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  • Artikel  (3)
  • Neueste Artikel (Zeitschrifteninhaltsverzeichnisse / in press)  (3)
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  • Bioinformatics  (2)
  • Bioinformatics. 2009; 25(16): 2103-2109. Published 2009 Mar 13. doi: 10.1093/bioinformatics/btp143.  (1)
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  • Neueste Artikel (Zeitschrifteninhaltsverzeichnisse / in press)  (3)
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  • 1
    Publikationsdatum: 2014-04-11
    Beschreibung: Motivation: Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. Results: We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated. Availability and implementation: R/Bioconductor package epigenomix. Contact: h.klein@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Digitale ISSN: 1460-2059
    Thema: Biologie , Informatik , Medizin
    Publiziert von Oxford University Press
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Publikationsdatum: 2013-06-24
    Beschreibung: Motivation: Bisulfite sequencing is currently the gold standard to obtain genome-wide DNA methylation profiles in eukaryotes. In contrast to the rapid development of appropriate pre-processing and alignment software, methods for analyzing the resulting methylation profiles are relatively limited so far. For instance, an appropriate pipeline to detect DNA methylation differences between cancer and control samples is still required. Results: We propose an algorithm that detects significantly differentially methylated regions in data obtained by targeted bisulfite sequencing approaches, such as reduced representation bisulfite sequencing. In a first step, this approach tests all target regions for methylation differences by taking spatial dependence into account. A false discovery rate procedure controls the expected proportion of incorrectly rejected regions. In a second step, the significant target regions are trimmed to the actually differentially methylated regions. This hierarchical procedure detects differentially methylated regions with increased power compared with existing methods. Availability: R/Bioconductor package BiSeq. Contact: katja.hebestreit@uni-muenster.de Supplementary information: Supplementary Data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Digitale ISSN: 1460-2059
    Thema: Biologie , Informatik , Medizin
    Publiziert von Oxford University Press
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 3
    Publikationsdatum: 2009-03-13
    Print ISSN: 1367-4803
    Digitale ISSN: 1460-2059
    Thema: Biologie , Informatik , Medizin
    Publiziert von Oxford University Press
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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