MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing

Krishna R. Kalari, Asha A. Nair, Jaysheel D. Bhavsar, Daniel R. O'Brien, Jaime I. Davila, Matthew A. Bockol, Jinfu Nie, Xiaojia Tang, Saurabh Baheti, Jay B. Doughty, Sumit Middha, Hugues Sicotte, Aubrey E. Thompson, Yan W. Asmann, Jean Pierre A. Kocher

Research output: Contribution to journalArticlepeer-review

122 Scopus citations

Abstract

Background: Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome.Results: For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients.Conclusions: Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/.

Original languageEnglish (US)
Article number224
JournalBMC bioinformatics
Volume15
Issue number1
DOIs
StatePublished - Jun 27 2014

Keywords

  • Bioinformatics workflow
  • Exon counts
  • Expressed single nucleotide variants
  • Fusion transcripts
  • Gene expression
  • RNA-Seq
  • RNA-Seq reports
  • Transcriptomic sequencing

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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