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MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments
Motivation: DNA enrichment followed by sequencing is a versatile tool in molecular biology, with a wide variety of applications including genome-wide analysis of epigenetic marks and mechanisms. A common requirement of these diverse applications is a comparison of read coverage between experimental...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892689/ https://www.ncbi.nlm.nih.gov/pubmed/24227674 http://dx.doi.org/10.1093/bioinformatics/btt650 |
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author | Lienhard, Matthias Grimm, Christina Morkel, Markus Herwig, Ralf Chavez, Lukas |
author_facet | Lienhard, Matthias Grimm, Christina Morkel, Markus Herwig, Ralf Chavez, Lukas |
author_sort | Lienhard, Matthias |
collection | PubMed |
description | Motivation: DNA enrichment followed by sequencing is a versatile tool in molecular biology, with a wide variety of applications including genome-wide analysis of epigenetic marks and mechanisms. A common requirement of these diverse applications is a comparison of read coverage between experimental conditions. The amount of samples generated for such comparisons ranges from few replicates to hundreds of samples per condition for epigenome-wide association studies. Consequently, there is an urgent need for software that allows for fast and simple processing and comparison of sequencing data derived from enriched DNA. Results: Here, we present a major update of the R/Bioconductor package MEDIPS, which allows for an arbitrary number of replicates per group and integrates sophisticated statistical methods for the detection of differential coverage between experimental conditions. Our approach can be applied to a diversity of quantitative sequencing data. In addition, our update adds novel functionality to MEDIPS, including correlation analysis between samples, and takes advantage of Bioconductor’s annotation databases to facilitate annotation of specific genomic regions. Availability and implementation: The latest version of MEDIPS is available as version 1.12.0 and part of Bioconductor 2.13. The package comes with a manual containing detailed description of its functionality and is available at http://www.bioconductor.org. Contact: lienhard@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-3892689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-38926892014-01-15 MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments Lienhard, Matthias Grimm, Christina Morkel, Markus Herwig, Ralf Chavez, Lukas Bioinformatics Applications Notes Motivation: DNA enrichment followed by sequencing is a versatile tool in molecular biology, with a wide variety of applications including genome-wide analysis of epigenetic marks and mechanisms. A common requirement of these diverse applications is a comparison of read coverage between experimental conditions. The amount of samples generated for such comparisons ranges from few replicates to hundreds of samples per condition for epigenome-wide association studies. Consequently, there is an urgent need for software that allows for fast and simple processing and comparison of sequencing data derived from enriched DNA. Results: Here, we present a major update of the R/Bioconductor package MEDIPS, which allows for an arbitrary number of replicates per group and integrates sophisticated statistical methods for the detection of differential coverage between experimental conditions. Our approach can be applied to a diversity of quantitative sequencing data. In addition, our update adds novel functionality to MEDIPS, including correlation analysis between samples, and takes advantage of Bioconductor’s annotation databases to facilitate annotation of specific genomic regions. Availability and implementation: The latest version of MEDIPS is available as version 1.12.0 and part of Bioconductor 2.13. The package comes with a manual containing detailed description of its functionality and is available at http://www.bioconductor.org. Contact: lienhard@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2014-01-15 2013-11-13 /pmc/articles/PMC3892689/ /pubmed/24227674 http://dx.doi.org/10.1093/bioinformatics/btt650 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Lienhard, Matthias Grimm, Christina Morkel, Markus Herwig, Ralf Chavez, Lukas MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments |
title | MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments |
title_full | MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments |
title_fullStr | MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments |
title_full_unstemmed | MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments |
title_short | MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments |
title_sort | medips: genome-wide differential coverage analysis of sequencing data derived from dna enrichment experiments |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892689/ https://www.ncbi.nlm.nih.gov/pubmed/24227674 http://dx.doi.org/10.1093/bioinformatics/btt650 |
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