Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage

ABSTRACT Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribosome profiling (ribo-seq) and proteomic data for Salmonella enteric...

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Autores principales:	Patrick Willems, Igor Fijalkowski, Petra Van Damme
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	Deinococcus radiodurans Salmonella alternative translation initiation bacterial genome (re)annotation chimeric spectra riboproteogenomics Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/d37629f750554a13b702882925d5bd97
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spelling	oai:doaj.org-article:d37629f750554a13b702882925d5bd972021-12-02T18:44:44ZLost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage10.1128/mSystems.00833-202379-5077https://doaj.org/article/d37629f750554a13b702882925d5bd972020-10-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00833-20https://doaj.org/toc/2379-5077ABSTRACT Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribosome profiling (ribo-seq) and proteomic data for Salmonella enterica serovar Typhimurium to identify unannotated proteins or alternative protein forms. This data analysis encompasses the searching of cofragmenting peptides and postprocessing with extended peptide-to-spectrum quality features, including comparison to predicted fragment ion intensities. When this strategy is applied, an enhanced proteome depth is achieved, as well as greater confidence for unannotated peptide hits. We demonstrate the general applicability of our pipeline by reanalyzing public Deinococcus radiodurans data sets. Taken together, our results show that systematic reanalysis using available prokaryotic (proteome) data sets holds great promise to assist in experimentally based genome annotation. IMPORTANCE Delineation of open reading frames (ORFs) causes persistent inconsistencies in prokaryote genome annotation. We demonstrate that by advanced (re)analysis of omics data, a higher proteome coverage and sensitive detection of unannotated ORFs can be achieved, which can be exploited for conditional bacterial genome (re)annotation, which is especially relevant in view of annotating the wealth of sequenced prokaryotic genomes obtained in recent years.Patrick WillemsIgor FijalkowskiPetra Van DammeAmerican Society for MicrobiologyarticleDeinococcus radioduransSalmonellaalternative translation initiationbacterial genome (re)annotationchimeric spectrariboproteogenomicsMicrobiologyQR1-502ENmSystems, Vol 5, Iss 5 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deinococcus radiodurans Salmonella alternative translation initiation bacterial genome (re)annotation chimeric spectra riboproteogenomics Microbiology QR1-502
spellingShingle	Deinococcus radiodurans Salmonella alternative translation initiation bacterial genome (re)annotation chimeric spectra riboproteogenomics Microbiology QR1-502 Patrick Willems Igor Fijalkowski Petra Van Damme Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage
description	ABSTRACT Prokaryotic genome annotation is heavily dependent on automated gene annotation pipelines that are prone to propagate errors and underestimate genome complexity. We describe an optimized proteogenomic workflow that uses ribosome profiling (ribo-seq) and proteomic data for Salmonella enterica serovar Typhimurium to identify unannotated proteins or alternative protein forms. This data analysis encompasses the searching of cofragmenting peptides and postprocessing with extended peptide-to-spectrum quality features, including comparison to predicted fragment ion intensities. When this strategy is applied, an enhanced proteome depth is achieved, as well as greater confidence for unannotated peptide hits. We demonstrate the general applicability of our pipeline by reanalyzing public Deinococcus radiodurans data sets. Taken together, our results show that systematic reanalysis using available prokaryotic (proteome) data sets holds great promise to assist in experimentally based genome annotation. IMPORTANCE Delineation of open reading frames (ORFs) causes persistent inconsistencies in prokaryote genome annotation. We demonstrate that by advanced (re)analysis of omics data, a higher proteome coverage and sensitive detection of unannotated ORFs can be achieved, which can be exploited for conditional bacterial genome (re)annotation, which is especially relevant in view of annotating the wealth of sequenced prokaryotic genomes obtained in recent years.
format	article
author	Patrick Willems Igor Fijalkowski Petra Van Damme
author_facet	Patrick Willems Igor Fijalkowski Petra Van Damme
author_sort	Patrick Willems
title	Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage
title_short	Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage
title_full	Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage
title_fullStr	Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage
title_full_unstemmed	Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage
title_sort	lost and found: re-searching and re-scoring proteomics data aids genome annotation and improves proteome coverage
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/d37629f750554a13b702882925d5bd97
work_keys_str_mv	AT patrickwillems lostandfoundresearchingandrescoringproteomicsdataaidsgenomeannotationandimprovesproteomecoverage AT igorfijalkowski lostandfoundresearchingandrescoringproteomicsdataaidsgenomeannotationandimprovesproteomecoverage AT petravandamme lostandfoundresearchingandrescoringproteomicsdataaidsgenomeannotationandimprovesproteomecoverage
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Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage

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