Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions

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  • Khatun, Jainab
    • Other Affiliation: College of Arts and Sciences, Boise State University, Boise, ID, USA
  • Yu, Yanbao
    • Affiliation: School of Medicine, Department of Biochemistry and Biophysics
  • Wrobel, John A
    • Affiliation: School of Medicine, Department of Biochemistry and Biophysics
  • Risk, Brian A
    • Other Affiliation: College of Arts and Sciences, Boise State University, Boise, ID, USA
  • Gunawardena, Harsha P
    • Affiliation: School of Medicine, Integrative Program for Biological and Genome Sciences
  • Secrest, Ashley
    • Other Affiliation: College of Arts and Sciences, Boise State University, Boise, ID, USA
  • Spitzer, Wendy J
    • Other Affiliation: College of Arts and Sciences, Boise State University, Boise, ID, USA
  • Xie, Ling
    • Affiliation: School of Medicine, Department of Biochemistry and Biophysics
  • Wang, Li
    • Affiliation: School of Medicine, Integrative Program for Biological and Genome Sciences
  • Chen, Xian
    • Affiliation: School of Medicine, Department of Biochemistry and Biophysics
  • Giddings, Morgan C.
    • Affiliation: School of Medicine, Department of Biochemistry and Biophysics
    • Other Affiliation: College of Arts and Sciences, Boise State University, Boise, ID, USA
Abstract
  • Abstract Background Proteogenomic mapping is an approach that uses mass spectrometry data from proteins to directly map protein-coding genes and could aid in locating translational regions in the human genome. In concert with the ENcyclopedia of DNA Elements (ENCODE) project, we applied proteogenomic mapping to produce proteogenomic tracks for the UCSC Genome Browser, to explore which putative translational regions may be missing from the human genome. Results We generated ~1 million high-resolution tandem mass (MS/MS) spectra for Tier 1 ENCODE cell lines K562 and GM12878 and mapped them against the UCSC hg19 human genome, and the GENCODE V7 annotated protein and transcript sets. We then compared the results from the three searches to identify the best-matching peptide for each MS/MS spectrum, thereby increasing the confidence of the putative new protein-coding regions found via the whole genome search. At a 1% false discovery rate, we identified 26,472, 24,406, and 13,128 peptides from the protein, transcript, and whole genome searches, respectively; of these, 481 were found solely via the whole genome search. The proteogenomic mapping data are available on the UCSC Genome Browser at http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeUncBsuProt. Conclusions The whole genome search revealed that ~4% of the uniquely mapping identified peptides were located outside GENCODE V7 annotated exons. The comparison of the results from the disparate searches also identified 15% more spectra than would have been found solely from a protein database search. Therefore, whole genome proteogenomic mapping is a complementary method for genome annotation when performed in conjunction with other searches.
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  • Article
Rights statement
  • In Copyright
Rights holder
  • Jainab Khatun et al.; licensee BioMed Central Ltd.
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Journal title
  • BMC Genomics
Journal volume
  • 14
Journal issue
  • 1
Page start
  • 141
Language
  • English
Is the article or chapter peer-reviewed?
  • Yes
ISSN
  • 1471-2164
Bibliographic citation
  • BMC Genomics. 2013 Feb 28;14(1):141
Publisher
  • BioMed Central Ltd
Access right
  • Open Access
Date uploaded
  • March 25, 2013

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