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BMC Genomics. 2014 May 7;15:343. doi: 10.1186/1471-2164-15-343.

XSAnno: a framework for building ortholog models in cross-species transcriptome comparisons.

Author information

1
Department of Neurobiology, Kavli Institute for Neuroscience, Yale School of Medicine, 06510 New Haven, CT, USA. nenad.sestan@yale.edu.

Abstract

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.

PMID:
24884593
PMCID:
PMC4035071
DOI:
10.1186/1471-2164-15-343
[Indexed for MEDLINE]
Free PMC Article

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