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  • 1.
    Aguileta, Gabriela
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bielawski, Joseph P.
    Yang, Ziheng
    Proposed standard nomenclature for the alpha- and beta-globin gene families2006In: Genes & Genetic Systems, ISSN 1341-7568, E-ISSN 1880-5779, Vol. 81, no 5, p. 367-371Article in journal (Refereed)
    Abstract [en]

    The globin family of genes and proteins has been a recurrent object of study for many decades. This interest has generated a vast amount of knowledge. However it has also created an inconsistent and confusing nomenclature, due to the lack of a systematic approach to naming genes and failure to reflect the phylogenetic relationships among genes of the gene family. To alleviate the problems with the existing system, here we propose a standardized nomenclature for the alpha and beta globin family of genes, based on a phylogenetic analysis of vertebrate alpha and beta globins, and following the Guidelines for Human Gene Nomenclature.

  • 2.
    Alvarez-Castro, Jose M.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Le Rouzic, Arnaud
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Carlborg, Örjan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    How to perform meaningful estimates of genetic effects2008In: PLoS Genetics, ISSN 1553-7390, Vol. 4, no 5, p. e1000062-Article in journal (Refereed)
    Abstract [en]

    Although the genotype-phenotype map plays a central role both in Quantitative and Evolutionary Genetics, the formalization of a completely general and satisfactory model of genetic effects, particularly accounting for epistasis, remains a theoretical challenge. Here, we use a two-locus genetic system in simulated populations with epistasis to show the convenience of using a recently developed model, NOIA, to perform estimates of genetic effects and the decomposition of the genetic variance that are orthogonal even under deviations from the Hardy-Weinberg proportions. We develop the theory for how to use this model in interval mapping of quantitative trait loci using Halley-Knott regressions, and we analyze a real data set to illustrate the advantage of using this approach in practice. In this example, we show that departures from the Hardy-Weinberg proportions that are expected by sampling alone substantially alter the orthogonal estimates of genetic effects when other statistical models, like F-2 or G2A, are used instead of NOIA. Finally, for the first time from real data, we provide estimates of functional genetic effects as sets of effects of natural allele substitutions in a particular genotype, which enriches the debate on the interpretation of genetic effects as implemented both in functional and in statistical models. We also discuss further implementations leading to a completely general genotype-phenotype map.

  • 3.
    Ameur, Adam
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    A Bioinformatics Study of Human Transcriptional Regulation2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Regulation of transcription is a central mechanism in all living cells that now can be investigated with high-throughput technologies. Data produced from such experiments give new insights to how transcription factors (TFs) coordinate the gene transcription and thereby regulate the amounts of proteins produced. These studies are also important from a medical perspective since TF proteins are often involved in disease. To learn more about transcriptional regulation, we have developed strategies for analysis of data from microarray and massively parallel sequencing (MPS) experiments.

    Our computational results consist of methods to handle the steadily increasing amount of data from high-throughput technologies. Microarray data analysis tools have been assembled in the LCB-Data Warehouse (LCB-DWH) (paper I), and other analysis strategies have been developed for MPS data (paper V). We have also developed a de novo motif search algorithm called BCRANK (paper IV).

    The analysis has lead to interesting biological findings in human liver cells (papers II-V). The investigated TFs appeared to bind at several thousand sites in the genome, that we have identified at base pair resolution. The investigated histone modifications are mainly found downstream of transcription start sites, and correlated to transcriptional activity. These histone marks are frequently found for pairs of genes in a bidirectional conformation. Our results suggest that a TF can bind in the shared promoter of two genes and regulate both of them.

    From a medical perspective, the genes bound by the investigated TFs are candidates to be involved in metabolic disorders. Moreover, we have developed a new strategy to detect single nucleotide polymorphisms (SNPs) that disrupt the binding of a TF (paper IV). We further demonstrated that SNPs can affect transcription in the immediate vicinity. Ultimately, our method may prove helpful to find disease-causing regulatory SNPs.

    List of papers
    1. The LCB Data Warehouse
    Open this publication in new window or tab >>The LCB Data Warehouse
    Show others...
    2006 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 22, no 8, p. 1024-1026Article in journal (Refereed) Published
    Abstract [en]

    The Linnaeus Centre for Bioinformatics Data Warehouse (LCB-DWH) is a web-based infrastructure for reliable and secure microarray gene expression data management and analysis that provides an online service for the scientific community. The LCB-DWH is an effort towards a complete system for storage (using the BASE system), analysis and publication of microarray data. Important features of the system include: access to established methods within R/Bioconductor for data analysis, built-in connection to the Gene Ontology database and a scripting facility for automatic recording and re-play of all the steps of the analysis. The service is up and running on a high performance server. At present there are more than 150 registered users.

    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:uu:diva-97704 (URN)10.1093/bioinformatics/btl036 (DOI)16455749 (PubMedID)
    Available from: 2008-11-06 Created: 2008-11-06 Last updated: 2017-12-14Bibliographically approved
    2. Binding sites for metabolic disease related transcription factors inferred at base pair resolution by chromatin immunoprecipitation and genomic microarrays
    Open this publication in new window or tab >>Binding sites for metabolic disease related transcription factors inferred at base pair resolution by chromatin immunoprecipitation and genomic microarrays
    Show others...
    2005 (English)In: Human Molecular Genetics, ISSN 0964-6906, E-ISSN 1460-2083, Vol. 14, no 22, p. 3435-3447Article in journal (Refereed) Published
    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.

    Keywords
    Base Pairing/*genetics, Binding Sites/genetics, Cell Line; Tumor, Chromatin/*metabolism, Chromatin Immunoprecipitation/methods, Consensus Sequence, Genome; Human, Hepatocyte Nuclear Factor 3-beta/physiology, Hepatocyte Nuclear Factor 4/physiology, Hepatocytes/metabolism, Histones/metabolism, Humans, Metabolic Diseases/*metabolism, Oligonucleotide Array Sequence Analysis/methods, Promoter Regions (Genetics), Research Support; N.I.H.; Extramural, Research Support; Non-U.S. Gov't, Sequence Analysis; DNA, Transcription Factors/genetics/*metabolism, Upstream Stimulatory Factors/metabolism
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-80603 (URN)10.1093/hmg/ddi378 (DOI)16221759 (PubMedID)
    Available from: 2006-05-19 Created: 2006-05-19 Last updated: 2017-12-14Bibliographically approved
    3. Whole-genome maps of USF1 and USF2 binding and histone H3 acetylation reveal new aspects of promoter structure and candidate genes for common human disorders
    Open this publication in new window or tab >>Whole-genome maps of USF1 and USF2 binding and histone H3 acetylation reveal new aspects of promoter structure and candidate genes for common human disorders
    Show others...
    2008 (English)In: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 18, no 3, p. 380-392Article in journal (Refereed) Published
    Abstract [en]

    Transcription factors and histone modifications are crucial regulators of gene expression that mutually influence each other. We present the DNA binding profiles of upstream stimulatory factors 1 and 2 (USF1, USF2) and acetylated histone H3 (H3ac) in a liver cell line for the whole human genome using ChIP-chip at a resolution of 35 base pairs. We determined that these three proteins bind mostly in proximity of protein coding genes transcription start sites (TSSs), and their bindings are positively correlated with gene expression levels. Based on the spatial and functional relationship between USFs and H3ac at protein coding gene promoters, we found similar promoter architecture for known genes and the novel and less-characterized transcripts human mRNAs and spliced ESTs. Furthermore, our analysis revealed a previously underestimated abundance of genes in a bidirectional conformation, where USFs are bound in between TSSs. After taking into account this promoter conformation, the results indicate that H3ac is mainly located downstream of TSS, and it is at this genomic location where it positively correlates with gene expression. Finally, USF1, which is associated to familial combined hyperlipidemia, was found to bind and potentially regulate nuclear mitochondrial genes as well as genes for lipid and cholesterol metabolism, frequently in collaboration with GA binding protein transcription factor alpha (GABPA, nuclear respiratory factor 2 [NRF-2]). This expands our understanding about the transcriptional control of metabolic processes and its alteration in metabolic disorders.

    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:uu:diva-97706 (URN)10.1101/gr.6880908 (DOI)000253766700004 ()18230803 (PubMedID)
    Available from: 2008-11-06 Created: 2008-11-06 Last updated: 2017-12-14Bibliographically approved
    4. New algorithm and ChIP-analysis identifies candidate functional SNPs
    Open this publication in new window or tab >>New algorithm and ChIP-analysis identifies candidate functional SNPs
    Show others...
    In: PNASArticle in journal (Refereed) Submitted
    Identifiers
    urn:nbn:se:uu:diva-97707 (URN)
    Available from: 2008-11-06 Created: 2008-11-06Bibliographically approved
    5. Differential binding and co-binding pattern of FOXA1 and FOXA3 and their relation to H3K4me3 in HepG2 cells revealed by ChIP-seq
    Open this publication in new window or tab >>Differential binding and co-binding pattern of FOXA1 and FOXA3 and their relation to H3K4me3 in HepG2 cells revealed by ChIP-seq
    Show others...
    2009 (English)In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 10, no 11, p. R129-Article in journal (Refereed) Published
    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.

    National Category
    Medical and Health Sciences Biological Sciences
    Identifiers
    urn:nbn:se:uu:diva-119751 (URN)10.1186/gb-2009-10-11-r129 (DOI)000273344600016 ()19919681 (PubMedID)
    Note

    De två (2) första författarna delar förstaförfattarskapet.

    Available from: 2010-03-01 Created: 2010-03-01 Last updated: 2017-12-12Bibliographically approved
  • 4.
    Ameur, Adam
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Rada-Iglesias, Alvaro
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Komorowski, Jan
    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.
    Identification of candidate regulatory SNPs by combination of transcription-factor-binding site prediction, SNP genotyping and haploChIP2009In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 37, no 12, p. e85-Article in journal (Refereed)
    Abstract [en]

    Disease-associated SNPs detected in large-scale association studies are   frequently located in non-coding genomic regions, suggesting that they may be involved in transcriptional regulation. Here we describe a new strategy for detecting regulatory SNPs (rSNPs), by combining   computational and experimental approaches. Whole genome ChIP-chip data   for USF1 was analyzed using a novel motif finding algorithm called   BCRANK. 1754 binding sites were identified and 140 candidate rSNPs were   found in the predicted sites. For validating their regulatory function,   seven SNPs found to be heterozygous in at least one of four human cell   samples were investigated by ChIP and sequence analysis (haploChIP). In   four of five cases where the SNP was predicted to affect binding, USF1   was preferentially bound to the allele containing the consensus motif.   Allelic differences in binding for other proteins and histone marks   further reinforced the SNPs regulatory potential. Moreover, for one of   these SNPs, H3K36me3 and POLR2A levels at neighboring heterozygous SNPs   indicated effects on transcription. Our strategy, which is entirely   based on in vivo data for both the prediction and validation steps, can   identify individual binding sites at base pair resolution and predict   rSNPs. Overall, this approach can help to pinpoint the causative SNPs   in complex disorders where the associated haplotypes are located in regulatory regions. Availability: BCRANK is available from Bioconductor  (http://www.bioconductor.org).

  • 5.
    Ameur, Adam
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Rada-Iglesias, Alvaro
    Komorowski, Jan
    Wadelius, Claes
    Ameur, Adam
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    New algorithm and ChIP-analysis identifies candidate functional SNPsIn: PNASArticle in journal (Refereed)
  • 6.
    Ameur, Adam
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Yankovski, Vladimir
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Enroth, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    The LCB Data Warehouse2006In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 22, no 8, p. 1024-1026Article in journal (Refereed)
    Abstract [en]

    The Linnaeus Centre for Bioinformatics Data Warehouse (LCB-DWH) is a web-based infrastructure for reliable and secure microarray gene expression data management and analysis that provides an online service for the scientific community. The LCB-DWH is an effort towards a complete system for storage (using the BASE system), analysis and publication of microarray data. Important features of the system include: access to established methods within R/Bioconductor for data analysis, built-in connection to the Gene Ontology database and a scripting facility for automatic recording and re-play of all the steps of the analysis. The service is up and running on a high performance server. At present there are more than 150 registered users.

  • 7. Anderson, Frank E.
    et al.
    Córdoba, Alonso J.
    Thollesson, Mikael
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Evolution, Genomics and Systematics, Molecular Evolution. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bilaterian phylogeny based on analyses of a region of the sodium-potassium ATPase alpha-subunit gene2004In: Journal of Molecular Evolution, ISSN 0022-2844, E-ISSN 1432-1432, Vol. 58, no 3, p. 252-268Article in journal (Refereed)
    Abstract [en]

    Molecular investigations of deep-level relationships within and among the animal phyla have been hampered by a lack of slowly evolving genes that are amenable to study by molecular systematists. To provide new data for use in deep-level metazoan phylogenetic studies, primers were developed to amplify a 1.3-kb region of the subunit of the nuclear-encoded sodium–potassium ATPase gene from 31 bilaterians representing several phyla. Maximum parsimony, maximum likelihood, and Bayesian analyses of these sequences (combined with ATPase sequences for 23 taxa downloaded from GenBank) yield congruent trees that corroborate recent findings based on analyses of other data sets (e.g., the 18S ribosomal RNA gene). The ATPase-based trees support monophyly for several clades (including Lophotrochozoa, a form of Ecdysozoa, Vertebrata, Mollusca, Bivalvia, Gastropoda, Arachnida, Hexapoda, Coleoptera, and Diptera) but do not support monophyly for Deuterostomia, Arthropoda, or Nemertea. Parametric bootstrapping tests reject monophyly for Arthropoda and Nemertea but are unable to reject deuterostome monophyly. Overall, the sodium–potassium ATPase -subunit gene appears to be useful for deep-level studies of metazoan phylogeny.

  • 8.
    Andersson, Claes
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Fusing Domain Knowledge with Data: Applications in Bioinformatics2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.

    List of papers
    1. In vitro drug sensitivity-gene expression correlations involve a tissue of origin dependency
    Open this publication in new window or tab >>In vitro drug sensitivity-gene expression correlations involve a tissue of origin dependency
    Show others...
    2007 (English)In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 47, no 1, p. 239-248Article in journal (Refereed) Published
    Abstract [en]

    A major concern of chemogenomics is to associate drug activity with biological variables. Several reports have clustered cell line drug activity profiles as well as drug activity-gene expression correlation profiles and noted that the resulting groupings differ but still reflect mechanism of action. The present paper shows that these discrepancies can be viewed as a weighting of drug-drug distances, the weights depending on which cell lines the two drugs differ in.

    Keywords
    computers in chemistry, computer program
    National Category
    Medical and Health Sciences Signal Processing
    Research subject
    Electrical Engineering with specialization in Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-20891 (URN)10.1021/ci060073n (DOI)000243577400029 ()17238270 (PubMedID)
    Available from: 2007-10-28 Created: 2007-10-28 Last updated: 2016-09-25Bibliographically approved
    2. Bayesian detection of periodic mRNA time profiles withouth use of training examples
    Open this publication in new window or tab >>Bayesian detection of periodic mRNA time profiles withouth use of training examples
    2006 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 7, p. 63-Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time. RESULTS: A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented. CONCLUSION: In comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.

    National Category
    Medical and Health Sciences Engineering and Technology
    Identifiers
    urn:nbn:se:uu:diva-96785 (URN)10.1186/1471-2105-7-63 (DOI)16469110 (PubMedID)
    Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2017-12-14Bibliographically approved
    3. Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors
    Open this publication in new window or tab >>Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors
    Show others...
    2007 (English)In: BMC Systems Biology, ISSN 1752-0509, Vol. 1, p. 45-Article in journal (Refereed) Published
    Abstract [en]

    Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

    National Category
    Biological Sciences Signal Processing
    Research subject
    Electrical Engineering with specialization in Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-96786 (URN)10.1186/1752-0509-1-45 (DOI)000252363100001 ()17939860 (PubMedID)
    Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2016-09-25Bibliographically approved
    4. Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors
    Open this publication in new window or tab >>Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors
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    2010 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 49, no 2, p. 93-104Article in journal (Refereed) Published
    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.

    National Category
    Medical and Health Sciences Computer and Information Sciences
    Identifiers
    urn:nbn:se:uu:diva-96787 (URN)10.1016/j.artmed.2010.02.004 (DOI)000279172200003 ()
    Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2018-01-13
    5. Feature Selection using Classification of Unlabeled Data
    Open this publication in new window or tab >>Feature Selection using Classification of Unlabeled Data
    Manuscript (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-96788 (URN)
    Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2010-01-13Bibliographically approved
  • 9.
    Andersson, Claes R.
    et al.
    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.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Rickardson, Linda
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology.
    Larsson, Rolf
    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, Clinical Pharmacology.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    In vitro drug sensitivity-gene expression correlations involve a tissue of origin dependency2007In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 47, no 1, p. 239-248Article in journal (Refereed)
    Abstract [en]

    A major concern of chemogenomics is to associate drug activity with biological variables. Several reports have clustered cell line drug activity profiles as well as drug activity-gene expression correlation profiles and noted that the resulting groupings differ but still reflect mechanism of action. The present paper shows that these discrepancies can be viewed as a weighting of drug-drug distances, the weights depending on which cell lines the two drugs differ in.

  • 10.
    Andersson, Claes R.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Hvidsten, Torgeir R.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Isaksson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Pharmacology. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors2007In: BMC Systems Biology, ISSN 1752-0509, Vol. 1, p. 45-Article in journal (Refereed)
    Abstract [en]

    Background: We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results: We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion: The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes.

  • 11.
    Andersson, Claes R.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Isaksson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signal Processing.
    Bayesian detection of periodic mRNA time profiles withouth use of training examples2006In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 7, p. 63-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time. RESULTS: A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented. CONCLUSION: In comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.

  • 12.
    Andersson, Claes R.
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Larsson, Rolf
    Isaksson, Anders
    Gustafsson, Mats G.
    Feature Selection using Classification of Unlabeled DataManuscript (Other academic)
  • 13.
    Andersson, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Decoding the Structural Layer of Transcriptional Regulation: Computational Analyses of Chromatin and Chromosomal Aberrations2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Gene activity is regulated at two separate layers. Through structural and chemical properties of DNA – the primary layer of encoding – local signatures may enable, or disable, the binding of proteins or complexes of them with regulatory potential to the DNA. At a higher level – the structural layer of encoding – gene activity is regulated through the properties of higher order DNA structure, chromatin, and chromosome organization. Cells with abnormal chromosome compaction or organization, e.g. cancer cells, may thus have perturbed regulatory activities resulting in abnormal gene activity.

    Hence, there is a great need to decode the transcriptional regulation encoded in both layers to further our understanding of the factors that control activity and life of a cell and, ultimately, an organism. Modern genome-wide studies with those aims rely on data-intense experiments requiring sophisticated computational and statistical methods for data handling and analyses. This thesis describes recent advances of analyzing experimental data from quantitative biological studies to decipher the structural layer of encoding in human cells.

    Adopting an integrative approach when possible, combining multiple sources of data, allowed us to study the influences of chromatin (Papers I and II) and chromosomal aberrations (Paper IV) on transcription. Combining chromatin data with chromosomal aberration data allowed us to identify putative driver oncogenes and tumor-suppressor genes in cancer (Paper IV).

    Bayesian approaches enabling the incorporation of background information in the models and the adaptability of such models to data have been very useful. Their usages yielded accurate and narrow detection of chromosomal breakpoints in cancer (Papers III and IV) and reliable positioning of nucleosomes and their dynamics during transcriptional regulation at functionally relevant regulatory elements (Paper II).

    Using massively parallel sequencing data, we explored the chromatin landscapes of human cells (Papers I and II) and concluded that there is a preferential and evolutionary conserved positioning at internal exons nearly unaffected by the transcriptional level. We also observed a strong association between certain histone modifications and the inclusion or exclusion of an exon in the mature gene transcript, suggesting a functional role in splicing.

    List of papers
    1. 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
    2. Strand-based mixture modeling of nucleosome positioning in HepG2 cells and their regulatory dynamics in response to TGF-beta treatment
    Open this publication in new window or tab >>Strand-based mixture modeling of nucleosome positioning in HepG2 cells and their regulatory dynamics in response to TGF-beta treatment
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Identifiers
    urn:nbn:se:uu:diva-130998 (URN)
    Available from: 2010-09-20 Created: 2010-09-20 Last updated: 2010-11-11
    3. A Segmental Maximum A Posteriori Approach to Genome-wide Copy Number Profiling
    Open this publication in new window or tab >>A Segmental Maximum A Posteriori Approach to Genome-wide Copy Number Profiling
    Show others...
    2008 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 24, no 6, p. 751-758Article in journal (Other academic) Published
    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.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-13616 (URN)10.1093/bioinformatics/btn003 (DOI)000254010400003 ()18204059 (PubMedID)
    Available from: 2008-08-21 Created: 2008-08-21 Last updated: 2017-12-11Bibliographically approved
    4. Integrative epigenomic and genomic analysis of malignant pheochromocytoma
    Open this publication in new window or tab >>Integrative epigenomic and genomic analysis of malignant pheochromocytoma
    Show others...
    2010 (English)In: Experimental and Molecular Medicine, ISSN 1226-3613, E-ISSN 2092-6413, Vol. 42, no 7, p. 484-502Article in journal (Refereed) Published
    Abstract [en]

    Epigenomic and genomic changes affect gene expression and contribute to tumor development. The histone modifications trimethylated histone H3 lysine 4 (H3K4me3) and lysine 27 (H3K27me3) are epigenetic regulators associated to active and silenced genes, respectively and alterations of these modifications have been observed in cancer. Furthermore, genomic aberrations such as DNA copy number changes are common events in tumors. Pheochromocytoma is a rare endocrine tumor of the adrenal gland that mostly occurs sporadic with unknown epigenetic/genetic cause. The majority of cases are benign. Here we aimed to combine the genome-wide profiling of H3K4me3 and H3K27me3, obtained by the ChIP-chip methodology, and DNA copy number data with global gene expression examination in a malignant pheochromocytoma sample. The integrated analysis of the tumor expression levels, in relation to normal adrenal medulla, indicated that either histone modifications or chromosomal alterations, or both, have great impact on the expression of a substantial fraction of the genes in the investigated sample. Candidate tumor suppressor genes identified with decreased expression, a H3K27me3 mark and/or in regions of deletion were for instance TGIF1, DSC3, TNFRSF10B, RASSF2, HOXA9, PTPRE and CDH11. More genes were found with increased expression, a H3K4me3 mark, and/or in regions of gain. Potential oncogenes detected among those were GNAS, INSM1, DOK5, ETV1, RET, NTRK1, IGF2, and the H3K27 trimethylase gene EZH2. Our approach to associate histone methylations and DNA copy number changes to gene expression revealed apparent impact on global gene transcription, and enabled the identification of candidate tumor genes for further exploration.

    Keywords
    histone code, DNA copy number changes, gene expression, oncogenes, pheochromocytoma, tumor suppressor genes
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-129532 (URN)10.3858/emm.2010.42.7.050 (DOI)000280558100002 ()20534969 (PubMedID)
    Available from: 2010-08-18 Created: 2010-08-18 Last updated: 2017-12-12Bibliographically approved
  • 14.
    Andersson, Robin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Bruder, Carl E G
    Piotrowski, Arkadiusz
    Menzel, Uwe
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Nord, Helena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Sandgren, Johanna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
    Hvidsten, Torgeir R
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    de Ståhl, Teresita Diaz
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Dumanski, Jan P
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    A Segmental Maximum A Posteriori Approach to Genome-wide Copy Number Profiling2008In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 24, no 6, p. 751-758Article in journal (Other academic)
    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.

  • 15.
    Andersson, Robin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Enroth, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Barbacioru, Catalin
    Reddy Bysani, Madhu Sudhan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Wallerman, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Tuch, Brian
    Lee, Clarence
    Peckham, Heather
    McKernan, Kevin
    de la Vega, Francisco
    Komorowski, Jan
    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.
    Strand-based mixture modeling of nucleosome positioning in HepG2 cells and their regulatory dynamics in response to TGF-beta treatmentManuscript (preprint) (Other academic)
  • 16.
    Andersson, Robin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Enroth, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Rada-Iglesias, Alvaro
    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.
    Nucleosomes are well positioned in exons and carry characteristic histone modifications2009In: Genome Research, ISSN 1088-9051, E-ISSN 1549-5469, Vol. 19, no 10, p. 1732-1741Article in journal (Refereed)
    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.

  • 17.
    Andersson, Robin
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Vitoria, Aida
    Maluszynski, Jan
    Komorowski, Jan
    RoSy: A Rough Knowledge Base System2005In: 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, p. 48-58Conference paper (Refereed)
    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.

  • 18.
    Ardell, David H
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    SCANMS: adjusting for multiple comparisons in sliding window neutrality tests.2004In: Bioinformatics, ISSN 1367-4803, Vol. 20, no 12, p. 1986-8Article in journal (Other scientific)
  • 19.
    Ardell, David H.
    et al.
    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 Science and Technology, Biology, Department of Evolution, Genomics and Systematics, Molecular Evolution.
    Andersson, Siv G. E.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Evolution, Genomics and Systematics, Molecular Evolution.
    TFAM detects co-evolution of tRNA identity rules with lateral transfer of histidyl-tRNA sythetase2006In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 34, no 3, p. 893-904Article in journal (Refereed)
    Abstract [en]

    We present TFAM, an automated, statistical method to classify the identity of tRNAs. TFAM, currently optimized for bacteria, classifies initiator tRNAs and predicts the charging identity of both typical and atypical tRNAs such as suppressors with high confidence. We show statistical evidence for extensive variation in tRNA identity determinants among bacterial genomes due to variation in overall tDNA base content. With TFAM we have detected the first case of eukaryotic-like tRNA identity rules in bacteria. An alpha-proteobacterial clade encompassing Rhizobiales, Caulobacter crescentus and Silicibacter pomeroyi, unlike a sister clade containing the Rickettsiales, Zymomonas mobilis and Gluconobacter oxydans, uses the eukaryotic identity element A73 instead of the highly conserved prokaryotic element C73. We confirm divergence of bacterial histidylation rules by demonstrating perfect covariation of alpha-proteobacterial tRNA(His) acceptor stems and residues in the motif IIb tRNA-binding pocket of their histidyl-tRNA synthetases (HisRS). Phylogenomic analysis supports lateral transfer of a eukaryotic-like HisRS into the alpha-proteobacteria followed by in situ adaptation of the bacterial tDNA(His) and identity rule divergence. Our results demonstrate that TFAM is an effective tool for the bioinformatics, comparative genomics and evolutionary study of tRNA identity.

  • 20.
    Ardell, David Herman
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Informatic Approaches to Molecular Translation2005In: Intelligent Information Processing and Web Mining: Advances in Soft Computing, Proceedings of the IIS'2005 Symposium, 2005, p. 684-Conference paper (Other scientific)
  • 21. Bandelt, HJ
    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.
    Quasi-median graphs from sets of partitions2002In: Discrete Applied Mathematics, ISSN 0166-218X, Vol. 122, no 23-35, p. 23-35Article in journal (Other (popular scientific, debate etc.))
    Abstract [en]

    In studies of molecular evolution, one is typically confronted with the task of inferring a phylogenetic tree from a set X of sequences of length n over a finite alphabet Lambda. For studies that invoke parsimony, it has been found helpful to consider the quasi-median graph generated by X in the Hamming graph Lambda(n). Although a great deal is already known about quasi-median graphs (and their algebraic counterparts), little is known about the quasi-median generation in Lambda(n) starting from a set X of vertices. We describe the vertices of the quasi-median graph generated by X in terms of the coordinatewise partitions of X. In particular, we clarify when the generated quasi-median graph is the so-called relation graph associated with X. This immediately characterizes the instances where either a block graph or the total Hamming graph is generated.

  • 22.
    Bang S, Koolen JH, Moulton V
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    A bound for the number of columns l((c,a,b)) in the intersection array of a distance-regular graph2003In: European Journal of Combinatorics, ISSN 0195-6698, Vol. 24, no 7, p. 785-795Article in journal (Refereed)
  • 23.
    Barrio, Alvaro Martinez
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Eriksson, Oskar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Medical Genetics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Badhai, Jitendra
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Medical Genetics.
    Fröjmark, Anne-Sophie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Medical Genetics.
    Bongcam-Rudloff, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Dahl, Niklas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Medical Genetics.
    Schuster, Jens
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Medical Genetics.
    Targeted Resequencing and Analysis of the Diamond-Blackfan Anemia Disease Locus RPS192009In: PLoS ONE, ISSN 1932-6203, Vol. 4, no 7, p. e6172-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: The Ribosomal protein S19 gene locus (RPS19) has been linked to two kinds of red cell aplasia, Diamond-Blackfan Anemia (DBA) and Transient Erythroblastopenia in Childhood (TEC). Mutations in RPS19 coding sequences have been found in 25% of DBA patients, but not in TEC patients. It has been suggested that non-coding RPS19 sequence variants contribute to the considerable clinical variability in red cell aplasia. We therefore aimed at identifying non-coding variations associated with DBA or TEC phenotypes. METHODOLOGY/PRINCIPAL FINDINGS: We targeted a region of 19'980 bp encompassing the RPS19 gene in a cohort of 89 DBA and TEC patients for resequencing. We provide here a catalog of the considerable, previously unrecognized degree of variation in this region. We identified 73 variations (65 SNPs, 8 indels) that all are located outside of the RPS19 open reading frame, and of which 67.1% are classified as novel. We hypothesize that specific alleles in non-coding regions of RPS19 could alter the binding of regulatory proteins or transcription factors. Therefore, we carried out an extensive analysis to identify transcription factor binding sites (TFBS). A series of putative interaction sites coincide with detected variants. Sixteen of the corresponding transcription factors are of particular interest, as they are housekeeping genes or show a direct link to hematopoiesis, tumorigenesis or leukemia (e.g. GATA-1/2, PU.1, MZF-1). CONCLUSIONS: Specific alleles at predicted TFBSs may alter the expression of RPS19, modify an important interaction between transcription factors with overlapping TFBS or remove an important stimulus for hematopoiesis. We suggest that the detected interactions are of importance for hematopoiesis and could provide new insights into individual response to treatment.

  • 24.
    Barrio, Alvaro Martínez
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Lagercrantz, Erik
    Sperber, Göran O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Physiology.
    Blomberg, Jonas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Virology.
    Bongcam-Rudloff, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Annotation and visualization of endogenous retroviral sequences using the Distributed Annotation System (DAS) and eBioX2009In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 10 Suppl. 6, p. S18-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: The Distributed Annotation System (DAS) is a widely used network protocol for sharing biological information. The distributed aspects of the protocol enable the use of various reference and annotation servers for connecting biological sequence data to pertinent annotations in order to depict an integrated view of the data for the final user. RESULTS: An annotation server has been devised to provide information about the endogenous retroviruses detected and annotated by a specialized in silico tool called RetroTector. We describe the procedure to implement the DAS 1.5 protocol commands necessary for constructing the DAS annotation server. We use our server to exemplify those steps. Data distribution is kept separated from visualization which is carried out by eBioX, an easy to use open source program incorporating multiple bioinformatics utilities. Some well characterized endogenous retroviruses are shown in two different DAS clients. A rapid analysis of areas free from retroviral insertions could be facilitated by our annotations. CONCLUSION: The DAS protocol has shown to be advantageous in the distribution of endogenous retrovirus data. The distributed nature of the protocol is also found to aid in combining annotation and visualization along a genome in order to enhance the understanding of ERV contribution to its evolution. Reference and annotation servers are conjointly used by eBioX to provide visualization of ERV annotations as well as other data sources. Our DAS data source can be found in the central public DAS service repository, http://www.dasregistry.org, or at http://loka.bmc.uu.se/das/sources.

  • 25. Beisvåg, Vidar
    et al.
    Kauffmann, Audrey
    Malone, James
    Foy, Carole
    Salit, Marc
    Schimmel, Heinz
    Bongcam-Rudloff, Erik
    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.
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Parkinson, Helen
    Huber, Wolfgang