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

The accurate characterization of transcripts and 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. Prevailing methods of ortholog annotation for RNA-seq analysis, do not take inter-species variation in mappability into consideration. Here we developed 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 keeping the false negative rate low.