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PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration

Jeroen Crappé (UGent) , Elvis Ndah (UGent) , Alexander Koch (UGent) , Sandra Steyaert (UGent) , Daria Fijalkowska (UGent) , Sarah De Keulenaer (UGent) , Ellen De Meester (UGent) , Tim De Meyer (UGent) , Wim Van Criekinge (UGent) , Petra Van Damme (UGent) , et al.
(2015) NUCLEIC ACIDS RESEARCH. 43(5).
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Abstract
An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5'-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use.
Keywords
COMPLEXITY, PREDICTION, CELLS, DISCOVERY, IDENTIFICATION, TRANSLATION, MASS-SPECTROMETRY, PROVIDES EVIDENCE, LARGE NONCODING RNAS, SPECTROMETRY-BASED PROTEIN

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Citation

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MLA
Crappé, Jeroen, et al. “PROTEOFORMER: Deep Proteome Coverage through Ribosome Profiling and MS Integration.” NUCLEIC ACIDS RESEARCH, vol. 43, no. 5, 2015, doi:10.1093/nar/gku1283.
APA
Crappé, J., Ndah, E., Koch, A., Steyaert, S., Fijalkowska, D., De Keulenaer, S., … Menschaert, G. (2015). PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration. NUCLEIC ACIDS RESEARCH, 43(5). https://doi.org/10.1093/nar/gku1283
Chicago author-date
Crappé, Jeroen, Elvis Ndah, Alexander Koch, Sandra Steyaert, Daria Fijalkowska, Sarah De Keulenaer, Ellen De Meester, et al. 2015. “PROTEOFORMER: Deep Proteome Coverage through Ribosome Profiling and MS Integration.” NUCLEIC ACIDS RESEARCH 43 (5). https://doi.org/10.1093/nar/gku1283.
Chicago author-date (all authors)
Crappé, Jeroen, Elvis Ndah, Alexander Koch, Sandra Steyaert, Daria Fijalkowska, Sarah De Keulenaer, Ellen De Meester, Tim De Meyer, Wim Van Criekinge, Petra Van Damme, and Gerben Menschaert. 2015. “PROTEOFORMER: Deep Proteome Coverage through Ribosome Profiling and MS Integration.” NUCLEIC ACIDS RESEARCH 43 (5). doi:10.1093/nar/gku1283.
Vancouver
1.
Crappé J, Ndah E, Koch A, Steyaert S, Fijalkowska D, De Keulenaer S, et al. PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration. NUCLEIC ACIDS RESEARCH. 2015;43(5).
IEEE
[1]
J. Crappé et al., “PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration,” NUCLEIC ACIDS RESEARCH, vol. 43, no. 5, 2015.
@article{5833451,
  abstract     = {{An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5'-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use.}},
  articleno    = {{e29}},
  author       = {{Crappé, Jeroen and Ndah, Elvis and Koch, Alexander and Steyaert, Sandra and Fijalkowska, Daria and De Keulenaer, Sarah and De Meester, Ellen and De Meyer, Tim and Van Criekinge, Wim and Van Damme, Petra and Menschaert, Gerben}},
  issn         = {{0305-1048}},
  journal      = {{NUCLEIC ACIDS RESEARCH}},
  keywords     = {{COMPLEXITY,PREDICTION,CELLS,DISCOVERY,IDENTIFICATION,TRANSLATION,MASS-SPECTROMETRY,PROVIDES EVIDENCE,LARGE NONCODING RNAS,SPECTROMETRY-BASED PROTEIN}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{10}},
  title        = {{PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration}},
  url          = {{http://dx.doi.org/10.1093/nar/gku1283}},
  volume       = {{43}},
  year         = {{2015}},
}
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