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

Xiaojia Tang; Saurabh Baheti; Khader Shameer; Kevin J Thompson; Quin Wills; Nifang Niu; Ilona N Holcomb; Stephane C Boutet; Ramesh Ramakrishnan; Jennifer M Kachergus; Jean-Pierre A Kocher; Richard M Weinshilboum; Liewei Wang; E Aubrey Thompson; Krishna R Kalari

doi:10.1093/nar/gku1005

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

Journal article

Open access

Peer reviewed

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

Xiaojia Tang, Saurabh Baheti, Khader Shameer, Kevin J Thompson, Quin Wills, Nifang Niu, Ilona N Holcomb, Stephane C Boutet, Ramesh Ramakrishnan, Jennifer M Kachergus, …

Nucleic acids research, Vol.42(22), pp.e172-e172

12/16/2014

DOI: 10.1093/nar/gku1005

PMCID: PMC4267611

PMID: 25352556

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Abstract

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/.

Breast Neoplasms - genetics

Cell Line

Cell Line, Tumor

Exome

Female

Gene Expression Profiling - methods

Genetic Variation

Humans

Mutation

Polymorphism, Single Nucleotide

Sequence Alignment

Sequence Analysis, RNA - methods

Single-Cell Analysis

Software

Details

Title: Subtitle: The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data
Creators: Xiaojia Tang - Mayo Clinic
Saurabh Baheti - Mayo Clinic
Khader Shameer - Mayo Clinic
Kevin J Thompson - Mayo Clinic
Quin Wills - Mayo Clinic
Nifang Niu - Mayo Clinic
Ilona N Holcomb - Fluidigm
Stephane C Boutet - Fluidigm
Ramesh Ramakrishnan - Fluidigm
Jennifer M Kachergus - Mayo Clinic in Florida
Jean-Pierre A Kocher - Mayo Clinic
Richard M Weinshilboum - Mayo Clinic
Liewei Wang - Mayo Clinic
E Aubrey Thompson - Mayo Clinic
Krishna R Kalari - Mayo Clinic
Resource Type: Journal article
Publication Details: Nucleic acids research, Vol.42(22), pp.e172-e172
DOI: 10.1093/nar/gku1005
PMID: 25352556
PMCID: PMC4267611
ISSN: 0305-1048
eISSN: 1362-4962
Grant note: P50 CA116201 / NCI NIH HHS U19 GM61388 / NIGMS NIH HHS P30 CA015083 / NCI NIH HHS
Language: English
Date published: 12/16/2014
Academic Unit: Stead Family Department of Pediatrics; Medical Genetics and Genomics
Record Identifier: 9984701642902771

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