Differential analysis of gene regulation at transcript resolution with RNA-seq.
Differential analysis of gene regulation at transcript resolution with RNA-seq.
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DOI：
10.1038/nbt.2450
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发表时间：
2013-01
影响因子：
46.9
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中科院分区：
文献类型：
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使用高通量RNA测序（RNA-seq）进行基因和转录物表达的差异分析由于测量变异性的几个来源而变得复杂，并提出了许多统计挑战。我们提出了Cuffdiff 2，这是一种算法，可以估计转录水平分辨率下的表达，并控制复制库中明显的可变性。Cuffdiff 2强有力地识别了差异表达的转录本和基因，揭示了差异剪接和启动子偏好的变化。我们通过对肺成纤维细胞对发育转录因子HOXA1缺失反应的差异分析证明了我们方法的准确性，我们发现HOXA1是肺成纤维细胞和HeLa细胞周期进展所必需的。HOXA1的缺失导致数千个个体转录本的显著表达水平变化，以及细胞周期关键调节因子的异构体转换事件。Cuffdiff 2在转录本分辨率的RNA-seq实验中进行了稳健的差异分析，揭示了其他高通量技术不易观察到的调控层。
Differential analysis of gene and transcript expression using high-throughput RNA sequencing (RNA-seq) is complicated by several sources of measurement variability and poses numerous statistical challenges. We present Cuffdiff 2, an algorithm that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries. Cuffdiff 2 robustly identifies differentially expressed transcripts and genes and reveals differential splicing and promoter-preference changes. We demonstrate the accuracy of our approach through differential analysis of lung fibroblasts in response to loss of the developmental transcription factor HOXA1, which we show is required for lung fibroblast and HeLa cell cycle progression. Loss of HOXA1 results in significant expression level changes in thousands of individual transcripts, along with isoform switching events in key regulators of the cell cycle. Cuffdiff 2 performs robust differential analysis in RNA-seq experiments at transcript resolution, revealing a layer of regulation not readily observable with other high-throughput technologies.
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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：
4.6
作者：
Guillamot, Maria;Manchado, Eusebio;Chiesa, Massimo;Gomez-Lopez, Gonzalo;Pisano, David G.;Sacristan, Maria P.;Malumbres, Marcos
通讯作者：
Malumbres, Marcos
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：
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：
46.9
作者：
通讯作者：
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