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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
Published on: May 22, 2018
Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions.
Jainab Khatun1, Yanbao Yu, John A Wrobel
1College of Arts and Sciences, Boise State University, Boise, ID, USA. jainabkhatun@boisestate.edu
BMC Genomics
|March 2, 2013
Summary
Proteogenomic mapping using mass spectrometry identified novel protein-coding regions in the human genome. This approach complements existing genome annotation methods by revealing regions outside current gene models.
Area of Science:
- Genomics
- Proteomics
- Bioinformatics
Background:
- Proteogenomic mapping leverages mass spectrometry data to identify protein-coding genes.
- This method aids in discovering novel translational regions within the human genome.
- The study integrated with the Encyclopedia of DNA Elements (ENCODE) project.
Purpose of the Study:
- To apply proteogenomic mapping to identify potential protein-coding regions missing from the human genome.
- To generate proteogenomic tracks for the UCSC Genome Browser.
Main Methods:
- Generated approximately 1 million tandem mass (MS/MS) spectra from K562 and GM12878 cell lines.
- Mapped spectra against the UCSC hg19 human genome and GENCODE V7 annotations.
- Compared results from protein, transcript, and whole-genome searches to enhance confidence in novel findings.
Main Results:
- Identified 481 unique peptides solely through whole-genome proteogenomic mapping.
- At a 1% false discovery rate, identified 26,472 peptides from protein, 24,406 from transcript, and 13,128 from whole-genome searches.
- Discovered that approximately 4% of uniquely mapped peptides fell outside GENCODE V7 annotated exons.
Conclusions:
- Whole-genome proteogenomic mapping is a valuable complementary technique for genome annotation.
- This approach identified 15% more spectra compared to protein database searches alone.
- The proteogenomic mapping data are publicly available on the UCSC Genome Browser.
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