Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data

dc.contributor.author Adetunji, Modupeore
dc.contributor.author Lamont, Susan
dc.contributor.author Lamont, Susan
dc.contributor.author Abasht, Behnam
dc.contributor.author Schmidt, Carl
dc.contributor.department Animal Science
dc.date 2020-09-11T22:45:52.000
dc.date.accessioned 2021-02-24T21:10:59Z
dc.date.available 2021-02-24T21:10:59Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-09-23
dc.description.abstract <p>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 <a href="https://modupeore.github.io/VAP/">https://modupeore.github.io/VAP/</a>.</p>
dc.description.comments <p>This article is published as Adetunji, Modupeore O., Susan J. Lamont, Behnam Abasht, and Carl J. Schmidt. "Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data." <em>PloS ONE</em> 14, no. 9 (2019): e0216838. DOI: <a href="https://doi.org/10.1371/journal.pone.0216838" target="_blank">10.1371/journal.pone.0216838</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ans_pubs/553/
dc.identifier.articleid 1554
dc.identifier.contextkey 19354168
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ans_pubs/553
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/93308
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ans_pubs/553/2019_LamontSusan_VariantAnalysis.pdf|||Sat Jan 15 00:55:53 UTC 2022
dc.source.uri 10.1371/journal.pone.0216838
dc.subject.disciplines Animal Sciences
dc.subject.disciplines Computational Biology
dc.subject.disciplines Genomics
dc.subject.disciplines Poultry or Avian Science
dc.title Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 5dee3d24-aa7a-4fe1-abf6-f0bb615bfe24
relation.isOrgUnitOfPublication 85ecce08-311a-441b-9c4d-ee2a3569506f
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