RNASEQR--a streamlined and accurate RNA-seq sequence analysis program.
RNASEQR--a streamlined and accurate RNA-seq sequence analysis program.
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RNASEQR-一个精确且精确的RNA-Seq序列分析程序。
DOI：
10.1093/nar/gkr1248
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发表时间：
2012-03
影响因子：
14.9
通讯作者：
Hood LE
中科院分区：
文献类型：
--
作者：
Chen LY;Wei KC;Huang AC;Wang K;Huang CY;Yi D;Tang CY;Galas DJ;Hood LE
基于下一代测序（NGS）技术的转录组学分析方法（通常称为RNA-seq）已被广泛用于研究全局基因表达、选择性外显子使用、新外显子发现、新转录异构体和基因组序列变异。然而，这项技术也提出了许多生物学和信息学的挑战，提取有意义的生物信息。RNA-seq数据分析建立在RNA-seq序列的高质量初始基因组定位和比对信息的基础上。为了实现这一目标，我们开发了RNASEQR，以准确有效地绘制数百万个RNA-seq序列。我们使用从Consensus CDS项目创建的模拟数据集和从人类胶质母细胞瘤患者生成的两个实验RNA-seq数据集，系统地比较了RNASEQR与四种最广泛使用的工具。我们的研究结果表明，RNASEQR可以更准确地估计基因表达，完整的基因结构和新的转录异构体，以及更准确地检测单核苷酸变异（SNV）。RNASEQR可以有效地分析RNA-seq实验的原始数据，并以与台式计算机上各种专业下游分析兼容的方式输出结果。
Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. Toward this goal, we have developed RNASEQR to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient. Our results showed that RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs). RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers.
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影响因子：
48
作者：
Katz, Yarden;Wang, Eric T.;Airoldi, Edoardo M.;Burge, Christopher B.
通讯作者：
Burge, Christopher B.
影响因子：
12.3
作者：
Denoeud F;Aury JM;Da Silva C;Noel B;Rogier O;Delledonne M;Morgante M;Valle G;Wincker P;Scarpelli C;Jaillon O;Artiguenave F
通讯作者：
Artiguenave F
影响因子：
48
作者：
Cloonan, Nicole;Forrest, Alistair R. R.;Grimmond, Sean M.
通讯作者：
Grimmond, Sean M.
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
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影响因子：
46.9
作者：
通讯作者：
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