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Genome Biol DOI:10.1186/s13059-021-02337-8

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

Publication TypeJournal Article
Year of Publication2021
AuthorsChung, M, Bruno, VM, Rasko, DA, Cuomo, CA, Muñoz, JF, Livny, J, Shetty, AC, Mahurkar, A, Hotopp, JCDunning
JournalGenome Biol
Date Published2021 04 29
KeywordsAnimals, Eukaryota, Gene Expression Profiling, Gene Expression Regulation, High-Throughput Nucleotide Sequencing, Humans, Organ Specificity, Prokaryotic Cells, RNA, RNA-Seq, ROC Curve, Sequence Alignment, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome, Workflow

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.


Alternate JournalGenome Biol
PubMed ID33926528
PubMed Central IDPMC8082843
Grant ListU19 AI110818 / AI / NIAID NIH HHS / United States
U19 AI110820 / AI / NIAID NIH HHS / United States