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Title: ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data

Abstract

This report presents the results from the 2016 ABRF Proteome Informatics Research Group (iPRG) study on proteoform inference and FDR estimation from bottom-up proteomics data. For this study, three replicate Q Exactive Orbitrap LC-MS/MS datasets were generated from each of four E. coli samples spiked with different equimolar mixtures of small recombinant proteins selected to mimic pairs of homologous proteins. Participants were given raw data and a sequence file, and asked to identify the proteins and provide estimates on the false discovery rate at the proteoform level. As part of this study, we tested a new submission system with a format validator running on a virtual private server (VPS) and allowed methods to be provided as executable R Markdown or IPython Notebooks. The task was perceived as difficult, and only eight unique submissions were received, though those who participated did well, with no one method performing best on all samples. However, none of the submissions included a complete Markdown or Notebook, even though examples were provided. Future iPRG studies need to be more successful in promoting and encouraging participation. The VPS and submission validator easily scale to much larger numbers of participants in these types of studies. The unique “ground-truth”more » dataset for proteoform identification generated for this study is now available to the research community, as are the server-side scripts for validating and managing submissions.« less

Authors:
; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1490328
Report Number(s):
PNNL-SA-133941
Journal ID: ISSN 1524-0215
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Biomolecular Techniques : JBT
Additional Journal Information:
Journal Volume: 29; Journal Issue: 2; Journal ID: ISSN 1524-0215
Country of Publication:
United States
Language:
English
Subject:
Proteomics, proteoforms, inference, false discovery rate, community discovery

Citation Formats

Lee, Joon-Yong, Choi, Hyungwon, Colangelo, Christopher M., Davis, Darryl, Hoopmann, Michael R., Käll, Lukas, Lam, Henry, Payne, Samuel H., Perez-Riverol, Yasset, The, Matthew, Wilson, Ryan, Weintraub, Susan T., and Palmblad, Magnus. ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data. United States: N. p., 2018. Web. doi:10.7171/jbt.18-2902-003.
Lee, Joon-Yong, Choi, Hyungwon, Colangelo, Christopher M., Davis, Darryl, Hoopmann, Michael R., Käll, Lukas, Lam, Henry, Payne, Samuel H., Perez-Riverol, Yasset, The, Matthew, Wilson, Ryan, Weintraub, Susan T., & Palmblad, Magnus. ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data. United States. doi:10.7171/jbt.18-2902-003.
Lee, Joon-Yong, Choi, Hyungwon, Colangelo, Christopher M., Davis, Darryl, Hoopmann, Michael R., Käll, Lukas, Lam, Henry, Payne, Samuel H., Perez-Riverol, Yasset, The, Matthew, Wilson, Ryan, Weintraub, Susan T., and Palmblad, Magnus. Sun . "ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data". United States. doi:10.7171/jbt.18-2902-003.
@article{osti_1490328,
title = {ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data},
author = {Lee, Joon-Yong and Choi, Hyungwon and Colangelo, Christopher M. and Davis, Darryl and Hoopmann, Michael R. and Käll, Lukas and Lam, Henry and Payne, Samuel H. and Perez-Riverol, Yasset and The, Matthew and Wilson, Ryan and Weintraub, Susan T. and Palmblad, Magnus},
abstractNote = {This report presents the results from the 2016 ABRF Proteome Informatics Research Group (iPRG) study on proteoform inference and FDR estimation from bottom-up proteomics data. For this study, three replicate Q Exactive Orbitrap LC-MS/MS datasets were generated from each of four E. coli samples spiked with different equimolar mixtures of small recombinant proteins selected to mimic pairs of homologous proteins. Participants were given raw data and a sequence file, and asked to identify the proteins and provide estimates on the false discovery rate at the proteoform level. As part of this study, we tested a new submission system with a format validator running on a virtual private server (VPS) and allowed methods to be provided as executable R Markdown or IPython Notebooks. The task was perceived as difficult, and only eight unique submissions were received, though those who participated did well, with no one method performing best on all samples. However, none of the submissions included a complete Markdown or Notebook, even though examples were provided. Future iPRG studies need to be more successful in promoting and encouraging participation. The VPS and submission validator easily scale to much larger numbers of participants in these types of studies. The unique “ground-truth” dataset for proteoform identification generated for this study is now available to the research community, as are the server-side scripts for validating and managing submissions.},
doi = {10.7171/jbt.18-2902-003},
journal = {Journal of Biomolecular Techniques : JBT},
issn = {1524-0215},
number = 2,
volume = 29,
place = {United States},
year = {2018},
month = {7}
}