RNA-Seq optimization with eQTL gold standards

Shannon E. Ellis, Simone Gupta, Foram N. Ashar, Joel S. Bader, Andrew B. West, Dan Arking

Research output: Contribution to journalArticle

Abstract

Background: RNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.Results: To address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.Conclusion: As each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one's data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments.

Original languageEnglish (US)
Article number892
JournalBMC Genomics
Volume14
Issue number1
DOIs
StatePublished - Dec 17 2013

Fingerprint

RNA
RNA Sequence Analysis
Molecular Sequence Annotation
Gene Expression
Libraries
Genotype
Datasets
DNA
Brain

Keywords

  • Blood
  • Brain
  • EQTL
  • GTEx
  • LCL
  • RNA-Seq

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

Ellis, S. E., Gupta, S., Ashar, F. N., Bader, J. S., West, A. B., & Arking, D. (2013). RNA-Seq optimization with eQTL gold standards. BMC Genomics, 14(1), [892]. https://doi.org/10.1186/1471-2164-14-892

RNA-Seq optimization with eQTL gold standards. / Ellis, Shannon E.; Gupta, Simone; Ashar, Foram N.; Bader, Joel S.; West, Andrew B.; Arking, Dan.

In: BMC Genomics, Vol. 14, No. 1, 892, 17.12.2013.

Research output: Contribution to journalArticle

Ellis, SE, Gupta, S, Ashar, FN, Bader, JS, West, AB & Arking, D 2013, 'RNA-Seq optimization with eQTL gold standards', BMC Genomics, vol. 14, no. 1, 892. https://doi.org/10.1186/1471-2164-14-892
Ellis SE, Gupta S, Ashar FN, Bader JS, West AB, Arking D. RNA-Seq optimization with eQTL gold standards. BMC Genomics. 2013 Dec 17;14(1). 892. https://doi.org/10.1186/1471-2164-14-892
Ellis, Shannon E. ; Gupta, Simone ; Ashar, Foram N. ; Bader, Joel S. ; West, Andrew B. ; Arking, Dan. / RNA-Seq optimization with eQTL gold standards. In: BMC Genomics. 2013 ; Vol. 14, No. 1.
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