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Statistical methods for identifying differentially expressed genes in RNA-Seq experiments
RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different exp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541212/ https://www.ncbi.nlm.nih.gov/pubmed/22849430 http://dx.doi.org/10.1186/2045-3701-2-26 |
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author | Fang, Zhide Martin, Jeffrey Wang, Zhong |
author_facet | Fang, Zhide Martin, Jeffrey Wang, Zhong |
author_sort | Fang, Zhide |
collection | PubMed |
description | RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses. |
format | Online Article Text |
id | pubmed-3541212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35412122013-01-11 Statistical methods for identifying differentially expressed genes in RNA-Seq experiments Fang, Zhide Martin, Jeffrey Wang, Zhong Cell Biosci Review RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can be applied to RNA-Seq data with or without modifications. Recently several additional methods have been developed specifically for RNA-Seq data sets. This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses. BioMed Central 2012-07-31 /pmc/articles/PMC3541212/ /pubmed/22849430 http://dx.doi.org/10.1186/2045-3701-2-26 Text en Copyright ©2012 Fang et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Fang, Zhide Martin, Jeffrey Wang, Zhong Statistical methods for identifying differentially expressed genes in RNA-Seq experiments |
title | Statistical methods for identifying differentially expressed genes in RNA-Seq experiments |
title_full | Statistical methods for identifying differentially expressed genes in RNA-Seq experiments |
title_fullStr | Statistical methods for identifying differentially expressed genes in RNA-Seq experiments |
title_full_unstemmed | Statistical methods for identifying differentially expressed genes in RNA-Seq experiments |
title_short | Statistical methods for identifying differentially expressed genes in RNA-Seq experiments |
title_sort | statistical methods for identifying differentially expressed genes in rna-seq experiments |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3541212/ https://www.ncbi.nlm.nih.gov/pubmed/22849430 http://dx.doi.org/10.1186/2045-3701-2-26 |
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