Background: Although the costs of next generation sequencing technology have decreased over the past years,\nthere is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is\nno one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational\nworkflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any\ngenome.\nResults: For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real\ndatasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of\nreads, gene expression assessment and exon read counting, identification of expressed single nucleotide\nvariants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This\nworkflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes.\nSeveral clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The\nresults from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of\ndiseases for better diagnosis and treatment of patients.\nConclusions: Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants,\nmapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed\non a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from\nhttp://bioinformaticstools.mayo.edu/research/maprseq/.
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