Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.
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DOI：
10.1038/nbt.1621
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发表时间：
2010-05
影响因子：
46.9
通讯作者：
Pachter L
中科院分区：
文献类型：
--
作者：
Trapnell C;Williams BA;Pertea G;Mortazavi A;Kwan G;van Baren MJ;Salzberg SL;Wold BJ;Pachter L
高通量mRNA测序（RNA-Seq）有望同时发现转录本和估计丰度。我们介绍了一种算法，转录装配加上统计模型的RNA-Seq实验，产生估计的丰度。我们的算法在一个名为Cufflinks的开源软件程序中实现。为了测试Cufflinks，我们对来自小鼠成肌细胞系的超过4.3亿对75 bp RNA-Seq读数进行了测序和分析，这些读数代表了分化时间序列。我们检测到13，692个已知的转录本和3，724个以前未注释的转录本，其中62%得到了独立表达数据或其他物种同源基因的支持。对时间序列中转录本表达的分析显示，330个基因的显性转录起始位点（TSS）或剪接异构体发生了完全转换，沿着还有1,304个基因发生了更微妙的转换。这些动力学表明，在这个研究充分的肌肉发育模型中，存在大量的调节灵活性和复杂性。
High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation. We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
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影响因子：
64.8
作者：
Cordes KR;Sheehy NT;White MP;Berry EC;Morton SU;Muth AN;Lee TH;Miano JM;Ivey KN;Srivastava D
通讯作者：
Srivastava D
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.
影响因子：
4.9
作者：
DILWORTH, RP
通讯作者：
DILWORTH, RP
DOI：
10.1093/bioinformatics/btp692
发表时间：
2010-02-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Li B;Ruotti V;Stewart RM;Thomson JA;Dewey CN
通讯作者：
Dewey CN