A proteogenomics workflow to uncover the world of small proteins in<i>Staphylococcus aureus</i>

Fuchs, Stephan; Kucklick, Martin; Lehmann, Erik; Beckmann, Alexander; Wilkens, Maya; Kolte, Baban; Mustafayeva, Ayten; Ludwig, Tobias; Diwo, Maurice; Wissing, Josef; Jaensch, L.; Ahrens, Christian H.; Ignatova, Zoya; Engelmann, Susanne

doi:10.1101/2020.05.25.114132

Cited by 3 publications

(2 citation statements)

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“…This results in much larger 6frame databases and thus a (moderate) reduction of sensitivity, but completely avoids all annotation-related biases [17][18][19]. With a focus on sProteins, it is also possible to extend annotations with additional predictions of (short) ORFs with high coding potential [20]. Already two decades ago EST data have been used to predict novel isoforms to allow the identification of proteins arising from splice variants [21].…”

Section: Introductionmentioning

confidence: 99%

Direct mapping of Peptide-to-Spectra-Matches to genome information facilitates qualifying proteomics information

Anders¹,

Petruschke²,

Jehmlich³

et al. 2021

Preprint

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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-to-Spectra-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 previously have been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence in 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 also capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration with transcriptomics data and other source of genome-level information.

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Section: Introductionmentioning

confidence: 99%

Direct mapping of Peptide-to-Spectra-Matches to genome information facilitates qualifying proteomics information

Anders¹,

Petruschke²,

Jehmlich³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This results in much larger 6frame databases and thus a (moderate) reduction of sensitivity, but completely avoids all annotation-related biases [17][18][19]. With a focus on sProteins, it is also possible to extend annotations with additional predictions of (short) ORFs with high coding potential [20]. Already two decades ago expressed sequence tag (EST) data were used to predict novel isoforms to allow the identification of proteins arising from splice variants [21].…”

Section: Introductionmentioning

confidence: 99%

A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations

et al. 2021

View full text Add to dashboard Cite

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.

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Peptidomics and proteogenomics: background, challenges and future needs

Vitorino

Choudhury

Guedes

et al. 2021

Expert Review of Proteomics

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A proteogenomics workflow to uncover the world of small proteins inStaphylococcus aureus

Cited by 3 publications

References 61 publications

Direct mapping of Peptide-to-Spectra-Matches to genome information facilitates qualifying proteomics information

Direct mapping of Peptide-to-Spectra-Matches to genome information facilitates qualifying proteomics information

A workflow to identify novel proteins based on the direct mapping of peptide-spectrum-matches to genomic locations

Peptidomics and proteogenomics: background, challenges and future needs

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