MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing

By Krishna Rani Kalari, Asha A. Nair, Jaysheel D. Bhavsar, Daniel R. O'Brien, Jaime I. Dávila, Matthew A. Bockol, Jinfu J. Nie, Xiaojia Tang, Saurabh Baheti, Jay B. Doughty and 5 more. BMC Bioinformatics, 2014

358 citations recorded by the source corpus.

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/.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c8ec7f59ad713f83bf0e20087a210ab9477964b0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

  1. D. Craig
  2. J. Carpten
  3. Kendall Van Keuren-Jensen
  4. S. Byron
  5. D. Engelthaler

Citations in the corpus