MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing

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 co...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 15; no. 1; p. 224
Main Authors:	Kalari, Krishna R, Nair, Asha A, Bhavsar, Jaysheel D, O’Brien, Daniel R, Davila, Jaime I, Bockol, Matthew A, Nie, Jinfu, Tang, Xiaojia, Baheti, Saurabh, Doughty, Jay B, Middha, Sumit, Sicotte, Hugues, Thompson, Aubrey E, Asmann, Yan W, Kocher, Jean-Pierre A
Format:	Journal Article
Language:	English
Published:	London BioMed Central 27.06.2014 BioMed Central Ltd Springer Nature B.V
Subjects:	Algorithms Analysis Base Sequence Bioinformatics Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Exons - genetics Gene Expression Profiling Genes Genomics - methods Health Facilities High-Throughput Nucleotide Sequencing - methods Humans Life Sciences Microarrays Research projects RNA sequencing Sequence Analysis, RNA - methods Software Transcriptome analysis Transcriptomic sequencing Fusion transcripts RNA-Seq reports RNA-Seq Bioinformatics workflow Exon counts Gene expression Expressed single nucleotide variants
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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Summary:	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/ .
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/1471-2105-15-224