Comparing reference-based RNA-Seq mapping methods for non-human primate data.

摘要: The application of next-generation sequencing technology to gene expression quantification analysis, namely, RNA-Sequencing, has transformed the way in which studies are conducted and analyzed. These advances particular interest researchers studying organisms with missing or incomplete genomes, as need for knowledge sequence information is overcome. De novo assembly methods have gained widespread acceptance RNA-Seq community no true reference genome transcriptome. While such tremendous utility, computational cost still a significant challenge large complex genomes. In this manuscript, we present comparison four reference-based mapping non-human primate data. We utilize TopHat2 GSNAP human genome, Bowtie2 Stampy transcriptome total six approaches. For each these methods, explore rates locations, number detected genes, correlations between computed values, utility resulting data differential analysis. show that indeed analysis mammalian reference, details should be carefully considered when doing so. Critical algorithm features include short seed sequences, allowance mismatches, gapped alignments addition splice junction gaps. Such facilitate sensitive alignment reference.