Comparison of software packages for detecting differential expression in RNA-seq studies.

Comparison of software packages for detecting differential expression in RNA-seq studies.
复制标题

DOI:
10.1093/bib/bbt086
复制
发表时间:
2015-01
影响因子:
9.5
通讯作者:
Elo LL
Elo LL
中科院分区:
生物学2区
文献类型:
--
作者:
Seyednasrollah F;Laiho A;Elo LL

文献摘要

参考文献

被引文献

相似文献

RNA测序(RNA-seq)已迅速成为表征转录组的流行工具。许多RNA-seq研究中的一个基本研究问题是鉴定在不同样品组之间显示差异表达的可靠分子标记。随着RNA-seq的日益普及,已经为这项任务开发了许多数据分析方法和管道。然而,目前还没有关于最佳做法的明确共识,这使得选择适当的方法成为一项艰巨的任务,特别是对于没有强大统计或计算背景的基本用户来说。为了帮助选择,我们在这里进行了系统的比较,八个广泛使用的软件包和管道检测样本组之间的差异表达,在实际的研究设置,并提供选择一个强大的管道的一般准则。总的来说,我们的研究结果表明,所使用的数据分析工具可以显着影响数据分析的结果,突出了这种选择的重要性。
RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice.
微阵列研究中真实差异表达发现的再现性明显较低
DOI: 10.1093/bioinformatics/btn365
发表时间: 2008-09-15
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Zhang, Min;Yao, Chen;Li, Xia
通讯作者: Li, Xia
DOI: 10.1093/nar/gks804
发表时间: 2012-11-01
影响因子: 14.9
作者:
Nookaew I;Papini M;Pornputtapong N;Scalcinati G;Fagerberg L;Uhlén M;Nielsen J
通讯作者: Nielsen J
DOI: 10.1111/j.2517-6161.1995.tb02031.x
发表时间: 1995-01-01
影响因子: 5.8
作者:
BENJAMINI, Y;HOCHBERG, Y
通讯作者: HOCHBERG, Y
DOI: 10.1093/bioinformatics/btt087
发表时间: 2013-04-15
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Leng, Ning;Dawson, John A.;Kendziorski, Christina
通讯作者: Kendziorski, Christina
DOI: 10.1038/nmeth.1226
发表时间: 2008-07-01
期刊: NATURE METHODS
影响因子: 48
作者:
Mortazavi, Ali;Williams, Brian A.;Wold, Barbara
通讯作者: Wold, Barbara