Differential analysis of RNA-seq incorporating quantification uncertainty

Harold Pimentel; Nicolas L Bray; Suzette Puente; Páll Melsted; Lior Pachter

doi:10.1038/nmeth.4324

Differential analysis of RNA-seq incorporating quantification uncertainty

Nat Methods. 2017 Jul;14(7):687-690. doi: 10.1038/nmeth.4324. Epub 2017 Jun 5.

Authors

Harold Pimentel¹, Nicolas L Bray², Suzette Puente³, Páll Melsted⁴, Lior Pachter⁵

Affiliations

¹ Department of Computer Science, University of California, Berkeley, Berkeley, California, USA.
² Innovative Genomics Institute and Department of Molecular &Cell Biology, University of California, Berkeley, Berkeley, California, USA.
³ Department of Statistics, University of California, Berkeley, Berkeley, California, USA.
⁴ Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavík, Iceland.
⁵ Division of Biology and Biological Engineering, Caltech, Pasadena, California, USA.

PMID: 28581496
DOI: 10.1038/nmeth.4324

Abstract

We describe sleuth (http://pachterlab.github.io/sleuth), a method for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance. sleuth is implemented in an interactive shiny app that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of data from RNA-seq experiments.

MeSH terms

Base Sequence
Computer Simulation*
Gene Expression / physiology*
Models, Biological
RNA / genetics*
Software*

Substances

RNA