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TOPAZ: asymmetric suffix array neighbourhood search for massive protein databases.

ABSTRACT: Protein homology search is an important, yet time-consuming, step in everything from protein annotation to metagenomics. Its application, however, has become increasingly challenging, due to the exponential growth of protein databases. In order to perform homology search at the required scale, many methods have been proposed as alternatives to BLAST that make an explicit trade-off between sensitivity and speed. One such method, SANSparallel, uses a parallel implementation of the suffix array neighbourhood search (SANS) technique to achieve high speed and provides several modes to allow for greater sensitivity at the expense of performance.We present a new approach called asymmetric SANS together with scored seeds and an alternative suffix array ordering scheme called optimal substitution ordering. These techniques dramatically improve both the sensitivity and speed of the SANS approach. Our implementation, TOPAZ, is one of the top performing methods in terms of speed, sensitivity and scalability. In our benchmark, searching UniProtKB for homologous proteins to the Dickeya solani proteome, TOPAZ took less than 3 minutes to achieve a sensitivity of 0.84 compared to BLAST.Despite the trade-off homology search methods have to make between sensitivity and speed, TOPAZ stands out as one of the most sensitive and highest performance methods currently available.

SUBMITTER: Medlar A

PROVIDER: S-EPMC6069885 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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TOPAZ: asymmetric suffix array neighbourhood search for massive protein databases.

Medlar Alan A Holm Liisa L

BMC bioinformatics 20180731 1

<h4>Background</h4>Protein homology search is an important, yet time-consuming, step in everything from protein annotation to metagenomics. Its application, however, has become increasingly challenging, due to the exponential growth of protein databases. In order to perform homology search at the required scale, many methods have been proposed as alternatives to BLAST that make an explicit trade-off between sensitivity and speed. One such method, SANSparallel, uses a parallel implementation of t ...[more]

PMID: 30064374

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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.

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Dataset Information

TOPAZ: asymmetric suffix array neighbourhood search for massive protein databases.

Publications

TOPAZ: asymmetric suffix array neighbourhood search for massive protein databases.

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