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      LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor

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      Author(s): 1 , * , 1 , 2 , 3 , *
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Summary: Genomic datasets are often interpreted in the context of large-scale reference databases. One approach is to identify significantly overlapping gene sets, which works well for gene-centric data. However, many types of high-throughput data are based on genomic regions. Locus Overlap Analysis (LOLA) provides easy and automatable enrichment analysis for genomic region sets, thus facilitating the interpretation of functional genomics and epigenomics data.

          Availability and Implementation: R package available in Bioconductor and on the following website: http://lola.computational-epigenetics.org.

          Contact: nsheffield@ 123456cemm.oeaw.ac.at or cbock@ 123456cemm.oeaw.ac.at

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          DNA methylation dynamics during in vivo differentiation of blood and skin stem cells.

          Christoph Bock, Isabel Beerman, Wen-Hui Lien … (2012)
          DNA methylation is a mechanism of epigenetic regulation that is common to all vertebrates. Functional studies underscore its relevance for tissue homeostasis, but the global dynamics of DNA methylation during in vivo differentiation remain underexplored. Here we report high-resolution DNA methylation maps of adult stem cell differentiation in mouse, focusing on 19 purified cell populations of the blood and skin lineages. DNA methylation changes were locus specific and relatively modest in magnitude. They frequently overlapped with lineage-associated transcription factors and their binding sites, suggesting that DNA methylation may protect cells from aberrant transcription factor activation. DNA methylation and gene expression provided complementary information, and combining the two enabled us to infer the cellular differentiation hierarchy of the blood lineage directly from genome-scale data. In summary, these results demonstrate that in vivo differentiation of adult stem cells is associated with small but informative changes in the genomic distribution of DNA methylation. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Genomation: a toolkit to summarize, annotate and visualize genomic intervals.

            Altuna Akalin, Vedran Franke, Kristian Vlahoviček … (2015)
            Biological insights can be obtained through computational integration of genomics data sets consisting of diverse types of information. The integration is often hampered by a large variety of existing file formats, often containing similar information, and the necessity to use complicated tools to achieve the desired results. We have built an R package, genomation, to expedite the extraction of biological information from high throughput data. The package works with a variety of genomic interval file types and enables easy summarization and annotation of high throughput data sets with given genomic annotations.
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              Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions

              Nathan C. Sheffield, Robert E. Thurman, Lingyun Song … (2013)
              Regulatory elements recruit transcription factors that modulate gene expression distinctly across cell types, but the relationships among these remains elusive. To address this, we analyzed matched DNase-seq and gene expression data for 112 human samples representing 72 cell types. We first defined more than 1800 clusters of DNase I hypersensitive sites (DHSs) with similar tissue specificity of DNase-seq signal patterns. We then used these to uncover distinct associations between DHSs and promoters, CpG islands, conserved elements, and transcription factor motif enrichment. Motif analysis within clusters identified known and novel motifs in cell-type-specific and ubiquitous regulatory elements and supports a role for AP-1 regulating open chromatin. We developed a classifier that accurately predicts cell-type lineage based on only 43 DHSs and evaluated the tissue of origin for cancer cell types. A similar classifier identified three sex-specific loci on the X chromosome, including the XIST lincRNA locus. By correlating DNase I signal and gene expression, we predicted regulated genes for more than 500K DHSs. Finally, we introduce a web resource to enable researchers to use these results to explore these regulatory patterns and better understand how expression is modulated within and across human cell types.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 February 2016
                Publication date (Electronic): 27 October 2015
                Publication date PMC-release: 27 October 2015
                Volume: 32
                Issue: 4
                Pages: 587-589
                Affiliations
                1CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences,
                2Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria and
                3Max Planck Institute for Informatics, 66123 Saarbrücken, Germany
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: John Hancock

                Article
                Publisher ID: btv612
                DOI: 10.1093/bioinformatics/btv612
                PMC ID: 4743627
                PubMed ID: 26508757
                SO-VID: 422e67c3-f28a-4143-97f8-da1359e70aec
                Copyright © © The Author 2015. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 24 May 2015
                Date revision received : 7 September 2015
                Date accepted : 16 October 2015
                Page count
                Pages: 3
                Categories
                Subject: Applications Notes
                Subject: Genome Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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