期刊
BMC GENOMICS
卷 15, 期 -, 页码 -出版社
BMC
DOI: 10.1186/1471-2164-15-343
关键词
Comparative transcriptomics; Ortholog annotation; RNA-seq; Gene expression; Prefrontal cortex; Evolution; Human evolution; Primate; Macaque; Chimpanzee
资金
- 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
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.
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