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The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data

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, annota...

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Autores principales: Tang, Xiaojia, Baheti, Saurabh, Shameer, Khader, Thompson, Kevin J., Wills, Quin, Niu, Nifang, Holcomb, Ilona N., Boutet, Stephane C., Ramakrishnan, Ramesh, Kachergus, Jennifer M., Kocher, Jean-Pierre A., Weinshilboum, Richard M., Wang, Liewei, Thompson, E. Aubrey, Kalari, Krishna R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267611/
https://www.ncbi.nlm.nih.gov/pubmed/25352556
http://dx.doi.org/10.1093/nar/gku1005
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author Tang, Xiaojia
Baheti, Saurabh
Shameer, Khader
Thompson, Kevin J.
Wills, Quin
Niu, Nifang
Holcomb, Ilona N.
Boutet, Stephane C.
Ramakrishnan, Ramesh
Kachergus, Jennifer M.
Kocher, Jean-Pierre A.
Weinshilboum, Richard M.
Wang, Liewei
Thompson, E. Aubrey
Kalari, Krishna R.
author_facet Tang, Xiaojia
Baheti, Saurabh
Shameer, Khader
Thompson, Kevin J.
Wills, Quin
Niu, Nifang
Holcomb, Ilona N.
Boutet, Stephane C.
Ramakrishnan, Ramesh
Kachergus, Jennifer M.
Kocher, Jean-Pierre A.
Weinshilboum, Richard M.
Wang, Liewei
Thompson, E. Aubrey
Kalari, Krishna R.
author_sort Tang, Xiaojia
collection PubMed
description 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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spelling pubmed-42676112014-12-23 The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data Tang, Xiaojia Baheti, Saurabh Shameer, Khader Thompson, Kevin J. Wills, Quin Niu, Nifang Holcomb, Ilona N. Boutet, Stephane C. Ramakrishnan, Ramesh Kachergus, Jennifer M. Kocher, Jean-Pierre A. Weinshilboum, Richard M. Wang, Liewei Thompson, E. Aubrey Kalari, Krishna R. Nucleic Acids Res Methods Online 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/. Oxford University Press 2014-12-16 2014-10-28 /pmc/articles/PMC4267611/ /pubmed/25352556 http://dx.doi.org/10.1093/nar/gku1005 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Tang, Xiaojia
Baheti, Saurabh
Shameer, Khader
Thompson, Kevin J.
Wills, Quin
Niu, Nifang
Holcomb, Ilona N.
Boutet, Stephane C.
Ramakrishnan, Ramesh
Kachergus, Jennifer M.
Kocher, Jean-Pierre A.
Weinshilboum, Richard M.
Wang, Liewei
Thompson, E. Aubrey
Kalari, Krishna R.
The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
title The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
title_full The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
title_fullStr The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
title_full_unstemmed The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
title_short The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
title_sort esnv-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267611/
https://www.ncbi.nlm.nih.gov/pubmed/25352556
http://dx.doi.org/10.1093/nar/gku1005
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