PeptidePicker: A scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments
One challenge in Multiple Reaction Monitoring (MRM)-based proteomics is to select the most appropriate surrogate peptides to represent a target protein. We present here a software package to automatically generate these most appropriate surrogate peptides for an LC/MRM–MS analysis. Our method integr...
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Published in | Journal of proteomics Vol. 106; pp. 151 - 161 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
25.06.2014
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Subjects | |
Online Access | Get full text |
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Abstract | One challenge in Multiple Reaction Monitoring (MRM)-based proteomics is to select the most appropriate surrogate peptides to represent a target protein. We present here a software package to automatically generate these most appropriate surrogate peptides for an LC/MRM–MS analysis. Our method integrates information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM which is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our knowledge in choosing the best candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it previously has been observed. The modularity of the workflow allows further extension and additional selection criteria to be incorporated. We have developed a simple Web interface where the researcher provides the protein accession number, the subject organism, and peptide-specific options. Currently, the software is designed for human and mouse proteomes, but additional species can be easily be added. Our software improved the peptide selection by eliminating human error, considering multiple data sources and all of the isoforms of the protein, and resulted in faster peptide selection — approximately 50 proteins per hour compared to 8 per day.
Compiling a list of optimal surrogate peptides for target proteins to be analyzed by LC/MRM–MS has been a cumbersome process, in which expert researchers retrieved information from different online repositories and used their own reasoning to find the most appropriate peptides. Our scientific workflow automates this process by integrating information from different data sources including UniProt, Global Proteome Machine, NCBI's dbSNP, and PeptideAtlas, simulating the researchers' reasoning, and incorporating their knowledge of how to select the best proteotypic peptides for an MRM analysis. The developed software can help to standardize the selection of peptides, eliminate human error, and increase productivity.
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•A scientific workflow for automated peptide selection for MRM assays is presented.•The software integrates data from multiple online repositories with expert knowledge.•Our automatic workflow runs faster than manual selection.•Our software resulted in more possible peptides, and pointed out human errors.•The software is modular and extendable to meet the needs of different laboratories. |
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AbstractList | One challenge in Multiple Reaction Monitoring (MRM)-based proteomics is to select the most appropriate surrogate peptides to represent a target protein. We present here a software package to automatically generate these most appropriate surrogate peptides for an LC/MRM–MS analysis. Our method integrates information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM which is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our knowledge in choosing the best candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it previously has been observed. The modularity of the workflow allows further extension and additional selection criteria to be incorporated. We have developed a simple Web interface where the researcher provides the protein accession number, the subject organism, and peptide-specific options. Currently, the software is designed for human and mouse proteomes, but additional species can be easily be added. Our software improved the peptide selection by eliminating human error, considering multiple data sources and all of the isoforms of the protein, and resulted in faster peptide selection — approximately 50 proteins per hour compared to 8 per day.
Compiling a list of optimal surrogate peptides for target proteins to be analyzed by LC/MRM–MS has been a cumbersome process, in which expert researchers retrieved information from different online repositories and used their own reasoning to find the most appropriate peptides. Our scientific workflow automates this process by integrating information from different data sources including UniProt, Global Proteome Machine, NCBI's dbSNP, and PeptideAtlas, simulating the researchers' reasoning, and incorporating their knowledge of how to select the best proteotypic peptides for an MRM analysis. The developed software can help to standardize the selection of peptides, eliminate human error, and increase productivity.
[Display omitted]
•A scientific workflow for automated peptide selection for MRM assays is presented.•The software integrates data from multiple online repositories with expert knowledge.•Our automatic workflow runs faster than manual selection.•Our software resulted in more possible peptides, and pointed out human errors.•The software is modular and extendable to meet the needs of different laboratories. One challenge in Multiple Reaction Monitoring (MRM)-based proteomics is to select the most appropriate surrogate peptides to represent a target protein. We present here a software package to automatically generate these most appropriate surrogate peptides for an LC/MRM–MS analysis. Our method integrates information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM which is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our knowledge in choosing the best candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it previously has been observed. The modularity of the workflow allows further extension and additional selection criteria to be incorporated. We have developed a simple Web interface where the researcher provides the protein accession number, the subject organism, and peptide-specific options. Currently, the software is designed for human and mouse proteomes, but additional species can be easily be added. Our software improved the peptide selection by eliminating human error, considering multiple data sources and all of the isoforms of the protein, and resulted in faster peptide selection — approximately 50 proteins per hour compared to 8 per day.Compiling a list of optimal surrogate peptides for target proteins to be analyzed by LC/MRM–MS has been a cumbersome process, in which expert researchers retrieved information from different online repositories and used their own reasoning to find the most appropriate peptides. Our scientific workflow automates this process by integrating information from different data sources including UniProt, Global Proteome Machine, NCBI's dbSNP, and PeptideAtlas, simulating the researchers' reasoning, and incorporating their knowledge of how to select the best proteotypic peptides for an MRM analysis. The developed software can help to standardize the selection of peptides, eliminate human error, and increase productivity. One challenge in Multiple Reaction Monitoring (MRM)-based proteomics is to select the most appropriate surrogate peptides to represent a target protein. We present here a software package to automatically generate these most appropriate surrogate peptides for an LC/MRM-MS analysis. Our method integrates information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM which is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our knowledge in choosing the best candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it previously has been observed. The modularity of the workflow allows further extension and additional selection criteria to be incorporated. We have developed a simple Web interface where the researcher provides the protein accession number, the subject organism, and peptide-specific options. Currently, the software is designed for human and mouse proteomes, but additional species can be easily be added. Our software improved the peptide selection by eliminating human error, considering multiple data sources and all of the isoforms of the protein, and resulted in faster peptide selection - approximately 50 proteins per hour compared to 8 per day. Compiling a list of optimal surrogate peptides for target proteins to be analyzed by LC/MRM-MS has been a cumbersome process, in which expert researchers retrieved information from different online repositories and used their own reasoning to find the most appropriate peptides. Our scientific workflow automates this process by integrating information from different data sources including UniProt, Global Proteome Machine, NCBI's dbSNP, and PeptideAtlas, simulating the researchers' reasoning, and incorporating their knowledge of how to select the best proteotypic peptides for an MRM analysis. The developed software can help to standardize the selection of peptides, eliminate human error, and increase productivity. |
Author | Deelder, André M. Borchers, Christoph H. Smith, Derek S. Mohammed, Yassene Palmblad, Magnus Domański, Dominik Jackson, Angela M. |
Author_xml | – sequence: 1 givenname: Yassene surname: Mohammed fullname: Mohammed, Yassene organization: University of Victoria — Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada – sequence: 2 givenname: Dominik surname: Domański fullname: Domański, Dominik organization: Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland – sequence: 3 givenname: Angela M. surname: Jackson fullname: Jackson, Angela M. organization: University of Victoria — Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada – sequence: 4 givenname: Derek S. surname: Smith fullname: Smith, Derek S. organization: University of Victoria — Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada – sequence: 5 givenname: André M. surname: Deelder fullname: Deelder, André M. organization: Center for Proteomics and Metabolomics, Leiden University Medical Center, The Netherlands – sequence: 6 givenname: Magnus surname: Palmblad fullname: Palmblad, Magnus organization: Center for Proteomics and Metabolomics, Leiden University Medical Center, The Netherlands – sequence: 7 givenname: Christoph H. surname: Borchers fullname: Borchers, Christoph H. email: christoph@proteincentre.com organization: University of Victoria — Genome British Columbia Proteomics Centre, University of Victoria, Victoria, BC V8Z7X8, Canada |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24769191$$D View this record in MEDLINE/PubMed |
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SubjectTerms | Algorithms Animals Computational Biology - methods computer software Data integration Databases, Protein Humans Internet Mass Spectrometry Mice Models, Statistical monitoring MRM Peptide selection peptides Peptides - chemistry Programming Languages protein isoforms proteins Proteome proteomics Proteomics - methods Reproducibility of Results Scientific workflow Software SRM Targeted proteomics Trypsin - chemistry User-Computer Interface Workflow |
Title | PeptidePicker: A scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments |
URI | https://dx.doi.org/10.1016/j.jprot.2014.04.018 https://www.ncbi.nlm.nih.gov/pubmed/24769191 https://www.proquest.com/docview/2000162590 |
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