The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data.
The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data.
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DOI：
10.1093/nar/gku1005
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发表时间：
2014-12-16
影响因子：
14.9
通讯作者：
Kalari KR
中科院分区：
文献类型：
--
作者：
Tang X;Baheti S;Shameer K;Thompson KJ;Wills Q;Niu N;Holcomb IN;Boutet SC;Ramakrishnan R;Kachergus JM;Kocher JP;Weinshilboum RM;Wang L;Thompson EA;Kalari KR
Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6–96.8% precision and 91.6–95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/.
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影响因子：
64.8
作者：
通讯作者：
--
影响因子：
48
作者：
Engstrom, Par G.;Steijger, Tamara;Sipos, Botond;Grant, Gregory R.;Kahles, Andre;Raetsch, Gunnar;Goldman, Nick;Hubbard, Tim J.;Harrow, Jennifer;Guigo, Roderic;Bertone, Paul
通讯作者：
Bertone, Paul
影响因子：
14.9
作者：
Karolchik D;Barber GP;Casper J;Clawson H;Cline MS;Diekhans M;Dreszer TR;Fujita PA;Guruvadoo L;Haeussler M;Harte RA;Heitner S;Hinrichs AS;Learned K;Lee BT;Li CH;Raney BJ;Rhead B;Rosenbloom KR;Sloan CA;Speir ML;Zweig AS;Haussler D;Kuhn RM;Kent WJ
通讯作者：
Kent WJ
影响因子：
14.9
作者：
Forbes SA;Bindal N;Bamford S;Cole C;Kok CY;Beare D;Jia M;Shepherd R;Leung K;Menzies A;Teague JW;Campbell PJ;Stratton MR;Futreal PA
通讯作者：
Futreal PA
影响因子：
14.9
作者：
Finn RD;Mistry J;Tate J;Coggill P;Heger A;Pollington JE;Gavin OL;Gunasekaran P;Ceric G;Forslund K;Holm L;Sonnhammer EL;Eddy SR;Bateman A
通讯作者：
Bateman A