Proteogenomic database construction driven from large scale RNA-seq data

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Amino Acid Sequence
  • Animals
  • Automation
  • Caenorhabditis elegans / genetics
  • Caenorhabditis elegans / metabolism*
  • Databases, Genetic*
  • Databases, Protein*
  • Genome*
  • Helminth Proteins / chemistry
  • Helminth Proteins / genetics
  • Helminth Proteins / metabolism
  • Molecular Sequence Data
  • Proteome*
  • Sequence Analysis, RNA*


  • Helminth Proteins
  • Proteome