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Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data
The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6756534/ https://www.ncbi.nlm.nih.gov/pubmed/31545812 http://dx.doi.org/10.1371/journal.pone.0216838 |
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author | Adetunji, Modupeore O. Lamont, Susan J. Abasht, Behnam Schmidt, Carl J. |
author_facet | Adetunji, Modupeore O. Lamont, Susan J. Abasht, Behnam Schmidt, Carl J. |
author_sort | Adetunji, Modupeore O. |
collection | PubMed |
description | The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. Over 65% of WGS coding variants were identified from RNA-seq. Further, our results discovered SNPs resulting from post transcriptional modifications, such as RNA editing, which may reveal potentially functional variation that would have otherwise been missed in genomic data. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. The source code and user manuals are available at https://modupeore.github.io/VAP/. |
format | Online Article Text |
id | pubmed-6756534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67565342019-10-04 Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data Adetunji, Modupeore O. Lamont, Susan J. Abasht, Behnam Schmidt, Carl J. PLoS One Research Article The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. Over 65% of WGS coding variants were identified from RNA-seq. Further, our results discovered SNPs resulting from post transcriptional modifications, such as RNA editing, which may reveal potentially functional variation that would have otherwise been missed in genomic data. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. The source code and user manuals are available at https://modupeore.github.io/VAP/. Public Library of Science 2019-09-23 /pmc/articles/PMC6756534/ /pubmed/31545812 http://dx.doi.org/10.1371/journal.pone.0216838 Text en © 2019 Adetunji 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Adetunji, Modupeore O. Lamont, Susan J. Abasht, Behnam Schmidt, Carl J. Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data |
title | Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data |
title_full | Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data |
title_fullStr | Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data |
title_full_unstemmed | Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data |
title_short | Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data |
title_sort | variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6756534/ https://www.ncbi.nlm.nih.gov/pubmed/31545812 http://dx.doi.org/10.1371/journal.pone.0216838 |
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