Cargando…
Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data
Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling method...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3608647/ https://www.ncbi.nlm.nih.gov/pubmed/23555596 http://dx.doi.org/10.1371/journal.pone.0058815 |
_version_ | 1782264263476248576 |
---|---|
author | Quinn, Emma M. Cormican, Paul Kenny, Elaine M. Hill, Matthew Anney, Richard Gill, Michael Corvin, Aiden P. Morris, Derek W. |
author_facet | Quinn, Emma M. Cormican, Paul Kenny, Elaine M. Hill, Matthew Anney, Richard Gill, Michael Corvin, Aiden P. Morris, Derek W. |
author_sort | Quinn, Emma M. |
collection | PubMed |
description | Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling methods following alignment to both genome and transcriptome by applying them to RNA-seq data from a HapMap lymphoblastoid cell line sample and comparing results with sequence variation data from 1000 Genomes. We determined that the best method to achieve high specificity and sensitivity, and greatest number of SNP calls, is to remove duplicate sequence reads after alignment to the genome and to call SNPs using SAMtools. The accuracy of SNP calls is dependent on sequence coverage available. In terms of specificity, 89% of RNA-seq SNPs calls were true variants where coverage is >10X. In terms of sensitivity, at >10X coverage 92% of all expected SNPs in expressed exons could be detected. Overall, the results indicate that RNA-seq SNP data are a very useful by-product of sequence-based transcriptome analysis. If RNA-seq is applied to disease tissue samples and assuming that genes carrying mutations relevant to disease biology are being expressed, a very high proportion of these mutations can be detected. |
format | Online Article Text |
id | pubmed-3608647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36086472013-04-03 Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data Quinn, Emma M. Cormican, Paul Kenny, Elaine M. Hill, Matthew Anney, Richard Gill, Michael Corvin, Aiden P. Morris, Derek W. PLoS One Research Article Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling methods following alignment to both genome and transcriptome by applying them to RNA-seq data from a HapMap lymphoblastoid cell line sample and comparing results with sequence variation data from 1000 Genomes. We determined that the best method to achieve high specificity and sensitivity, and greatest number of SNP calls, is to remove duplicate sequence reads after alignment to the genome and to call SNPs using SAMtools. The accuracy of SNP calls is dependent on sequence coverage available. In terms of specificity, 89% of RNA-seq SNPs calls were true variants where coverage is >10X. In terms of sensitivity, at >10X coverage 92% of all expected SNPs in expressed exons could be detected. Overall, the results indicate that RNA-seq SNP data are a very useful by-product of sequence-based transcriptome analysis. If RNA-seq is applied to disease tissue samples and assuming that genes carrying mutations relevant to disease biology are being expressed, a very high proportion of these mutations can be detected. Public Library of Science 2013-03-26 /pmc/articles/PMC3608647/ /pubmed/23555596 http://dx.doi.org/10.1371/journal.pone.0058815 Text en © 2013 Quinn et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Quinn, Emma M. Cormican, Paul Kenny, Elaine M. Hill, Matthew Anney, Richard Gill, Michael Corvin, Aiden P. Morris, Derek W. Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data |
title | Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data |
title_full | Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data |
title_fullStr | Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data |
title_full_unstemmed | Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data |
title_short | Development of Strategies for SNP Detection in RNA-Seq Data: Application to Lymphoblastoid Cell Lines and Evaluation Using 1000 Genomes Data |
title_sort | development of strategies for snp detection in rna-seq data: application to lymphoblastoid cell lines and evaluation using 1000 genomes data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3608647/ https://www.ncbi.nlm.nih.gov/pubmed/23555596 http://dx.doi.org/10.1371/journal.pone.0058815 |
work_keys_str_mv | AT quinnemmam developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata AT cormicanpaul developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata AT kennyelainem developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata AT hillmatthew developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata AT anneyrichard developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata AT gillmichael developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata AT corvinaidenp developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata AT morrisderekw developmentofstrategiesforsnpdetectioninrnaseqdataapplicationtolymphoblastoidcelllinesandevaluationusing1000genomesdata |