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MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms. | LitMetric

MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms.

Bioinformatics

Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany.

Published: June 2015

AI Article Synopsis

Article Abstract

Unlabelled: Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes.

Availability And Implementation: MSProGene is written in Java and Python. It is open source and available at http://sourceforge.net/projects/msprogene/.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765881PMC
http://dx.doi.org/10.1093/bioinformatics/btv236DOI Listing

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