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J Proteome Res. 2014 Jan 3;13(1):21-8. doi: 10.1021/pr400294c. Epub 2013 Jul 17.

Proteogenomic database construction driven from large scale RNA-seq data.

Author information

1
Department of Electrical and Computing Engineering, ¶Department of Bioinformatics and Systems Biology, and §Department of Computer Science, University of California, San Diego , La Jolla, California 92093, United States.

Abstract

The advent of inexpensive RNA-seq technologies and other deep sequencing technologies for RNA has the promise to radically improve genomic annotation, providing information on transcribed regions and splicing events in a variety of cellular conditions. Using MS-based proteogenomics, many of these events can be confirmed directly at the protein level. However, the integration of large amounts of redundant RNA-seq data and mass spectrometry data poses a challenging problem. Our paper addresses this by construction of a compact database that contains all useful information expressed in RNA-seq reads. Applying our method to cumulative C. elegans data reduced 496.2 GB of aligned RNA-seq SAM files to 410 MB of splice graph database written in FASTA format. This corresponds to 1000× compression of data size, without loss of sensitivity. We performed a proteogenomics study using the custom data set, using a completely automated pipeline, and identified a total of 4044 novel events, including 215 novel genes, 808 novel exons, 12 alternative splicings, 618 gene-boundary corrections, 245 exon-boundary changes, 938 frame shifts, 1166 reverse strands, and 42 translated UTRs. Our results highlight the usefulness of transcript + proteomic integration for improved genome annotations.

PMID:
23802565
PMCID:
PMC4034692
DOI:
10.1021/pr400294c
[Indexed for MEDLINE]
Free PMC Article

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