A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations
Abstract Background Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins bec...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2021-05-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-021-04159-8 |
id |
doaj-ef4f88052a0946e2b9f80c64ec97b611 |
---|---|
record_format |
Article |
spelling |
doaj-ef4f88052a0946e2b9f80c64ec97b6112021-05-30T11:52:53ZengBMCBMC Bioinformatics1471-21052021-05-0122112010.1186/s12859-021-04159-8A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locationsJohn Anders0Hannes Petruschke1Nico Jehmlich2Sven-Bastiaan Haange3Martin von Bergen4Peter F Stadler5Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität LeipzigDepartment of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZDepartment of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZDepartment of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZInstitute of Biochemistry, Faculty of Life Sciences, University of LeipzigGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and Competence Center for Scalable Data Services and Solutions Dresden-Leipzig and Leipzig Research Center for Civilization Diseases, University LeipzigAbstract Background Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions. Results We observe that number and quality of the peptide-spectrum matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that have previously been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence at the proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short (< 100 AA) and are most likely bona fide novel proteins. Conclusions The aggregation of PSM quality information for predicted ORFs provides a robust and efficient method to identify novel proteins in proteomics data. The workflow is in particular capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration of transcriptomics data and other sources of genome-level information.https://doi.org/10.1186/s12859-021-04159-8Small proteinsMetaproteogenomicsPeptide-spectrum matchesMicrobial communitities |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
John Anders Hannes Petruschke Nico Jehmlich Sven-Bastiaan Haange Martin von Bergen Peter F Stadler |
spellingShingle |
John Anders Hannes Petruschke Nico Jehmlich Sven-Bastiaan Haange Martin von Bergen Peter F Stadler A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations BMC Bioinformatics Small proteins Metaproteogenomics Peptide-spectrum matches Microbial communitities |
author_facet |
John Anders Hannes Petruschke Nico Jehmlich Sven-Bastiaan Haange Martin von Bergen Peter F Stadler |
author_sort |
John Anders |
title |
A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations |
title_short |
A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations |
title_full |
A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations |
title_fullStr |
A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations |
title_full_unstemmed |
A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations |
title_sort |
workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2021-05-01 |
description |
Abstract Background Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions. Results We observe that number and quality of the peptide-spectrum matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that have previously been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence at the proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short (< 100 AA) and are most likely bona fide novel proteins. Conclusions The aggregation of PSM quality information for predicted ORFs provides a robust and efficient method to identify novel proteins in proteomics data. The workflow is in particular capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration of transcriptomics data and other sources of genome-level information. |
topic |
Small proteins Metaproteogenomics Peptide-spectrum matches Microbial communitities |
url |
https://doi.org/10.1186/s12859-021-04159-8 |
work_keys_str_mv |
AT johnanders aworkflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT hannespetruschke aworkflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT nicojehmlich aworkflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT svenbastiaanhaange aworkflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT martinvonbergen aworkflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT peterfstadler aworkflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT johnanders workflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT hannespetruschke workflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT nicojehmlich workflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT svenbastiaanhaange workflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT martinvonbergen workflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations AT peterfstadler workflowtoidentifynovelproteinsbasedonthedirectmappingofpeptidespectrummatchestogenomiclocations |
_version_ |
1721419988259045376 |