GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.
GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.
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DOI：
10.1371/journal.pone.0025279
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Chang JH
中科院分区：
文献类型：
--
作者：
Cumbie JS;Kimbrel JA;Di Y;Schafer DW;Wilhelm LJ;Fox SE;Sullivan CM;Curzon AD;Carrington JC;Mockler TC;Chang JH
基因计数器是一个完整的基于Perl的计算管道，用于分析差异基因表达的RNA序列(RNA-Seq)数据。除了用于研究真核模式生物的转录本外，基因计数器还适用于原核生物和没有可用的基因组参考序列的非模式生物。对于比对，基因计数器配置为CASHX、Bowtie和BWA，但最终用户可以使用任何符合序列比对/图谱(SAM)的首选程序。为了分析差异基因表达的数据，可以使用基于负二项分布变化的三个统计程序包中的任何一个来运行基因计数器。默认方法是我们基于负二项分布的过度参数版本开发的一种新的、简单的统计检验。基因计数器还包括三种不同的方法，用于评估丰富的基因本体(GO)术语的差异表达特征。结果是透明的，数据系统地存储在MySQL关系数据库中，以便于进行其他分析和质量评估。我们使用下一代测序技术生成了一个小规模的RNA-Seq数据集，该数据集来源于对拟南芥防御反应的大量研究，并使用基因计数器对数据进行处理。总体而言，微阵列分析的支持以及三个统计程序包结果的观察和大量重叠表明，基因计数器非常适合于处理小样本和基因计数高度变异性的独特特征。
GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.
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影响因子：
4.4
作者：
McIntyre LM;Lopiano KK;Morse AM;Amin V;Oberg AL;Young LJ;Nuzhdin SV
通讯作者：
Nuzhdin SV
影响因子：
64.8
作者：
Graveley BR;Brooks AN;Carlson JW;Duff MO;Landolin JM;Yang L;Artieri CG;van Baren MJ;Boley N;Booth BW;Brown JB;Cherbas L;Davis CA;Dobin A;Li R;Lin W;Malone JH;Mattiuzzo NR;Miller D;Sturgill D;Tuch BB;Zaleski C;Zhang D;Blanchette M;Dudoit S;Eads B;Green RE;Hammonds A;Jiang L;Kapranov P;Langton L;Perrimon N;Sandler JE;Wan KH;Willingham A;Zhang Y;Zou Y;Andrews J;Bickel PJ;Brenner SE;Brent MR;Cherbas P;Gingeras TR;Hoskins RA;Kaufman TC;Oliver B;Celniker SE
通讯作者：
Celniker SE
影响因子：
7
作者：
Filichkin, Sergei A.;Priest, Henry D.;Mockler, Todd C.
通讯作者：
Mockler, Todd C.
影响因子：
12.3
作者：
Langmead B;Trapnell C;Pop M;Salzberg SL
通讯作者：
Salzberg SL
DOI：
10.1007/978-1-60327-563-7_5
发表时间：
2009-01-01
期刊：
PLANT SYSTEMS BIOLOGY
影响因子：
--
作者：
Fox, Samuel;Filichkin, Sergei;Mockler, Todd C.
通讯作者：
Mockler, Todd C.