Introduction

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.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.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/.

Publications

  1. MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing.
    Cite this
    Kalari KR, Nair AA, Bhavsar JD, O'Brien DR, Davila JI, Bockol MA, Nie J, Tang X, Baheti S, Doughty JB, Middha S, Sicotte H, Thompson AE, Asmann YW, Kocher JP, 2014-06-01 - BMC bioinformatics

Credits

  1. Krishna R Kalari
    Developer

  2. Asha A Nair
    Developer

  3. Jaysheel D Bhavsar
    Developer

  4. Daniel R O'Brien
    Developer

  5. Jaime I Davila
    Developer

  6. Matthew A Bockol
    Developer

  7. Jinfu Nie
    Developer

  8. Xiaojia Tang
    Developer

  9. Saurabh Baheti
    Developer

  10. Jay B Doughty
    Developer

  11. Sumit Middha
    Developer

  12. Hugues Sicotte
    Developer

  13. Aubrey E Thompson
    Developer

  14. Yan W Asmann
    Developer

  15. Jean-Pierre A Kocher
    Investigator

    Department of Health Sciences Research, Mayo Clinic, United States of America

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Summary
AccessionBT006379
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesPerl, R
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByJean-Pierre A Kocher