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  • 51.
    Dress A, Grunewald S, Gutman I, Lepovic M, Vidovic D
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    On the number of walks in trees2003In: MATCH-COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY, ISSN 0340-6253, no 48, p. 63-85Article in journal (Refereed)
  • 52. Dress, A
    et al.
    Huber, KT
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics. V.
    Antipodal metrics and split systems2002In: European Journal of Combinatorics, ISSN 0195-6698, Vol. 23, no 2, p. 187-200Article in journal (Refereed)
    Abstract [en]

    Recall that a metric d on a finite set X is called antipodal if there exists a map sigma : X --> X: x --> (x) over bar so that d(x, (x) over bar) = d(x, y) + d(y, (x) over bar) holds for all x, y epsilon X. Antipodal metrics canonically arise as metrics induced on specific weighted graphs, although their abundance becomes clearer in light of the fact that any finite metric space can be isometrically embedded in a more or less canonical way into an antipodal metric space called its full antipodal extension. In this paper, we examine in some detail antipodal metrics that are, in addition, totally split decomposable. In particular, we give an explicit characterization of such metrics, and prove that-somewhat surprisingly-the full antipodal extension of a proper metric d on a finite set X is totally split decomposable if and only if d is linear or #X = 3 holds.

  • 53. Dress, A
    et al.
    Huber, KT
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    An explicit computation of the injective hull of certain finite metric spaces in terms of their associated Buneman complex2002In: Advances in Mathematics, ISSN 0001-8708, Vol. 168, no 1, p. 1-28Article in journal (Other (popular scientific, debate etc.))
  • 54. Dress, A
    et al.
    Koolen, JH
    Moulton, V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    On line arrangements in the hyperbolic plane2002In: European Journal of Combinatorics, Vol. 23, no 5, p. 549-557Article in journal (Other (popular scientific, debate etc.))
    Abstract [en]

    Given a finite collection L of lines in the hyperbolic plane H, we denote by k = k(L) its Karzanov number, i.e., the maximal number of pairwise intersecting lines in L, and by C(L) and n = n(L) the set and the number, respectively, of those points at infinity that are incident with at least one line from L. By using purely combinatorial properties of cyclic seta:, it is shown that #L less than or equal to 2nk - ((2k+1)(2)) always holds and that #L equals 2nk - ((2k+1)(2)) if and only if there is no collection L' of lines in H with L subset of or equal to L', k(L') = k(L) and C(L') = C(L).

  • 55.
    Dress, Andreas, Giegerich, Robert, Grunewald, Stefan, Wagner, Holger
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Fibonacci-Cayley numbers and repetition patterns in genomic DNA2003In: Ann. Comb., no 3, p. 259-279Article in journal (Refereed)
  • 56. Dress, Andreas
    et al.
    Huber, Katharina
    Lesser, Alice
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Hereditarily Optimal Realizations of Consistent Metrics.2006In: Annals of Combinatorics, ISSN 0218-0006, E-ISSN 0219-3094, Vol. 10, no 1, p. 63-76Article in journal (Refereed)
    Abstract [en]

    One of the main problems in phylogenetics is to find good approximations of metrics by weighted trees. As an aid to solving this problem, it could be tempting to consider optimal realizations of metrics—the guiding principle being that, the (necessarily unique) optimal realization of a tree metric is the weighted tree that realizes this metric. And, although optimal realizations of arbitrary metrics are, in general, not trees, but rather weighted networks, one could still hope to obtain a phylogenetically informative representation of a given metric, maybe even more informative than the best approximating tree. However, optimal realizations are not only difficult to compute, they may also be non-unique. Here we focus on one possible way out of this dilemma: hereditarily optimal realizations. These are essentially unique, and can be described in a rather explicit way. In this paper, we recall what a hereditarily optimal realization of a metric is and how it is related to the 1-skeleton of the tight span of that metric, and we investigate under what conditions it coincides with this 1-skeleton. As a consequence, we will show that hereditarily optimal realizations for consistent metrics, a large class of phylogentically relevant metrics, can be computed in a straight-forward fashion.

  • 57.
    Edvardsson S, Gardner PP, Poole AM, Hendy MD, Penny D, Moulton V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    A search for H/ACA snoRNAs in yeast using MFE secondary structure prediction2003In: Bioinformatics, ISSN 1367-4803, Vol. 19, no 7, p. 865-873Article in journal (Refereed)
  • 58.
    Enroth, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    The Nucleosome as a Signal Carrying Unit: From Experimental Data to Combinatorial Models of Transcriptional Control2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The human genome consists of over 3 billion nucleotides and would be around 2 meters long if uncoiled and laid out. Each human somatic cell contains all this in their nucleus which is only around 5 µm across. This extreme compaction is largely achieved by wrapping the DNA around a histone octamer, the nucleosome. Still, the DNA is accessible to the transcriptional machinery and this regulation is highly dynamic and change rapidly with, e.g. exposure to drugs. The individual histone proteins can carry specific modifications such as methylations and acetylations. These modifications are a major part of the epigenetic status of the DNA which contributes significantly to the transcriptional status of a gene - certain modifications repress transcription and others are necessary for transcription to occur. Specific histone methylations and acetylations have also been implicated in more detailed regulation such as inclusion/exclusion of individual exons, i.e. splicing. Thus, the nucleosome is involved in chromatin remodeling and transcriptional regulation – both directly from steric hindrance but also as a signaling platform via the epigenetic modifications.

    In this work, we have developed tools for storage (Paper I) and normalization (Paper II) of next generation sequencing data in general, and analyzed nucleosome locations and histone modification in particular (Paper I, III and IV). The computational tools developed allowed us as one of the first groups to discover well positioned nucleosomes over internal exons in such wide spread organisms as worm, mouse and human. We have also provided biological insight into how the epigenetic histone modifications can control exon expression in a combinatorial way. This was achieved by applying a Monte Carlo feature selection system in combination with rule based modeling of exon expression. The constructed model was validated on data generated in three additional cell types suggesting a general mechanism.

     

    List of papers
    1. SICTIN: Rapid footprinting of massively parallel sequencing data
    Open this publication in new window or tab >>SICTIN: Rapid footprinting of massively parallel sequencing data
    2010 (English)In: BioData Mining, ISSN 1756-0381, E-ISSN 1756-0381, Vol. 3, article id 4Article in journal (Refereed) Published
    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.

    National Category
    Medical and Health Sciences Mathematics
    Identifiers
    urn:nbn:se:uu:diva-129177 (URN)10.1186/1756-0381-3-4 (DOI)000208761100004 ()20707885 (PubMedID)
    Available from: 2010-08-10 Created: 2010-08-06 Last updated: 2017-12-12Bibliographically approved
    2.
    The record could not be found. The reason may be that the record is no longer available or you may have typed in a wrong id in the address field.
    3. Nucleosomes are well positioned in exons and carry characteristic histone modifications
    Open this publication in new window or tab >>Nucleosomes are well positioned in exons and carry characteristic histone modifications
    Show others...
    2009 (English)In: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 19, no 10, p. 1732-1741Article in journal (Refereed) Published
    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.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-107609 (URN)10.1101/gr.092353.109 (DOI)000270389700005 ()19687145 (PubMedID)
    Note

    De tre första författarna delar första författarskapet.

    Available from: 2009-08-19 Created: 2009-08-19 Last updated: 2017-12-13Bibliographically approved
    4. Combinations of histone modifications control exon expression
    Open this publication in new window or tab >>Combinations of histone modifications control exon expression
    (English)Article in journal (Other academic) Submitted
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-129178 (URN)
    Available from: 2010-08-10 Created: 2010-08-06 Last updated: 2010-12-22Bibliographically approved
  • 59.
    Enroth, Stefan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Genomics. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andersson, Claes R.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Andersson, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    A strand specific high resolution normalization method for chip-sequencing data employing multiple experimental control measurements2012In: Algorithms for Molecular Biology, ISSN 1748-7188, E-ISSN 1748-7188, Vol. 7, p. 2-Article in journal (Refereed)
    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.

  • 60.
    Enroth, Stefan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Andersson, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bysani, Madhusudhan Reddy
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics.
    Wallerman, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics.
    Tuch, Brian
    De la Vega, Fransisco
    Heldin, Carl-Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwig Institute for Cancer Research.
    Moustakas, Aristidis
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwig Institute for Cancer Research.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics.
    Nucleosome regulatory dynamics in response to TGF-beta treatment in HepG2 cells2014In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 42, no 11, p. 6921-6934Article in journal (Refereed)
  • 61.
    Enroth, Stefan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Genomics. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Andersson, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bysani, Madhusudhan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wallerman, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Termén, Stefan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwig Institute for Cancer Research. Uppsala University, 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 University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwig Institute for Cancer Research. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Moustakas, Aristidis
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwig Institute for Cancer Research. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, 01-248 Warszawa, Poland.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nucleosome regulatory dynamics in response to TGF beta2014In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 42, no 11, p. 6921-6934Article in journal (Refereed)
    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.

  • 62.
    Enroth, Stefan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Andersson, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    SICTIN: Rapid footprinting of massively parallel sequencing data2010In: BioData Mining, ISSN 1756-0381, E-ISSN 1756-0381, Vol. 3, article id 4Article in journal (Refereed)
    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.

  • 63.
    Enroth, Stefan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bornelöv, Susanne
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Combinations of histone modifications control exon expressionArticle in journal (Other academic)
  • 64.
    Enroth, Stefan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bornelöv, Susanne
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Combinations of histone modifications control exon expressionArticle in journal (Other academic)
  • 65.
    Enroth, Stefan
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Rada-Iglesisas, Alvaro
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Andersson, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wallerman, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wanders, Alkwin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Pahlman, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Colorectal Surgery.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Genetics. Uppsala University, 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 mucosa2011In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 11, p. 450-Article in journal (Refereed)
    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.

  • 66. Forslund, Kristoffer
    et al.
    Huson, Daniel H
    Moulton, Vincent
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    VisRD--visual recombination detection.2004In: Bioinformatics, ISSN 1367-4803, Vol. 20, no 18, p. 3654-5Article in journal (Refereed)
  • 67.
    Freyhult, E
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Andersson, K
    Gustafsson, M G
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Technology, Department of Engineering Sciences. Signals and systems.
    Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR2003In: Biophysical Journal, Vol. 84, no 4, p. 2264-2272Article in journal (Refereed)
  • 68.
    Freyhult, Eva
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    A Study in RNA Bioinformatics: Identification, Prediction and Analysis2007Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Research in the last few decades has revealed the great capacity of the RNA molecule. RNA, which previously was assumed to play a main role only as an intermediate in the translation of genes to proteins, is today known to play many important roles in the cell in addition to that as a messenger RNA and transfer RNA, including the ability to catalyze reactions and gene regulations at various levels.

    This thesis investigates several computational aspects of RNA. We will discuss identification of novel RNAs and RNAs that are known to exist in related species, RNA secondary structure prediction, as well as more general tools for analyzing, visualizing and classifying RNA sequences.

    We present two benchmark studies concerning RNA identification, both de novo identification/characterization of single RNA sequences and homology search methods.

    We develope a novel algorithm for analysis of the RNA folding landscape that is based on the nearest neighbor energy model adopted in many secondary structure prediction programs. We implement this algorithm, which computes structural neighbors of a given RNA secondary structure, in the program RNAbor, which is accessible on a web server.

    Furthermore, we combine a mutual information based structure prediction algorithm with a sequence logo visualization to create a novel visualization tool for analyzing an RNA alignment and identifying covarying sites.

    Finally, we present extensions to sequence logos for the purpose of tRNA identity analysis. We introduce function logos, which display features that distinguish functional subclasses within a large set of structurally related sequences, as well as the inverse logos, which display underrepresented features. For the purpose of comparing tRNA identity elements between different taxa we introduce two contrasting logos, the information difference and the Kullback-Leibler divergence difference logos.

    List of papers
    1. A comparison of RNA folding measures
    Open this publication in new window or tab >>A comparison of RNA folding measures
    2005 In: BMC Bioinformatics, ISSN 1471-2105, Vol. 6, p. 241-Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-96423 (URN)
    Available from: 2007-11-13 Created: 2007-11-13Bibliographically approved
    2. Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA
    Open this publication in new window or tab >>Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA
    2007 (English)In: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 17, no 1, p. 117-125Article in journal (Refereed) Published
    Abstract [en]

    Homology search is one of the most ubiquitous bioinformatic tasks, yet it is unknown how effective the currently available tools are for identifying noncoding RNAs (ncRNAs). In this work, we use reliable ncRNA data sets to assess the effectiveness of methods such as BLAST, FASTA, HMMer, and Infernal. Surprisingly, the most popular homology search methods are often the least accurate. As a result, many studies have used inappropriate tools for their analyses. On the basis of our results, we suggest homology search strategies using the currently available tools and some directions for future development.

    National Category
    Biological Sciences
    Identifiers
    urn:nbn:se:uu:diva-96424 (URN)10.1101/gr.5890907 (DOI)000243191400015 ()17151342 (PubMedID)
    Available from: 2007-11-13 Created: 2007-11-13 Last updated: 2017-12-14Bibliographically approved
    3. Boltzmann probability of RNA structural neighbors and riboswitch detection
    Open this publication in new window or tab >>Boltzmann probability of RNA structural neighbors and riboswitch detection
    2007 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 23, no 16, p. 2054-2062Article in journal (Refereed) Published
    Abstract [en]

    Motivation: We describe algorithms implemented in a new software package, RNAbor, to investigate structures in a neighborhood of an input secondary structure of an RNA sequence s. The input structure could be the minimum free energy structure, the secondary structure obtained by analysis of the X-ray structure or by comparative sequence analysis, or an arbitrary intermediate structure.

    Results: A secondary structure of s is called a -neighbor of if and differ by exactly base pairs. RNAbor computes the number (N), the Boltzmann partition function (Z) and the minimum free energy (MFE) and corresponding structure over the collection of all -neighbors of . This computation is done simultaneously for all m, in run time O (mn3) and memory O(mn2), where n is the sequence length. We apply RNAbor for the detection of possible RNA conformational switches, and compare RNAbor with the switch detection method paRNAss. We also provide examples of how RNAbor can at times improve the accuracy of secondary structure prediction.

    National Category
    Biological Sciences Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-96425 (URN)10.1093/bioinformatics/btm314 (DOI)000249818300004 ()
    Available from: 2007-11-13 Created: 2007-11-13 Last updated: 2018-01-13Bibliographically approved
    4. RNAbor: a web server for RNA structural neighbors
    Open this publication in new window or tab >>RNAbor: a web server for RNA structural neighbors
    2007 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 35, no Suppl. S: Web Server issue, p. W305-W309Article in journal (Refereed) Published
    Abstract [en]

    RNAbor provides a new tool for researchers in the biological and related sciences to explore important aspects of RNA secondary structure and folding pathways. RNAbor computes statistics concerning delta-neighbors of a given input RNA sequence and structure (the structure can, for example, be the minimum free energy (MFE) structure). A delta-neighbor is a structure that differs from the input structure by exactly delta base pairs, that is, it can be obtained from the input structure by adding and/or removing exactly d base pairs. For each distance delta RNAbor computes the density of delta-neighbors, the number of delta-neighbors, and the MFE structure, or MFEd structure, among all delta-neighbors. RNAbor can be used to study possible folding pathways, to determine alternate low-energy structures, to predict potential nucleation sites and to explore structural neighbors of an intermediate, biologically active structure. The web server is available at http://bioinformatics.bc.edu/clotelab/RNAbor.

    Keywords
    Cluster Analysis, Computational Biology/*methods, Computer Simulation, Conserved Sequence, Databases, Genetic, Internet, Molecular Sequence Data, Nucleic Acid Conformation, RNA/*chemistry, RNA, Untranslated, Regulatory Sequences, Ribonucleic Acid, Sequence Alignment, Sequence Analysis, RNA, Sequence Homology, Nucleic Acid
    National Category
    Biological Sciences
    Identifiers
    urn:nbn:se:uu:diva-96426 (URN)10.1093/nar/gkm255 (DOI)000255311500057 ()17526527 (PubMedID)
    Available from: 2007-11-13 Created: 2007-11-13 Last updated: 2017-12-14Bibliographically approved
    5. Predicting RNA structure using mutual information
    Open this publication in new window or tab >>Predicting RNA structure using mutual information
    2005 In: Applied Bioinformatics, ISSN 1175-5636, Vol. 4, no 1, p. 53-59Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-96427 (URN)
    Available from: 2007-11-13 Created: 2007-11-13Bibliographically approved
    6. Visualizing bacterial tRNA identity determinants and antideterminants using function logos and inverse function logos
    Open this publication in new window or tab >>Visualizing bacterial tRNA identity determinants and antideterminants using function logos and inverse function logos
    2006 In: Nucleic Acids Research, ISSN 0305-1048, Vol. 34, no 3, p. 905-916Article in journal (Refereed) Published
    Identifiers
    urn:nbn:se:uu:diva-96428 (URN)
    Available from: 2007-11-13 Created: 2007-11-13Bibliographically approved
    7. New computational methods reveal tRNA identity element divergence between Proteobacteria and Cyanobacteria
    Open this publication in new window or tab >>New computational methods reveal tRNA identity element divergence between Proteobacteria and Cyanobacteria
    2007 (English)In: Biochimie, ISSN 0300-9084, E-ISSN 1638-6183, Vol. 89, no 10, p. 1276-1288Article in journal (Refereed) Published
    Abstract [en]

    There are at least 21 subfunctional classes of tRNAs in most cells that, despite a very highly conserved and compact common structure, must interact specifically with different cliques of proteins or cause grave organismal consequences. Protein recognition of specific tRNA substrates is achieved in part through class-restricted tRNA features called tRNA identity determinants. In earlier work we used TFAM, a statistical classifier of tRNA function, to show evidence of unexpectedly large diversity among bacteria in tRNA identity determinants. We also created a data reduction technique called function logos to visualize identity determinants for a given taxon. Here we show evidence that determinants for lysylated isoleucine tRNAs are not the same in Proteobacteria as in other bacterial groups including the Cyanobacteria. Consistent with this, the lysylating biosynthetic enzyme TilS lacks a C-terminal domain in Cyanobacteria that is present in Proteobacteria. We present here, using function logos, a map estimating all potential identity determinants generally operational in Cyanobacteria and Proteobacteria. To further isolate the differences in potential tRNA identity determinants between Proteobacteria and Cyanobacteria, we created two new data reduction visualizations to contrast sequence and function logos between two taxa. One, called Information Difference logos (ID logos), shows the evolutionary gain or retention of functional information associated to features in one lineage. The other, Kullback–Leibler divergence Difference logos (KLD logos), shows recruitments or shifts in the functional associations of features, especially those informative in both lineages. We used these new logos to specifically isolate and visualize the differences in potential tRNA identity determinants between Proteobacteria and Cyanobacteria. Our graphical results point to numerous differences in potential tRNA identity determinants between these groups. Although more differences in general are explained by shifts in functional association rather than gains or losses, the apparent identity differences in lysylated isoleucine tRNAs appear to have evolved through both mechanisms.

    Keywords
    tRNA identity, Function logos, tRNA identity determinants, Lysylated isoleucine tRNA, TilS, Aminoacyl-tRNA synthetase, Proteobacteria, Cyanobacteria, Kullback–Leibler Divergence
    National Category
    Biological Sciences
    Identifiers
    urn:nbn:se:uu:diva-96429 (URN)10.1016/j.biochi.2007.07.013 (DOI)000250613600015 ()17889982 (PubMedID)
    Available from: 2007-11-13 Created: 2007-11-13 Last updated: 2017-12-14Bibliographically approved
  • 69.
    Freyhult, Eva
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bollback, Jonathan P.
    Gardner, Paul P.
    Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA2007In: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 17, no 1, p. 117-125Article in journal (Refereed)
    Abstract [en]

    Homology search is one of the most ubiquitous bioinformatic tasks, yet it is unknown how effective the currently available tools are for identifying noncoding RNAs (ncRNAs). In this work, we use reliable ncRNA data sets to assess the effectiveness of methods such as BLAST, FASTA, HMMer, and Infernal. Surprisingly, the most popular homology search methods are often the least accurate. As a result, many studies have used inappropriate tools for their analyses. On the basis of our results, we suggest homology search strategies using the currently available tools and some directions for future development.

  • 70.
    Freyhult, Eva
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Cui, Yuanyuan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Nilsson, Olle
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Ardell, David H.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    New computational methods reveal tRNA identity element divergence between Proteobacteria and Cyanobacteria2007In: Biochimie, ISSN 0300-9084, E-ISSN 1638-6183, Vol. 89, no 10, p. 1276-1288Article in journal (Refereed)
    Abstract [en]

    There are at least 21 subfunctional classes of tRNAs in most cells that, despite a very highly conserved and compact common structure, must interact specifically with different cliques of proteins or cause grave organismal consequences. Protein recognition of specific tRNA substrates is achieved in part through class-restricted tRNA features called tRNA identity determinants. In earlier work we used TFAM, a statistical classifier of tRNA function, to show evidence of unexpectedly large diversity among bacteria in tRNA identity determinants. We also created a data reduction technique called function logos to visualize identity determinants for a given taxon. Here we show evidence that determinants for lysylated isoleucine tRNAs are not the same in Proteobacteria as in other bacterial groups including the Cyanobacteria. Consistent with this, the lysylating biosynthetic enzyme TilS lacks a C-terminal domain in Cyanobacteria that is present in Proteobacteria. We present here, using function logos, a map estimating all potential identity determinants generally operational in Cyanobacteria and Proteobacteria. To further isolate the differences in potential tRNA identity determinants between Proteobacteria and Cyanobacteria, we created two new data reduction visualizations to contrast sequence and function logos between two taxa. One, called Information Difference logos (ID logos), shows the evolutionary gain or retention of functional information associated to features in one lineage. The other, Kullback–Leibler divergence Difference logos (KLD logos), shows recruitments or shifts in the functional associations of features, especially those informative in both lineages. We used these new logos to specifically isolate and visualize the differences in potential tRNA identity determinants between Proteobacteria and Cyanobacteria. Our graphical results point to numerous differences in potential tRNA identity determinants between these groups. Although more differences in general are explained by shifts in functional association rather than gains or losses, the apparent identity differences in lysylated isoleucine tRNAs appear to have evolved through both mechanisms.

  • 71.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Edvardsson, Sverker Edvardsson
    Tamas, Ivica Tamas
    Moulton, Vincent
    Poole, Anthony M.
    Fisher: a program for the detection of H/ACA snoRNAs using MFE secondary structure prediction and comparative genomics -- assessment and update.2008In: BMC Research Notes 2008, Vol. 1, no 49Article in journal (Other (popular scientific, debate etc.))
    Abstract [en]

    Background

    The H/ACA family of small nucleolar RNAs (snoRNAs) plays a central role in guiding the pseudouridylation of ribosomal RNA (rRNA). In an effort to systematically identify the complete set of rRNA-modifying H/ACA snoRNAs from the genome sequence of the budding yeast, Saccharomyces cerevisiae, we developed a program -- Fisher -- and previously presented several candidate snoRNAs based on our analysis [1].

    Findings

    In this report, we provide a brief update of this work, which was aborted after the publication of experimentally-identified snoRNAs [2] identical to candidates we had identified bioinformatically using Fisher. Our motivation for revisiting this work is to report on the status of the candidate snoRNAs described in [1], and secondly, to report that a modified version of Fisher together with the available multiple yeast genome sequences was able to correctly identify several H/ACA snoRNAs for modification sites not identified by the snoGPS program [3]. While we are no longer developing Fisher, we briefly consider the merits of the Fisher algorithm relative to snoGPS, which may be of use for workers considering pursuing a similar search strategy for the identification of small RNAs. The modified source code for Fisher is made available as supplementary material.

    Conclusions

    Our results confirm the validity of using minimum free energy (MFE) secondary structure prediction to guide comparative genomic screening for RNA families with few sequence constraints.

  • 72.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Gardner, Paul P
    Moulton, Vincent
    A comparison of RNA folding measures2005In: BMC Bioinformatics, ISSN 1471-2105, Vol. 6, p. 241-Article in journal (Refereed)
  • 73.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Gardner, Paul P
    Moulton, Vincent
    A comparison of RNA folding measures.2005In: BMC Bioinformatics, ISSN 1471-2105, Vol. 6, no 1, p. 241-Article in journal (Refereed)
  • 74.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Ardell, David
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Visualizing bacterial tRNA identity determinants and antideterminants using function logos and inverse function logos.2006In: Nucleic Acids Research, ISSN 1362-4962, Vol. 34, no 3, p. 905-916Article in journal (Refereed)
  • 75.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Ardell, David H
    Visualizing bacterial tRNA identity determinants and antideterminants using function logos and inverse function logos2006In: Nucleic Acids Research, ISSN 0305-1048, Vol. 34, no 3, p. 905-916Article in journal (Refereed)
  • 76.
    Freyhult, Eva
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Clote, Peter
    Boltzmann probability of RNA structural neighbors and riboswitch detection2007In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 23, no 16, p. 2054-2062Article in journal (Refereed)
    Abstract [en]

    Motivation: We describe algorithms implemented in a new software package, RNAbor, to investigate structures in a neighborhood of an input secondary structure of an RNA sequence s. The input structure could be the minimum free energy structure, the secondary structure obtained by analysis of the X-ray structure or by comparative sequence analysis, or an arbitrary intermediate structure.

    Results: A secondary structure of s is called a -neighbor of if and differ by exactly base pairs. RNAbor computes the number (N), the Boltzmann partition function (Z) and the minimum free energy (MFE) and corresponding structure over the collection of all -neighbors of . This computation is done simultaneously for all m, in run time O (mn3) and memory O(mn2), where n is the sequence length. We apply RNAbor for the detection of possible RNA conformational switches, and compare RNAbor with the switch detection method paRNAss. We also provide examples of how RNAbor can at times improve the accuracy of secondary structure prediction.

  • 77.
    Freyhult, Eva
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Clote, Peter
    RNAbor: a web server for RNA structural neighbors2007In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 35, no Suppl. S: Web Server issue, p. W305-W309Article in journal (Refereed)
    Abstract [en]

    RNAbor provides a new tool for researchers in the biological and related sciences to explore important aspects of RNA secondary structure and folding pathways. RNAbor computes statistics concerning delta-neighbors of a given input RNA sequence and structure (the structure can, for example, be the minimum free energy (MFE) structure). A delta-neighbor is a structure that differs from the input structure by exactly delta base pairs, that is, it can be obtained from the input structure by adding and/or removing exactly d base pairs. For each distance delta RNAbor computes the density of delta-neighbors, the number of delta-neighbors, and the MFE structure, or MFEd structure, among all delta-neighbors. RNAbor can be used to study possible folding pathways, to determine alternate low-energy structures, to predict potential nucleation sites and to explore structural neighbors of an intermediate, biologically active structure. The web server is available at http://bioinformatics.bc.edu/clotelab/RNAbor.

  • 78.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Gardner, Paul
    Predicting RNA structure using mutual information.2005In: Applied Bioinformatics, ISSN 1175-5636, Vol. 4, no 1, p. 53-59Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure is often conserved in evolution, the well known, but underused, mutual information measure for identifying covarying sites in an alignment can be useful for identifying structural elements. This article presents MIfold, a MATLAB((R)) toolbox that employs mutual information, or a related covariation measure, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. RESULTS: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall performance of MIfold improves with the number of aligned sequences for certain types of RNA sequences. In addition, we show that, for these sequences, MIfold is more sensitive but less selective than the related RNAalifold structure prediction program and is comparable with the COVE structure prediction package. CONCLUSION: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. AVAILABILITY: MIfold is freely available from http://www.lcb.uu.se/~evaf/MIfold/

  • 79.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Gardner, Paul P
    Predicting RNA structure using mutual information2005In: Applied Bioinformatics, ISSN 1175-5636, Vol. 4, no 1, p. 53-59Article in journal (Refereed)
  • 80.
    Gardner, P.
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics. Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics. BIOINFORMATIK.
    Holland, B.
    Moulton, V.
    Hendy, M.
    Penny, D.
    Optimal alphabets for an RNA world2003In: The Royal Society Prooceedings: Biological Sciences, Vol. 270, p. 1177-1182Article in journal (Refereed)
  • 81. Giuffra, Elisabetta
    et al.
    Törnsten, Anna
    Marklund, Stefan
    Bongcam-Rudloff, Erik
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Chardon, Patrick
    Kijas, James M H
    Anderson, Susan I
    Archibald, Alan L
    Andersson, Leif
    Medicinska vetenskapsområdet, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    A large duplication associated with dominant white color in pigs originated2002In: Mamm Genome, ISSN 0938-8990, Vol. 13, no 10, p. 569-77Article in journal (Other scientific)
  • 82. Gjuvsland, Arne B.
    et al.
    Hayes, Ben J.
    Omholt, Stig W.
    Carlborg, Örjan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Statistical epistasis is a generic feature of gene regulatory networks2007In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 175, no 1, p. 411-420Article in journal (Refereed)
    Abstract [en]

    Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.

  • 83. Gloriam, David E.
    et al.
    Orchard, Sandra
    Bertinetti, Daniela
    Björling, Erik
    Bongcam-Rudloff, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Borrebaeck, Carl A. K.
    Bourbeillon, Julie
    Bradbury, Andrew R. M.
    de Daruvar, Antoine
    Duebel, Stefan
    Frank, Ronald
    Gibson, Toby J.
    Gold, Larry
    Haslam, Niall
    Herberg, Friedrich W.
    Hiltke, Tara
    Hoheisel, Joerg D.
    Kerrien, Samuel
    Koegl, Manfred
    Konthur, Zoltan
    Korn, Bernhard
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Montecchi-Palazzi, Luisa
    Palcy, Sandrine
    Rodriguez, Henry
    Schweinsberg, Sonja
    Sievert, Volker
    Stoevesandt, Oda
    Taussig, Michael J.
    Ueffing, Marius
    Uhlén, Mathias
    van der Maarel, Silvere
    Wingren, Christer
    Woollard, Peter
    Sherman, David J.
    Hermjakob, Henning
    A Community Standard Format for the Representation of Protein Affinity Reagents2010In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 9, no 1, p. 1-10Article in journal (Refereed)
    Abstract [en]

    Protein affinity reagents (PARs), most commonly antibodies, are essential reagents for protein characterization in basic research, biotechnology, and diagnostics as well as the fastest growing class of therapeutics. Large numbers of PARs are available commercially; however, their quality is often uncertain. In addition, currently available PARs cover only a fraction of the human proteome, and their cost is prohibitive for proteome scale applications. This situation has triggered several initiatives involving large scale generation and validation of antibodies, for example the Swedish Human Protein Atlas and the German Antibody Factory. Antibodies targeting specific subproteomes are being pursued by members of Human Proteome Organisation (plasma and liver proteome projects) and the United States National Cancer Institute (cancer-associated antigens). ProteomeBinders, a European consortium, aims to set up a resource of consistently quality-controlled protein-binding reagents for the whole human proteome. An ultimate PAR database resource would allow consumers to visit one online warehouse and find all available affinity reagents from different providers together with documentation that facilitates easy comparison of their cost and quality. However, in contrast to, for example, nucleotide databases among which data are synchronized between the major data providers, current PAR producers, quality control centers, and commercial companies all use incompatible formats, hindering data exchange. Here we propose Proteomics Standards Initiative (PSI)-PAR as a global community standard format for the representation and exchange of protein affinity reagent data. The PSI-PAR format is maintained by the Human Proteome Organisation PSI and was developed within the context of ProteomeBinders by building on a mature proteomics standard format, PSI-molecular interaction, which is a widely accepted and established community standard for molecular interaction data. Further information and documentation are available on the PSI-PAR web site.

  • 84.
    Gustafsson, Mats G.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology.
    Wallman, Mikael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Wickenberg-Bolin, Ulrika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Göransson, Hanna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology.
    Andersson, Claes R.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology.
    Isaksson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors2010In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 49, no 2, p. 93-104Article in journal (Refereed)
    Abstract [en]

    Objective:

    Successful use of classifiers that learn to make decisions from a set of patient examples require robust methods for performance estimation. Recently many promising approaches for determination of an upper bound for the error rate of a single classifier have been reported but the Bayesian credibility interval (Cl) obtained from a conventional holdout test still delivers one of the tightest bounds. The conventional Bayesian CI becomes unacceptably large in real world applications where the test set sizes are less than a few hundred. The source of this problem is that fact that the Cl is determined exclusively by the result on the test examples. In other words, there is no information at all provided by the uniform prior density distribution employed which reflects complete lack of prior knowledge about the unknown error rate. Therefore, the aim of the study reported here was to study a maximum entropy (ME) based approach to improved prior knowledge and Bayesian CIs, demonstrating its relevance for biomedical research and clinical practice.

    Method and material:

    It is demonstrated how a refined non-uniform prior density distribution can be obtained by means of the ME principle using empirical results from a few designs and tests using non-overlapping sets of examples.

    Results:

    Experimental results show that ME based priors improve the CIs when employed to four quite different simulated and two real world data sets.

    Conclusions:

    An empirically derived ME prior seems promising for improving the Bayesian Cl for the unknown error rate of a designed classifier.

  • 85.
    Helgesson, Gert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Eriksson, Stefan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Centre for Research Ethics and Bioethics.
    Four Themes in Recent Swedish Bioethics Debates2011In: Cambridge Quarterly of Healthcare Ethics, ISSN 0963-1801, E-ISSN 1469-2147, Vol. 20, no 3, p. 409-417Article in journal (Refereed)
    Abstract [en]

    A wide variety of bioethical themes have recently been debated and researched in Sweden, including genetic screening, HPV vaccination strategies, end-of-life care, injustices and priority setting in healthcare, dual-use research, and the never-ending story of scientific fraud. Also, there are some new events related to Swedish biobanking that might be of general interest. Here we will concentrate on four themes: end-of-life care, dual-use research, scientific fraud, and biobanking.

  • 86. Holland, B R
    et al.
    Huber, K T
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Dress, A
    Moulton, V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Delta plots: a tool for analyzing phylogenetic distance data.2002In: Mol Biol Evol, ISSN 0737-4038, Vol. 19, no 12, p. 2051-9Article in journal (Other scientific)
  • 87. Holland, B R
    et al.
    Huber, K T
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Penny, D
    Moulton, V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    The MinMax Squeeze: guaranteeing a minimal tree for population data.2005In: Mol Biol Evol, ISSN 0737-4038, Vol. 22, no 2, p. 235-42Article in journal (Other scientific)
  • 88. Holland, Barbara R
    et al.
    Huber, Katharina T
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Lockhart, Peter J
    Using consensus networks to visualize contradictory evidence for species phylogeny.2004In: Mol Biol Evol, ISSN 0737-4038, Vol. 21, no 7, p. 1459-61Article in journal (Other scientific)
  • 89. Huber, K. T.
    et al.
    Moulton, V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    The relation graph2002In: Discrete Mathematics, Vol. 244, no 1-3, p. 153-166Article in journal (Refereed)
    Abstract [en]

    Given a set R of distinct, non-trivial partitions of a finite set, we define the relation graph G(R) of R. In case R consists only of bipartitions, G(R) is the well-known Buneman graph, a median graph that has applications in the area of phylogenetic analysis., Here we consider properties of the relation graph for general sets of partitions and, in particular, we see that it mimics the behaviour of the Buneman graph by proving the following two theorems:

    (i) The graph G(R) is a Hamming graph if and only if R is strongly incompatible.

    (ii) The graph G(R) is a block graph with #R blocks if and only if R is strongly compatible.

  • 90.
    Huber, Katharina T
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Langton, Michael
    Penny, David
    Moulton, Vincent
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Hendy, Michael
    Spectronet: a package for computing spectra and median networks.2002In: Appl Bioinformatics, ISSN 1175-5636, Vol. 1, no 3, p. 159-61Article in journal (Other scientific)
  • 91.
    Huber, KT
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Recovering trees from well-separated multi-state characters2004In: Discrete Mathematics, Vol. 278, no 1-3, p. 151-164Article in journal (Refereed)
    Abstract [en]

    Recently, by studying Z(5)-edge colorings of bifurcating phylogenetic trees, Semple and Steel showed that every such tree can be convexly defined by at most five characters. The investigation of the rich structure of such edge colorings led us to the definition of a set of well-separated characters on a phylogenetic tree T that covers T which we study here. In particular, we show that such a set W of characters convexly defines a bifurcating phylogenetic tree T and that, provided this cover is sparse, the so called relation graph associated to W coincides with T. As a consequence of our results, it follows that T can be reconstructed. from W in polynomial time.

  • 92.
    Huber, KT
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Koolen, JH
    Moulton, V
    The tight span of an antipodal metric space: Part II - Geometrical properties2004In: Discrete & Computational Geometry, ISSN 0179-5376, Vol. 31, no 4, p. 567-586Article in journal (Refereed)
    Abstract [en]

    Suppose that X is a finite set and let R-x denote the set of functions that map X to R. Given a metric d on X, the tight span of (X, d) is the polyhedral complex T (X, d) that consists of the bounded faces of the polyhedron

    P(X, d) := {f is an element of R-x : f(x) + f (y) greater than or equal to d(x, y)}.

    In a previous paper we commenced a study of properties of T(X, d) when d is antipodal, that is, there exists an involution sigma : X --> X: x --> (x) over bar so that d(x, y) + d(y,(x) over bar) = d(x, (x) over bar) holds for all x, y c X. Here we continue our study, considering geometrical properties of the tight span of an antipodal metric space that arise from a metric with which the tight span comes naturally equipped. In particular, we introduce the concept of cell-decomposability for a metric and prove that the tight span of such a metric is the union of cells, each of which is isometric and polytope isomorphic to the tight span of some antipodal metric. In addition, we classify the antipodal cell-decomposable metrics and give a description of the polytopal structure of the tight span of such a metric.

  • 93.
    Huber, KT
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Semple, C
    Replacing cliques by stars in quasi-median graphs2004In: Discrete Applied Mathematics, Vol. 143, no 1-3, p. 194-203Article in journal (Refereed)
    Abstract [en]

    For a multi-set Sigma of splits (bipartitions) of a finite set X, we introduce the multi-split graph G(Sigma). This graph is a natural extension of the Buneman graph. Indeed, it is shown that several results pertaining to the Buneman graph extend to the multi-split graph. In addition, in case Sigma is derived from a set R of partitions of X by taking parts together with their complements, we show that the extremal instances where R is either strongly compatible or strongly incompatible are equivalent to G(Sigma) being either a tree or a Cartesian product of star trees, respectively.

  • 94.
    Hvidsten, T. R., Kryshtafovych, A., Komorowski, J. and Fidelis, K.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    A novel approach to fold recognition using sequence-derived properties from sets of structurally similar local fragments of proteins2003In: Bioinformatics, Vol. 19 (suppl 2), p. II81-II91Article in journal (Refereed)
  • 95.
    Hvidsten, T R
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Komorowski, J
    Sandvik, A K
    Laegreid, A
    Predicting gene function from gene expressions and ontologies.2001In: Pacific Symposium on Biocomputing, 2001, p. 299-310Conference paper (Refereed)
  • 96.
    Hvidsten, Torgeir R.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Predicting Function of Genes and Proteins from Sequence, Structure and Expression Data2004Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Functional genomics refers to the task of determining gene and protein function for whole genomes, and requires computational analysis of large amounts of biological data including DNA and protein sequences, protein structures and gene expressions. Machine learning methods provide a powerful tool to this end by first inducing general models from such data and already characterized genes or proteins and then by providing hypotheses on the functions of the remaining, uncharacterized cases.

    This study contains four parts giving novel contributions to functional genomics through the analysis of different biological data and different aspects of biological functions. Gene Ontology played an important part in this research providing a controlled vocabulary for describing the cellular roles of genes and proteins in terms of specific molecular functions and broad biological processes.

    The first part used gene expression time profiles to learn models capable of predicting the participation of genes in biological processes. The model consists of IF-THEN rules associating biological processes with minimal set of discrete changes in expression level over limited periods of time. The models were used to hypothesize new biological processes for both characterized and uncharacterized genes.

    The second part investigated the combinatorial nature of gene regulation by inducing IF-THEN rules associating minimal combinations of sequence motifs common to genes with similar expression profiles. Such combinations were shown to be significantly correlated to function, and provided hypotheses on the mechanisms behind the regulation of gene expression in several biological responses.

    The third part used a novel concept of local descriptors of protein structure to investigate sequence patterns governing protein structure at a local level and to predict the topological class (fold) of protein domains from sequence. Finally, the fourth part used local descriptors to represent protein structure and induced IF-THEN rule models predicting molecular function from structure.

  • 97.
    Hvidsten, Torgeir R.
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Komorowski, Jan
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Rough sets in bioinformatics2007In: Transactions on Rough Sets VII: Lecture Notes in Computer Science 4400, p. 225-243Article, review/survey (Other (popular scientific, debate etc.))
  • 98.
    Hvidsten, Torgeir R.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Kryshtafovych, Andriy
    Fidelis, Krzysztof
    Local descriptors of protein structure: A systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions2009In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 75, no 4, p. 870-884Article in journal (Refereed)
    Abstract [en]

    Local protein structure representations that incorporate long-range contacts between residues are often considered in protein structure comparison but have found relatively little use in structure prediction where assembly from single backbone fragments dominates. Here, we introduce the concept of local descriptors of protein structure to characterize local neighborhoods of amino acids including short- and long-range interactions. We build a library of recurring local descriptors and show that this library is general enough to allow assembly of unseen protein structures. The library could on average re-assemble 83% of 119 unseen structures, and showed little or no performance decrease between homologous targets and targets with folds not represented among domains used to build it. We then systematically evaluate the descriptor library to establish the level of the sequence signal in sets of protein fragments of similar geometrical conformation. In particular, we test whether that signal is strong enough to facilitate correct assignment and alignment of these local geometries to new sequences. We use the signal to assign descriptors to a test set of 479 sequences with less than 40% sequence identity to any domain used to build the library, and show that on average more than 50% of the backbone fragments constituting descriptors can be correctly aligned. We also use the assigned descriptors to infer SCOP folds, and show that correct predictions can be made in many of the 151 cases where PSI-BLAST was unable to detect significant sequence similarity to proteins in the library. Although the combinatorial problem of simultaneously aligning several fragments to sequence is a major bottleneck compared with single is that correct alignments imply correct long range distance constraints. The lack of these constraints is most likely the major reason why structure prediction methods fail to consistently produce adequate models when good templates are unavailable or undetectable. Thus, we believe that the current study offers new and valuable insight into the prediction of sequence-structure relationships in proteins.

  • 99.
    Hvidsten, Torgeir R
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Lægreid, Astrid
    Kryshtafovych, Andriy
    Andersson, Gunnar
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Fidelis, Krzysztof
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity2009In: PLoS ONE, ISSN 1932-6203, Vol. 4, no 7, p. e6266-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Sequence similarity to characterized proteins provides testable functional hypotheses for less than 50% of the proteins identified by genome sequencing projects. With structural genomics it is believed that structural similarities may give functional hypotheses for many of the remaining proteins. METHODOLOGY/PRINCIPAL FINDINGS: We provide a systematic analysis of the structure-function relationship in proteins using the novel concept of local descriptors of protein structure. A local descriptor is a small substructure of a protein which includes both short- and long-range interactions. We employ a library of commonly reoccurring local descriptors general enough to assemble most existing protein structures. We then model the relationship between these local shapes and Gene Ontology using rule-based learning. Our IF-THEN rule model offers legible, high resolution descriptions that combine local substructures and is able to discriminate functions even for functionally versatile folds such as the frequently occurring TIM barrel and Rossmann fold. By evaluating the predictive performance of the model, we provide a comprehensive quantification of the structure-function relationship based only on local structure similarity. Our findings are, among others, that conserved structure is a stronger prerequisite for enzymatic activity than for binding specificity, and that structure-based predictions complement sequence-based predictions. The model is capable of generating correct hypotheses, as confirmed by a literature study, even when no significant sequence similarity to characterized proteins exists. CONCLUSIONS/SIGNIFICANCE: Our approach offers a new and complete description and quantification of the structure-function relationship in proteins. By demonstrating how our predictions offer higher sensitivity than using global structure, and complement the use of sequence, we show that the presented ideas could advance the development of meta-servers in function prediction.

  • 100.
    Hvidsten, Torgeir R
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Wilczyński, Bartosz
    Kryshtafovych, Andriy
    Tiuryn, Jerzy
    Komorowski, Jan
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Fidelis, Krzysztof
    Discovering regulatory binding-site modules using rule-based learning.2005In: Genome Res, ISSN 1088-9051, Vol. 15, no 6, p. 856-66Article in journal (Refereed)
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