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Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies.

ABSTRACT: 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.

SUBMITTER: Blakeley P

PROVIDER: S-EPMC3703792 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies.

Blakeley Paul P Overton Ian M IM Hubbard Simon J SJ

Journal of proteome research 20121015 11

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 cont ...[more]

PMID: 23025403

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Project description:BackgroundOver the past few years, information retrieval has become more and more professionalized, and information specialists are considered full members of a research team conducting systematic reviews. Research groups preparing systematic reviews and clinical practice guidelines have been the driving force in the development of search strategies, but open questions remain regarding the transparency of the development process and the available resources. An empirically guided approach to the development of a search strategy provides a way to increase transparency and efficiency.MethodsOur aim in this paper is to describe the empirically guided development process for search strategies as applied by the German Institute for Quality and Efficiency in Health Care (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen, or "IQWiG"). This strategy consists of the following steps: generation of a test set, as well as the development, validation and standardized documentation of the search strategy.ResultsWe illustrate our approach by means of an example, that is, a search for literature on brachytherapy in patients with prostate cancer. For this purpose, a test set was generated, including a total of 38 references from 3 systematic reviews. The development set for the generation of the strategy included 25 references. After application of textual analytic procedures, a strategy was developed that included all references in the development set. To test the search strategy on an independent set of references, the remaining 13 references in the test set (the validation set) were used. The validation set was also completely identified.DiscussionOur conclusion is that an objectively derived approach similar to that used in search filter development is a feasible way to develop and validate reliable search strategies. Besides creating high-quality strategies, the widespread application of this approach will result in a substantial increase in the transparency of the development process of search strategies.

Project description:The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstances-a problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ?20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/ .

Dataset Information

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies.

Publications

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies.

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