As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.
Описание
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. - PubMed - NCBI
%0 Journal Article
%1 Tarazona:2015:Nucleic-Acids-Res:26184878
%A Tarazona, S
%A Furió-Tarí, P
%A Turrà, D
%A Pietro, A D
%A Nueda, M J
%A Ferrer, A
%A Conesa, A
%D 2015
%J Nucleic Acids Res
%K fulltext normalization quality-control rna-seq shouldread software
%N 21
%R 10.1093/nar/gkv711
%T Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package
%U https://www.ncbi.nlm.nih.gov/pubmed/26184878?dopt=Abstract
%V 43
%X As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.
@article{Tarazona:2015:Nucleic-Acids-Res:26184878,
abstract = {As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression. },
added-at = {2019-05-05T12:08:50.000+0200},
author = {Tarazona, S and Furi{\'o}-Tar{\'i}, P and Turr{\`a}, D and Pietro, A D and Nueda, M J and Ferrer, A and Conesa, A},
biburl = {https://www.bibsonomy.org/bibtex/240f478918f763d62a5e8ac42025d0daa/marcsaric},
description = {Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. - PubMed - NCBI},
doi = {10.1093/nar/gkv711},
interhash = {9cc9f78be21687e6c42cf39162a21905},
intrahash = {40f478918f763d62a5e8ac42025d0daa},
journal = {Nucleic Acids Res},
keywords = {fulltext normalization quality-control rna-seq shouldread software},
month = dec,
number = 21,
pmid = {26184878},
timestamp = {2019-05-05T12:08:50.000+0200},
title = {Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package},
url = {https://www.ncbi.nlm.nih.gov/pubmed/26184878?dopt=Abstract},
volume = 43,
year = 2015
}