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A benchmark for RNA-seq quantification pipelines
Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842274/ https://www.ncbi.nlm.nih.gov/pubmed/27107712 http://dx.doi.org/10.1186/s13059-016-0940-1 |
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author | Teng, Mingxiang Love, Michael I. Davis, Carrie A. Djebali, Sarah Dobin, Alexander Graveley, Brenton R. Li, Sheng Mason, Christopher E. Olson, Sara Pervouchine, Dmitri Sloan, Cricket A. Wei, Xintao Zhan, Lijun Irizarry, Rafael A. |
author_facet | Teng, Mingxiang Love, Michael I. Davis, Carrie A. Djebali, Sarah Dobin, Alexander Graveley, Brenton R. Li, Sheng Mason, Christopher E. Olson, Sara Pervouchine, Dmitri Sloan, Cricket A. Wei, Xintao Zhan, Lijun Irizarry, Rafael A. |
author_sort | Teng, Mingxiang |
collection | PubMed |
description | Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0940-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4842274 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48422742016-04-25 A benchmark for RNA-seq quantification pipelines Teng, Mingxiang Love, Michael I. Davis, Carrie A. Djebali, Sarah Dobin, Alexander Graveley, Brenton R. Li, Sheng Mason, Christopher E. Olson, Sara Pervouchine, Dmitri Sloan, Cricket A. Wei, Xintao Zhan, Lijun Irizarry, Rafael A. Genome Biol Method Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0940-1) contains supplementary material, which is available to authorized users. BioMed Central 2016-04-23 /pmc/articles/PMC4842274/ /pubmed/27107712 http://dx.doi.org/10.1186/s13059-016-0940-1 Text en © Teng et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Teng, Mingxiang Love, Michael I. Davis, Carrie A. Djebali, Sarah Dobin, Alexander Graveley, Brenton R. Li, Sheng Mason, Christopher E. Olson, Sara Pervouchine, Dmitri Sloan, Cricket A. Wei, Xintao Zhan, Lijun Irizarry, Rafael A. A benchmark for RNA-seq quantification pipelines |
title | A benchmark for RNA-seq quantification pipelines |
title_full | A benchmark for RNA-seq quantification pipelines |
title_fullStr | A benchmark for RNA-seq quantification pipelines |
title_full_unstemmed | A benchmark for RNA-seq quantification pipelines |
title_short | A benchmark for RNA-seq quantification pipelines |
title_sort | benchmark for rna-seq quantification pipelines |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842274/ https://www.ncbi.nlm.nih.gov/pubmed/27107712 http://dx.doi.org/10.1186/s13059-016-0940-1 |
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