, USA.
    De La Vega, Francisco M
    Applied Biosystems, part of Life Technologies, Foster City, CA 94404, USA.
    Heldin, Carl-Henrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwiginstitutet för cancerforskning. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Moustakas, Aristidis
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwiginstitutet för cancerforskning. Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi. Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, 01-248 Warszawa, Poland.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Nucleosome regulatory dynamics in response to TGF beta2014Inngår i: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 42, nr 11, s. 6921-6934Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Nucleosomes play important roles in a cell beyond their basal functionality in chromatin compaction. Their placement affects all steps in transcriptional regulation, from transcription factor (TF) binding to messenger ribonucleic acid (mRNA) synthesis. Careful profiling of their locations and dynamics in response to stimuli is important to further our understanding of transcriptional regulation by the state of chromatin. We measured nucleosome occupancy in human hepatic cells before and after treatment with transforming growth factor beta 1 (TGFβ1), using massively parallel sequencing. With a newly developed method, SuMMIt, for precise positioning of nucleosomes we inferred dynamics of the nucleosomal landscape. Distinct nucleosome positioning has previously been described at transcription start site and flanking TF binding sites. We found that the average pattern is present at very few sites and, in case of TF binding, the double peak surrounding the sites is just an artifact of averaging over many loci. We systematically searched for depleted nucleosomes in stimulated cells compared to unstimulated cells and identified 24 318 loci. Depending on genomic annotation, 44-78% of them were over-represented in binding motifs for TFs. Changes in binding affinity were verified for HNF4α by qPCR. Strikingly many of these loci were associated with expression changes, as measured by RNA sequencing.

  • 29.
    Enroth, Stefan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    SICTIN: Rapid footprinting of massively parallel sequencing data2010Inngår i: BioData Mining, ISSN 1756-0381, E-ISSN 1756-0381, Vol. 3, artikkel-id 4Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND: Massively parallel sequencing allows for genome-wide hypothesis-free investigation of for instance transcription factor binding sites or histone modifications. Although nucleotide resolution detailed information can easily be generated, biological insight often requires a more general view of patterns (footprints) over distinct genomic features such as transcription start sites, exons or repetitive regions. The construction of these footprints is however a time consuming task.

    METHODS: The presented software generates a binary representation of the signals enabling fast and scalable lookup. This representation allows for footprint generation in mere minutes on a desktop computer. Several different input formats are accepted, e.g. the SAM format, bed-files and the UCSC wiggle track.

    CONCLUSIONS: Hypothesis-free investigation of genome wide interactions allows for biological data mining at a scale never before seen. Until recently, the main focus of analysis of sequencing data has been targeted on signal patterns around transcriptional start sites which are in manageable numbers. Today, focus is shifting to a wider perspective and numerous genomic features are being studied. To this end, we provide a system allowing for fast querying in the order of hundreds of thousands of features.

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  • 30.
    Enroth, Stefan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Bornelöv, Susanne
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Wadelius, Claes
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Combinations of histone modifications mark exon inclusion levels2012Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, nr 1, artikkel-id e29911Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Splicing is a complex process regulated by sequence at the classical splice sites and other motifs in exons and introns with an enhancing or silencing effect. In addition, specific histone modifications on nucleosomes positioned over the exons have been shown to correlate both positively and negatively with exon expression. Here, we trained a model of "IF … THEN …" rules to predict exon inclusion levels in a transcript from histone modification patterns. Furthermore, we showed that combinations of histone modifications, in particular those residing on nucleosomes preceding or succeeding the exon, are better predictors of exon inclusion levels than single modifications. The resulting model was evaluated with cross validation and had an average accuracy of 72% for 27% of the exons, which demonstrates that epigenetic signals substantially mark alternative splicing.

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  • 31.
    Enroth, Stefan
    et al.
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Rada-Iglesisas, Alvaro
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wanders, Alkwin
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Pahlman, Lars
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Kolorektalkirurgi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Cancer associated epigenetic transitions identified by genome-wide histone methylation binding profiles in human colorectal cancer samples and paired normal mucosa2011Inngår i: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 11, s. 450-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Despite their well-established functional roles, histone modifications have received less attention than DNA methylation in the cancer field. In order to evaluate their importance in colorectal cancer (CRC), we generated the first genome-wide histone modification profiles in paired normal colon mucosa and tumor samples. Methods: Chromatin immunoprecipitation and microarray hybridization (ChIP-chip) was used to identify promoters enriched for histone H3 trimethylated on lysine 4 (H3K4me3) and lysine 27 (H3K27me3) in paired normal colon mucosa and tumor samples from two CRC patients and for the CRC cell line HT29. Results: By comparing histone modification patterns in normal mucosa and tumors, we found that alterations predicted to have major functional consequences were quite rare. Furthermore, when normal or tumor tissue samples were compared to HT29, high similarities were observed for H3K4me3. However, the differences found for H3K27me3, which is important in determining cellular identity, indicates that cell lines do not represent optimal tissue models. Finally, using public expression data, we uncovered previously unknown changes in CRC expression patterns. Genes positive for H3K4me3 in normal and/or tumor samples, which are typically already active in normal mucosa, became hyperactivated in tumors, while genes with H3K27me3 in normal and/or tumor samples and which are expressed at low levels in normal mucosa, became hypersilenced in tumors. Conclusions: Genome wide histone modification profiles can be used to find epigenetic aberrations in genes associated with cancer. This strategy gives further insights into the epigenetic contribution to the oncogenic process and may identify new biomarkers.

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  • 32.
    Grabherr, Manfred
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Molekylär evolution. Uppsala Univ, Natl Bioinformat Infrastruct Sweden, S-75236 Uppsala, Sweden.
    Kaminska, Bozena
    Polish Acad Sci, Nencki Inst Expt Biol, PL-02093 Warsaw, Poland;Guangzhou Med Univ, Affiliated Canc Hosp & Inst, Guangzhou 510095, Guangdong, Peoples R China.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi. Polish Acad Sci, Inst Comp Sci, PL-02093 Warsaw, Poland.
    Special Issue Introduction: The Wonders and Mysteries Next Generation Sequencing Technologies Help Reveal2018Inngår i: Genes, ISSN 2073-4425, E-ISSN 2073-4425, Vol. 9, nr 10, artikkel-id 505Artikkel i tidsskrift (Annet vitenskapelig)
    Fulltekst (pdf)
    FULLTEXT01
  • 33.
    Khaliq, Zeeshan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Leijon, Mikael
    Belak, Sandor
    Komorowski, Jan
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    A complete map of potential pathogenicity markers of avian influenza virus subtype H5 predicted from 11 expressed proteins2015Inngår i: BMC Microbiology, ISSN 1471-2180, E-ISSN 1471-2180, Vol. 15, artikkel-id 128Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Polybasic cleavage sites of the hemagglutinin (HA) proteins are considered to be the most important determinants indicating virulence of the avian influenza viruses (AIV). However, evidence is accumulating that these sites alone are not sufficient to establish high pathogenicity. There need to exist other sites located on the HA protein outside the cleavage site or on the other proteins expressed by AIV that contribute to the pathogenicity. Results: We employed rule-based computational modeling to construct a map, with high statistical significance, of amino acid (AA) residues associated to pathogenicity in 11 proteins of the H5 type viruses. We found potential markers of pathogenicity in all of the 11 proteins expressed by the H5 type of AIV. AA mutations S-43(HA1)-D, D-83(HA1)-A in HA; S-269-D, E-41-H in NA; S-48-N, K-212-N in NS1; V-166-A in M1; G-14-E in M2; K-77-R, S-377-N in NP; and Q-48-P in PB1-F2 were identified as having a potential to shift the pathogenicity from low to high. Our results suggest that the low pathogenicity is common to most of the subtypes of the H5 AIV while the high pathogenicity is specific to each subtype. The models were developed using public data and validated on new, unseen sequences. Conclusions: Our models explicitly define a viral genetic background required for the virus to be highly pathogenic and thus confirm the hypothesis of the presence of pathogenicity markers beyond the cleavage site.

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  • 34.
    Khaliq, Zeeshan
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Leijon, Mikael
    Natl Vet Inst SVA, Dept Virol Parasitol & Immunobiol VIP, Uppsala, Sweden.;OIE Collaborating Ctr Biotechnol Based Diag Infec, Ulls Vag 2B & 26, SE-75689 Uppsala, Sweden..
    Belak, Sandor
    OIE Collaborating Ctr Biotechnol Based Diag Infec, Ulls Vag 2B & 26, SE-75689 Uppsala, Sweden.;Swedish Univ Agr Sci SLU, Dept Biomed Sci & Vet Publ Hlth BVF, Uppsala, Sweden..
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Uppsala universitet, Science for Life Laboratory, SciLifeLab. Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland..
    Identification of combinatorial host-specific signatures with a potential to affect host adaptation in influenza A H1N1 and H3N2 subtypes2016Inngår i: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 17, artikkel-id 529Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: The underlying strategies used by influenza A viruses (IAVs) to adapt to new hosts while crossing the species barrier are complex and yet to be understood completely. Several studies have been published identifying singular genomic signatures that indicate such a host switch. The complexity of the problem suggested that in addition to the singular signatures, there might be a combinatorial use of such genomic features, in nature, defining adaptation to hosts.

    Results: We used computational rule-based modeling to identify combinatorial sets of interacting amino acid (aa) residues in 12 proteins of IAVs of H1N1 and H3N2 subtypes. We built highly accurate rule-based models for each protein that could differentiate between viral aa sequences coming from avian and human hosts. We found 68 host-specific combinations of aa residues, potentially associated to host adaptation on HA, M1, M2, NP, NS1, NEP, PA, PA-X, PB1 and PB2 proteins of the H1N1 subtype and 24 on M1, M2, NEP, PB1 and PB2 proteins of the H3N2 subtypes. In addition to these combinations, we found 132 novel singular aa signatures distributed among all proteins, including the newly discovered PA-X protein, of both subtypes. We showed that HA, NA, NP, NS1, NEP, PA-X and PA proteins of the H1N1 subtype carry H1N1-specific and HA, NA, PA-X, PA, PB1-F2 and PB1 of the H3N2 subtype carry H3N2-specific signatures. M1, M2, PB1-F2, PB1 and PB2 of H1N1 subtype, in addition to H1N1 signatures, also carry H3N2 signatures. Similarly M1, M2, NP, NS1, NEP and PB2 of H3N2 subtype were shown to carry both H3N2 and H1N1 host-specific signatures (HSSs).

    Conclusions: To sum it up, we computationally constructed simple IF-THEN rule-based models that could distinguish between aa sequences of avian and human IAVs. From the rules we identified HSSs having a potential to affect the adaptation to specific hosts. The identification of combinatorial HSSs suggests that the process of adaptation of IAVs to a new host is more complex than previously suggested. The present study provides a basis for further detailed studies with the aim to elucidate the molecular mechanisms providing the foundation for the adaptation process.

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  • 35.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi.
    Learning Rule-Based Models - The Rough Set Approach2014Inngår i: Comprehensive Biomedical Physics: Volume 6: Bioinformatics / [ed] Bengt Persson, Elsevier, 2014, Vol. 6, s. 19-39Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Rough sets are a mathematically well-founded approach to induce minimal rules from examples represented in the form of decision systems. Rough sets have been successfully used to build classifiers in many domains including lifer sciences. This chapter introduces the concept of rough sets in a semi-formal way accessible to a non-expert reader. Rough sets have several advantages over other approaches such as, for instance, human legibility of the rule models by non-experts, and the ability to represent combinatorial models. Other advantages of rough sets include data reduction and uncertainty handling. With help of examples taken from a very broad spectrum of bioinformatics applications of rough sets ranging from functional genomics, to proteomics, to transcriptomics, to epigenetics, to cancer and HIV research, this introduction explains the basics of rough sets. It then continues to more advanced topics in building rough set classifiers. The focus is on the process of developing a rule-based model, its interpretation, and statistical significance, which discern this chapter from many a text on machine learning. Finally, rough sets are briefly compared to other learning approaches including some statistical approaches. The availability of ROSETTA allows learning how to use rough set modeling in practice. A discussion of relative advantages and disadvantages of rough sets ends this introduction.

  • 36. Kruczyk, Marcin
    et al.
    Baltzer, Nicholas
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Mieczkowski, Jakub
    Dramiński, Michał
    Koronacki, Jacek
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Random Reducts: A Monte Carlo Rough Set-based Method for Feature Selection in Large Datasets2013Inngår i: Fundamenta Informaticae, ISSN 0169-2968, E-ISSN 1875-8681, Vol. 127, nr 1-4, s. 273-288Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An important step prior to constructing a classifier for a very large data set is feature selection. With many problems it is possible to find a subset of attributes that have the same discriminative power as the full data set. There are many feature selection methods but in none of them are Rough Set models tied up with statistical argumentation. Moreover, known methods of feature selection usually discard shadowed features, i.e. those carrying the same or partially the same information as the selected features. In this study we present Random Reducts (RR) - a feature selection method which precedes classification per se. The method is based on the Monte Carlo Feature Selection (MCFS) layout and uses Rough Set Theory in the feature selection process. On synthetic data, we demonstrate that the method is able to select otherwise shadowed features of which the user should be made aware, and to find interactions in the data set.

  • 37.
    Kruczyk, Marcin
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi.
    Przanowski, Piotr
    Dabrowski, Michal
    Swiatek-Machado, Karolina
    Mieczkowski, Jakub
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Ronowicz, Anna
    Piotrowski, Arkadiusz
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Kaminska, Bozena
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Integration of genome-wide of Stat3 binding and epigenetic modification mapping with transcriptome reveals novel Stat3 target genes in glioma cells2014Inngår i: Biochimica et Biophysica Acta, ISSN 0006-3002, E-ISSN 1878-2434, Vol. 1839, nr 11, s. 1341-1350Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND: Signal transducer and activator of transcription 3 (STAT3) is constitutively activated in many human tumors, including gliomas, and regulates the expression of genes implicated in proliferation, survival, apoptosis, angiogenesis and immune regulation. Only a small fraction of those genes has been proven to be direct STAT3 targets. In gliomas, STAT3 can play tumor suppressive or oncogenic roles depending on the tumor genetic background with target genes being largely unknown.

    RESULTS: We used chromatin immunoprecipitation, promoter microarrays and deep sequencing to assess the genome-wide occupancy of phospho (p)-Stat3 and epigenetic modifications of H3K4me3 and H3ac in C6 glioma cells. This combined assessment identified a list of 1200 genes whose promoters have both Stat3 binding sites and epigenetic marks characteristic for actively transcribed genes. The Stat3 and histone markings data were also intersected with a set of microarray data from C6 glioma cells after inhibition of Jak2/Stat3 signaling. Subsequently, we found 284 genes characterized by p-Stat3 occupancy, activating histone marks and transcriptional changes. Novel genes were screened for their potential involvement in oncogenesis, and the most interesting hits were verified by ChIP-PCR and STAT3 knockdown in human glioma cells.

    CONCLUSIONS: Non-random association between silent genes, histone marks and p-Stat3 binding near transcription start sites was observed, consistent with its repressive role in transcriptional regulation of target genes in glioma cells with specific genetic background.

  • 38.
    Kruczyk, Marcin
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Umer, Husen Muhammad
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Enroth, Stefan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Genomik.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data2013Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 14, s. 280-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Finding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM. Results: Sensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually. Conclusions: We show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders.

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  • 39.
    Kruczyk, Marcin
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Zetterberg, Henrik
    Hansson, Oskar
    Rolstad, Sindre
    Minthon, Lennart
    Wallin, Anders
    Blennow, Kaj
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Andersson, Mats Gunnar
    Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment2012Inngår i: Journal of neural transmission, ISSN 0300-9564, E-ISSN 1435-1463, Vol. 119, nr 7, s. 821-831Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid beta (A beta 42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE epsilon 4 did not contribute to the predictive power of the model.

  • 40. Midelfart, H
    et al.
    Komorowski, J
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Centrum för bioinformatik.
    Nørsett, K
    Yadetie, F
    Sandvik, A.K.
    Learning rough set classifiers from gene expressions and clinical data2002Inngår i: Fundamenta Informaticae, Vol. 53, nr 2, s. 155-183Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capability opens a wide range of research opportunities in biology, but the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance.

    One application of microarray technology is cancer studies where supervised learning may be used for predicting tumor subtypes and clinical parameters. We present a general Rough Set approach for classification of tumor samples analyzed with microarrays. This approach is tested on a data set of gastric tumors, and we develop classifiers for six clinical parameters.

    One major obstacle in training classifiers from microarray data is that the number of objects is much smaller that the number of attributes. We therefore introduce a feature selection method based on bootstrapping for selecting genes that discriminate significantly between the classes, and study the performance of this method.

    Moreover, the efficacy of several learning and discretization methods implemented in the ROSETTA system [18] is examined. Their performance is compared to that of linear and quadratic discrimination analysis. The classifiers are also biologically validated. One of the best classifiers is selected for each clinical parameter, and the connection between the genes used in these classifiers and the parameters are compared to the establish knowledge in the biomedical literature.

  • 41. Mikhail, Fady M.
    et al.
    Descartes, Maria
    Piotrowski, Arkadiusz
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    de Ståhl, Teresita Diaz
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Bruder, Carl E. G.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Dumanski, Jan P.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Carroll, Andrew J.
    A previously unrecognized microdeletion syndrome on chromosome 22 band q11.2 encompassing the BCR gene2007Inngår i: American journal of medical genetics. Part A, ISSN 1552-4825, Vol. 143A, nr 18, s. 2178-2184Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Susceptibility of the chromosome 22q11.2 region to rearrangements has been recognized on the basis of common clinical disorders such as the DiGeorge/velocardiofacial syndrome (DG/VCFs). Recent evidence has implicated low-copy repeats (LCRs); also known as segmental duplications; on 22q as mediators of nonallelic homologous recombination (NAHR) that result in rearrangements of 22q11.2. It has been shown that both deletion and duplication events can occur as a result of NAHR caused by unequal crossover of LCRs. Here we report on the clinical, cytogenetic and array CGH studies of a 15-year-old Hispanic boy with history of learning and behavior problems. We suggest that he represents a previously unrecognized microdeletion syndrome on chromosome 22 band q11.2 just telomeric to the DG/VCFs typically deleted region and encompassing the BCR gene. Using a 32K BAC array CGH chip we were able to refine and precisely narrow the breakpoints of this microdeletion, which was estimated to be 1.55-1.92 Mb in size and to span approximately 20 genes. This microdeletion region is flanked by LCR clusters containing several modules with a very high degree of sequence homology (>95%), and therefore could play a causal role in its origin.

  • 42. Mikhail, Fady M.
    et al.
    Sathienkijkanchai, Achara
    Robin, Nathaniel H.
    Prucka, Sandra
    Biggerstaff, Julie Sanford
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Bruder, Carl
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Piotrowski, Arkadiusz
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    de Ståhl, Teresita Diaz
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Dumanski, Jan P.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Carroll, Andrew J.
    Overlapping phenotype of wolf-hirschhorn and beckwith-wiedemann syndromes in a girl with der(4)t(4; 1 1)(pter;pter)2007Inngår i: American Journal of Medical Genetics, Part A, ISSN 1552-4825, Vol. 143, nr 15, s. 1760-1766Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We report on an 8-month-old girl with a novel unbalanced chromosomal rearrangement, consisting of a terminal deletion of 4p and a paternal duplication of terminal 11p. Each of these is associated with the well-known clinical phenotypes of Wolf-Hirschhorn syndrome (WHS) and Beckwith-Wiedemann syndrome (BWS), respectively. She presented for clinical evaluation of dysmorphic facial features, developmental delay, atrial septal defect (ASD), and left hydro-nephrosis. High-resolution cytogenetic analysis revealed a normal female karyotype, but subtelomeric fluorescence in situ hybridization (FISH) analysis revealed a der(4)t(4;11) (pter;pter). Both FISH and microarray CGH studies clearly demonstrated that the WHS critical regions 1 and 2 were deleted, and that the BWS imprinted domains (ID) 1 and 2 were duplicated on the der(4). Parental chromosome analysis revealed that the father carried a cryptic balanced t(4;11)(pter;pter). As expected, our patient manifests findings of both WHS (a growth retardation syndrome) and BWS (an overgrowth syndrome). We compare her unique phenotypic features with those that have been reported for both syndromes.

  • 43.
    Moghadam, Behrooz Torabi
    et al.
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
    Etemadikhah, Mitra
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Rajkowska, Grazyna
    Univ Mississippi, Med Ctr, Dept Psychiat & Human Behav, Jackson, MS 39216 USA.
    Stocluneier, Craig
    Univ Mississippi, Med Ctr, Dept Psychiat & Human Behav, Jackson, MS 39216 USA.
    Grabherr, Manfred
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland.
    Feuk, Lars
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
    Lindholm Carlström, Eva
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods2019Inngår i: Journal of Psychiatric Research, ISSN 0022-3956, E-ISSN 1879-1379, Vol. 114, s. 41-47Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigenetic analyses have yielded ambiguous and sometimes conflicting results. Here, we analyzed fresh frozen post-mortem brain tissue from a cohort of 73 subjects diagnosed with schizophrenia and 52 control samples, using the Illumina Infinium HumanMethylation450 Bead Chip, to investigate genome-wide DNA methylation patterns in the two groups. Analysis of differential methylation was performed with the Bioconductor Minfi package and modern machine-learning and visualization techniques, which were shown previously to be successful in detecting and highlighting differentially methylated patterns in case-control studies. In this dataset, however, these methods did not uncover any significant signals discerning the patient group and healthy controls, suggesting that if there are methylation changes associated with schizophrenia, they are heterogeneous and complex with small effect.

  • 44.
    Motallebipour, Mehdi
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Ameur, Adam
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Bysani, Madhusudhan Reddy
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Patra, Kalicharan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för fysiologi och utvecklingsbiologi, Zoologisk utvecklingsbiologi.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Mangion, Jonathan
    Barker, Melissa
    McKernan, Kevin
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik.
    Differential binding and co-binding pattern of FOXA1 and FOXA3 and their relation to H3K4me3 in HepG2 cells revealed by ChIP-seq2009Inngår i: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 10, nr 11, s. R129-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND: The forkhead box/winged helix family members FOXA1, FOXA2, and FOXA3 are of high importance in development and specification of the hepatic linage and the continued expression of liver-specific genes. RESULTS: Here, we present a genome-wide location analysis of FOXA1 and FOXA3 binding sites in HepG2 cells through chromatin immunoprecipitation with detection by sequencing (ChIP-seq) studies and compare these with our previous results on FOXA2. We found that these factors often bind close to each other in different combinations and consecutive immunoprecipitation of chromatin for one and then a second factor (ChIP-reChIP) shows that this occurs in the same cell and on the same DNA molecule, suggestive of molecular interactions. Using co-immunoprecipitation, we further show that FOXA2 interacts with both FOXA1 and FOXA3 in vivo, while FOXA1 and FOXA3 do not appear to interact. Additionally, we detected diverse patterns of trimethylation of lysine 4 on histone H3 (H3K4me3) at transcriptional start sites and directionality of this modification at FOXA binding sites. Using the sequence reads at polymorphic positions, we were able to predict allele specific binding for FOXA1, FOXA3, and H3K4me3. Finally, several SNPs associated with diseases and quantitative traits were located in the enriched regions. CONCLUSIONS: We find that ChIP-seq can be used not only to create gene regulatory maps but also to predict molecular interactions and to inform on the mechanisms for common quantitative variation.

  • 45.
    Nord, Helena
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Hartmann, Christian
    Andersson, Robin
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Menzel, Uwe
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Pfeifer, Susan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kvinnors och barns hälsa, Pediatrik.
    Piotrowski, Arkadiusz
    Bogdan, Adam
    Kloc, Wojciech
    Sandgren, Johanna
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kirurgiska vetenskaper, Endokrinkirurgi.
    Olofsson, Tommie
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Hesselager, Göran
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Neurokirurgi.
    Blomquist, Erik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för onkologi, radiologi och klinisk immunologi, Enheten för onkologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    von Deimling, Andreas
    Bruder, Carl E. G.
    Southern Research Institute, Birmingham, AL, USA.
    Dumanski, Jan P.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    de Ståhl, Teresita Díaz
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Characterization of novel and complex genomic aberrations in glioblastoma using a 32K BAC array2009Inngår i: Neuro-Oncology, ISSN 1522-8517, E-ISSN 1523-5866, Vol. 11, nr 6, s. 803-818Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Glioblastomas (GBs) are malignant CNS tumors often associated with devastating symptoms. Patients with GB have a very poor prognosis, and despite treatment, most of them die within 12 months from diagnosis. Several pathways, such as the RAS, tumor protein 53 (TP53), and phosphoinositide kinase 3 (PIK3) pathways, as well as the cell cycle control pathway, have been identified to be disrupted in this tumor. However, emerging data suggest that these aberrations represent only a fraction of the genetic changes involved in gliomagenesis. In this study, we have applied a 32K clone-based genomic array, covering 99% of the current assembly of the human genome, to the detailed genetic profiling of a set of 78 GBs. Complex patterns of aberrations, including high and narrow copy number amplicons, as well as a number of homozygously deleted loci, were identified. Amplicons that varied both in number (three on average) and in size (1.4 Mb on average) were frequently detected (81% of the samples). The loci encompassed not only previously reported oncogenes (EGFR, PDGFRA, MDM2, and CDK4) but also numerous novel oncogenes as GRB10, MKLN1, PPARGC1A, HGF, NAV3, CNTN1, SYT1, and ADAMTSL3. BNC2, PTPLAD2, and PTPRE, on the other hand, represent novel candidate tumor suppressor genes encompassed within homozygously deleted loci. Many of these genes are already linked to several forms of cancer; others represent new candidate genes that may serve as prognostic markers or even as therapeutic targets in the future. The large individual variation observed between the samples demonstrates the underlying complexity of the disease and strengthens the demand for an individualized therapy based on the genetic profile of the patient.

  • 46. Nørsett, Kristin G
    et al.
    Laegreid, Astrid
    Midelfart, Herman
    Yadetie, Fekadu
    Erlandsen, Sten Even
    Falkmer, Sture
    Grønbech, Jon E
    Waldum, Helge L
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Teknisk-naturvetenskapliga fakulteten, Biologiska sektionen, Centrum för bioinformatik.
    Sandvik, Arne K
    Gene expression based classification of gastric carcinoma.2004Inngår i: Cancer Lett, ISSN 0304-3835, Vol. 210, nr 2, s. 227-37Artikkel i tidsskrift (Fagfellevurdert)
  • 47.
    Orzechowski Westholm, Jakub
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Tronnersjö, Susanna
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi.
    Nordberg, Niklas
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinsk biokemi och mikrobiologi.
    Olsson, Ida
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Ronne, Hans
    Gis1 and Rph1 Regulate Glycerol and Acetate Metabolism in Glucose Depleted Yeast Cells2012Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, nr 2, s. e31577-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aging in organisms as diverse as yeast, nematodes, and mammals is delayed by caloric restriction, an effect mediated by the nutrient sensing TOR, RAS/cAMP, and AKT/Sch9 pathways. The transcription factor Gis1 functions downstream of these pathways in extending the lifespan of nutrient restricted yeast cells, but the mechanisms involved are still poorly understood. We have used gene expression microarrays to study the targets of Gis1 and the related protein Rph1 in different growth phases. Our results show that Gis1 and Rph1 act both as repressors and activators, on overlapping sets of genes as well as on distinct targets. Interestingly, both the activities and the target specificities of Gis1 and Rph1 depend on the growth phase. Thus, both proteins are associated with repression during exponential growth, targeting genes with STRE or PDS motifs in their promoters. After the diauxic shift, both become involved in activation, with Gis1 acting primarily on genes with PDS motifs, and Rph1 on genes with STRE motifs. Significantly, Gis1 and Rph1 control a number of genes involved in acetate and glycerol formation, metabolites that have been implicated in aging. Furthermore, several genes involved in acetyl CoA metabolism are downregulated by Gis1.

    Fulltekst (pdf)
    fulltext
  • 48. Przanowski, Piotr
    et al.
    Dabrowski, Michal
    Ellert-Miklaszewska, Aleksandra
    Kloss, Michal
    Mieczkowski, Jakub
    Kaza, Beata
    Ronowicz, Anna
    Hu, Feng
    Piotrowski, Arkadiusz
    Kettenmann, Helmut
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräknings- och systembiologi.
    Kaminska, Bozena
    The signal transducers Stat1 and Stat3 and their novel target Jmjd3 drive the expression of inflammatory genes in microglia2014Inngår i: Journal of Molecular Medicine, ISSN 0946-2716, E-ISSN 1432-1440, Vol. 92, nr 3, s. 239-254Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Most neurological diseases are associated with chronic inflammation initiated by the activation of microglia, which produce cytotoxic and inflammatory factors. Signal transducers and activators of transcription (STATs) are potent regulators of gene expression but contribution of particular STAT to inflammatory gene expression and STAT-dependent transcriptional networks underlying brain inflammation need to be identified. In the present study, we investigated the genomic distribution of Stat binding sites and the role of Stats in the gene expression in lipopolysaccharide (LPS)-activated primary microglial cultures. Integration of chromatin immunoprecipitation-promoter microarray data and transcriptome data revealed novel Stat-target genes including Jmjd3, Ccl5, Ezr, Ifih1, Irf7, Uba7, and Pim1. While knockdown of individual Stat had little effect on the expression of tested genes, knockdown of both Stat1 and Stat3 inhibited the expression of Jmjd3 and inflammatory genes. Transcriptional regulation of Jmjd3 by Stat1 and Stat3 is a novel mechanism crucial for launching inflammatory responses in microglia. The effects of Jmjd3 on inflammatory gene expression were independent of its H3K27me3 demethylase activity. Forced expression of constitutively activated Stat1 and Stat3 induced the expression of Jmjd3, inflammation-related genes, and the production of proinflammatory cytokines as potently as lipopolysacharide. Gene set enrichment and gene function analysis revealed categories linked to the inflammatory response in LPS and Stat1C + Stat3C groups. We defined upstream pathways that activate STATs in response to LPS and demonstrated contribution of Tlr4 and Il-6 and interferon-. signaling. Our findings define novel direct transcriptional targets of Stat1 and Stat3 and highlight their contribution to inflammatory gene expression.

    Fulltekst (pdf)
    fulltext
  • 49.
    Rada-Iglesias, Alvaro
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Enroth, Stefan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Ameur, Adam
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Koch, Christoph M.
    Clelland, Gayle K.
    Respuela-Alonso, Patricia
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Wilcox, Sarah
    Dovey, Oliver M.
    Ellis, Peter D.
    Langford, Cordelia F.
    Dunham, Ian
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Butyrate mediates decrease of histone acetylation centered on transcription start sites and down-regulation of associated genes2007Inngår i: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 17, nr 6, s. 708-719Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Butyrate is a histone deacetylase inhibitor (HDACi) with anti-neoplastic properties, which theoretically reactivates epigenetically silenced genes by increasing global histone acetylation. However, recent studies indicate that a similar number or even more genes are down-regulated than up-regulated by this drug. We treated hepatocarcinoma HepG2 cells with butyrate and characterized the levels of acetylation at DNA-bound histones H3 and H4 by ChIP-chip along the ENCODE regions. In contrast to the global increases of histone acetylation, many genomic regions close to transcription start sites were deacetylated after butyrate exposure. In order to validate these findings, we found that both butyrate and trichostatin A treatment resulted in histone deacetylation at selected regions, while nucleosome loss or changes in histone H3 lysine 4 trimethylation (H3K4me3) did not occur in such locations. Furthermore, similar histone deacetylation events were observed when colon adenocarcinoma HT-29 cells were treated with butyrate. In addition, genes with deacetylated promoters were down-regulated by butyrate, and this was mediated at the transcriptional level by affecting RNA polymerase II (POLR2A) initiation/elongation. Finally, the global increase in acetylated histones was preferentially localized to the nuclear periphery, indicating that it might not be associated to euchromatin. Our results are significant for the evaluation of HDACi as anti-tumourogenic drugs, suggesting that previous models of action might need to be revised, and provides an explanation for the frequently observed repression of many genes during HDACi treatment.

  • 50.
    Rada-Iglesias, Alvaro
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Wallerman, Ola
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Koch, Christoph
    Ameur, Adam
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Enroth, Stefan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Clelland, Gayle
    Wester, Kenneth
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Wilcox, Sarah
    Dovey, Oliver M
    Ellis, Peter D
    Wraight, Vicki L
    James, Keith
    Andrews, Rob
    Langford, Cordelia
    Dhami, Pawandeep
    Carter, Nigel
    Vetrie, David
    Pontén, Fredrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Komorowski, Jan
    Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik.
    Dunham, Ian
    Wadelius, Claes
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi.
    Binding sites for metabolic disease related transcription factors inferred at base pair resolution by chromatin immunoprecipitation and genomic microarrays2005Inngår i: Human Molecular Genetics, ISSN 0964-6906, E-ISSN 1460-2083, Vol. 14, nr 22, s. 3435-3447Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present a detailed in vivo characterization of hepatocyte transcriptional regulation in HepG2 cells, using chromatin immunoprecipitation and detection on PCR fragment-based genomic tiling path arrays covering the encyclopedia of DNA element (ENCODE) regions. Our data suggest that HNF-4α and HNF-3β, which were commonly bound to distal regulatory elements, may cooperate in the regulation of a large fraction of the liver transcriptome and that both HNF-4α and USF1 may promote H3 acetylation to many of their targets. Importantly, bioinformatic analysis of the sequences bound by each transcription factor (TF) shows an over-representation of motifs highly similar to the in vitro established consensus sequences. On the basis of these data, we have inferred tentative binding sites at base pair resolution. Some of these sites have been previously found by in vitro analysis and some were verified in vitro in this study. Our data suggests that a similar approach could be used for the in vivo characterization of all predicted/uncharacterized TF and that the analysis could be scaled to the whole genome.

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