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ProteoDisco: A flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies.

ABSTRACT: We present an R-based open-source software termed ProteoDisco that allows for flexible incorporation of genomic variants, fusion-genes and (aberrant) transcriptomic variants from standardized formats into protein variant sequences. ProteoDisco allows for a flexible step-by-step workflow allowing for in-depth customization to suit a myriad of research approaches in the field of proteogenomics, on all organisms for which a reference genome and transcript annotations are available. ProteoDisco (R package version ≥ 1.0.0) is available on Bioconductor at https://doi.org/doi:10.18129/B9.bioc.ProteoDisco and from https://github.com/ErasmusMC-CCBC/ProteoDisco/. Supplementary table, figures and data files are available at Bioinformatics online.

SUBMITTER: van de Geer WS

PROVIDER: S-EPMC8826347 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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ProteoDisco: a flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies.

van de Geer Wesley S WS van Riet Job J van de Werken Harmen J G HJG

Bioinformatics (Oxford, England) 20220201 5

<h4>Summary</h4>We present an R-based open-source software termed ProteoDisco that allows for flexible incorporation of genomic variants, fusion genes and (aberrant) transcriptomic variants from standardized formats into protein variant sequences. ProteoDisco allows for a flexible step-by-step workflow allowing for in-depth customization to suit a myriad of research approaches in the field of proteogenomics, on all organisms for which a reference genome and transcript annotations are available.< ...[more]

PMID: 34864882

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Project description:Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five "incorrect" targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives.

Project description:BackgroundIn metabarcoding analyses, the taxonomic assignment is crucial to place sequencing data in biological and ecological contexts. This fundamental step depends on a reference database, which should have a good taxonomic coverage to avoid unassigned sequences. However, this goal is rarely achieved in many geographic regions and for several taxonomic groups. On the other hand, more is not necessarily better, as sequences in reference databases belonging to taxonomic groups out of the studied region/environment context might lead to false assignments.MethodsWe investigated the effect of using several subsets of a cytochrome c oxidase subunit I (COI) reference database on taxonomic assignment. Published metabarcoding sequences from the Mediterranean Sea were assigned to taxa using COInr, which is a comprehensive, non-redundant and recent database of COI sequences obtained both from BOLD and NCBI, and two of its subsets: (i) all sequences except insects (COInr-WO-Insecta), which represent the overwhelming majority of COInr database, but are irrelevant for marine samples, and (ii) all sequences from taxonomic families present in the Mediterranean Sea (COInr-Med). Four different algorithms for taxonomic assignment were employed in parallel to evaluate differences in their output and data consistency.ResultsThe reduction of the database to more specific custom subsets increased the number of unassigned sequences. Nevertheless, since most of them were incorrectly assigned by the less specific databases, this is a positive outcome. Moreover, the taxonomic resolution (the lowest taxonomic level to which a sequence is attributed) of several sequences tended to increase when using customized databases. These findings clearly indicated the need for customized databases adapted to each study. However, the very high proportion of unassigned sequences points to the need to enrich the local database with new barcodes specifically obtained from the studied region and/or taxonomic group. Including novel local barcodes to the COI database proved to be very profitable: by adding only 116 new barcodes sequenced in our laboratory, thus increasing the reference database by only 0.04%, we were able to improve the resolution for ca. 0.6-1% of the Amplicon Sequence Variants (ASVs).

Dataset Information

ProteoDisco: A flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies.

Publications

ProteoDisco: a flexible R approach to generate customized protein databases for extended search space of novel and variant proteins in proteogenomic studies.

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