Best practices on the differential expression analysis of multi-species RNA-seq

Genome Biology

Christina A. Cuomo¹  José F. Muñoz¹  Jonathan Livny¹  Anup Mahurkar²  Amol C. Shetty²  Vincent M. Bruno³  Matthew Chung³  David A. Rasko³  Julie C. Dunning Hotopp⁴ 

[1] Infectious Disease and Microbiome Program, Broad Institute, 02142, Cambridge, MA, USA;Institute for Genome Sciences, University of Maryland School of Medicine, 21201, Baltimore, MD, USA;Institute for Genome Sciences, University of Maryland School of Medicine, 21201, Baltimore, MD, USA;Department of Microbiology and Immunology, University of Maryland School of Medicine, 21201, Baltimore, MD, USA;Institute for Genome Sciences, University of Maryland School of Medicine, 21201, Baltimore, MD, USA;Department of Microbiology and Immunology, University of Maryland School of Medicine, 21201, Baltimore, MD, USA;Greenebaum Cancer Center, University of Maryland, 21201, Baltimore, MD, USA;

关键词: RNA-Seq;    Transcriptomics;    Best practices;    Differential gene expression;   

DOI  :  10.1186/s13059-021-02337-8

来源: Springer

【 摘 要 】

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

【 授权许可】

CC BY