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edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test
Summary: Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514933/ https://www.ncbi.nlm.nih.gov/pubmed/25900919 http://dx.doi.org/10.1093/bioinformatics/btv209 |
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author | Dimont, Emmanuel Shi, Jiantao Kirchner, Rory Hide, Winston |
author_facet | Dimont, Emmanuel Shi, Jiantao Kirchner, Rory Hide, Winston |
author_sort | Dimont, Emmanuel |
collection | PubMed |
description | Summary: Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far, few exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as two replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes. Availability and implementation: The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/edgeRun). Contact: edimont@mail.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4514933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-45149332015-07-27 edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test Dimont, Emmanuel Shi, Jiantao Kirchner, Rory Hide, Winston Bioinformatics Applications Notes Summary: Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far, few exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as two replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes. Availability and implementation: The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/edgeRun). Contact: edimont@mail.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-08-01 2015-04-21 /pmc/articles/PMC4514933/ /pubmed/25900919 http://dx.doi.org/10.1093/bioinformatics/btv209 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Applications Notes Dimont, Emmanuel Shi, Jiantao Kirchner, Rory Hide, Winston edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test |
title | edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test |
title_full | edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test |
title_fullStr | edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test |
title_full_unstemmed | edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test |
title_short | edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test |
title_sort | edgerun: an r package for sensitive, functionally relevant differential expression discovery using an unconditional exact test |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4514933/ https://www.ncbi.nlm.nih.gov/pubmed/25900919 http://dx.doi.org/10.1093/bioinformatics/btv209 |
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