Journal
BMC GENOMICS
Volume 15, Issue -, Pages -Publisher
BMC
DOI: 10.1186/1471-2164-15-343
Keywords
Comparative transcriptomics; Ortholog annotation; RNA-seq; Gene expression; Prefrontal cortex; Evolution; Human evolution; Primate; Macaque; Chimpanzee
Funding
- Yale University Biomedical High Performance Computing Center
- China Scholarship Council
- Portuguese Foundation for Science and Technology
- NIH [MH081896, MH089929]
- Kavli Foundation
- James S. McDonnell Foundation
Ask authors/readers for more resources
Background: The accurate characterization of RNA transcripts and expression levels across species is critical for understanding transcriptome evolution. As available RNA-seq data accumulate rapidly, there is a great demand for tools that build gene annotations for cross-species RNA-seq analysis. However, prevailing methods of ortholog annotation for RNA-seq analysis between closely-related species do not take inter-species variation in mappability into consideration. Results: Here we present XSAnno, a computational framework that integrates previous approaches with multiple filters to improve the accuracy of inter-species transcriptome comparisons. The implementation of this approach in comparing RNA-seq data of human, chimpanzee, and rhesus macaque brain transcriptomes has reduced the false discovery of differentially expressed genes, while maintaining a low false negative rate. Conclusion: The present study demonstrates the utility of the XSAnno pipeline in building ortholog annotations and improving the accuracy of cross-species transcriptome comparisons.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available