Fast index based algorithms and software for matching position specific scoring matrices

Abstract

BackgroundIn biological sequence analysis, position specific scoring matrices (PSSMs) are widely used to represent sequence motifs in nucleotide as well as amino acid sequences. Searching with PSSMs in complete genomes or large sequence databases is a common, but computationally expensive task.ResultsWe present a new non-heuristic algorithm, called ESAsearch, to efficiently find matches of PSSMs in large databases. Our approach preprocesses the search space, e.g., a complete genome or a set of protein sequences, and builds an enhanced suffix array that is stored on file. This allows the searching of a database with a PSSM in sublinear expected time. Since ESAsearch benefits from small alphabets, we present a variant operating on sequences recoded according to a reduced alphabet. We also address the problem of non-comparable PSSM-scores by developing a method which allows the efficient computation of a matrix similarity threshold for a PSSM, given an E-value or a p-value. Our method is based on dynamic programming and, in contrast to other methods, it employs lazy evaluation of the dynamic programming matrix. We evaluated algorithm ESAsearch with nucleotide PSSMs and with amino acid PSSMs. Compared to the best previous methods, ESAsearch shows speedups of a factor between 17 and 275 for nucleotide PSSMs, and speedups up to factor 1.8 for amino acid PSSMs. Comparisons with the most widely used programs even show speedups by a factor of at least 3.8. Alphabet reduction yields an additional speedup factor of 2 on amino acid sequences compared to results achieved with the 20 symbol standard alphabet. The lazy evaluation method is also much faster than previous methods, with speedups of a factor between 3 and 330.ConclusionOur analysis of ESAsearch reveals sublinear runtime in the expected case, and linear runtime in the worst case for sequences not shorter than |A MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBamrtHrhAL1wy0L2yHvtyaeHbnfgDOvwBHrxAJfwnaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaWaaeGaeaaakeaaimaacqWFaeFqaaa@3821@|m + m - 1, where m is the length of the PSSM and A MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBamrtHrhAL1wy0L2yHvtyaeHbnfgDOvwBHrxAJfwnaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaWaaeGaeaaakeaaimaacqWFaeFqaaa@3821@ a finite alphabet. In practice, ESAsearch shows superior performance over the most widely used programs, especially for DNA sequences. The new algorithm for accurate on-the-fly calculations of thresholds has the potential to replace formerly used approximation approaches. Beyond the algorithmic contributions, we provide a robust, well documented, and easy to use software package, implementing the ideas and algorithms presented in this manuscript.

The GlxR regulon of the amino acid producer Corynebacterium glutamicum: Detection of the corynebacterial core regulon and integration into the transcriptional regulatory network model

“…Combining these data sets yielded a group of candidate genes that are both conserved in sequenced corynebacteria and most likely under transcriptional control by GlxR and its orthologs, respectively. The workflow is completed by performing electrophoretic mobility shift assays (EMSAs) for in vitro verification of detected binding sites in the model organism C. glutamicum ATCC 13032. raising sensitivity at the cost of diminished specificity and thereby increasing the number of detected motif instances (Beckstette et al, 2006). Subsequently, a bi-directional best blast analysis with an E-value threshold of 10 −5 was employed to identify potentially orthologous genes.…”

“…DNA binding sites were detected using the PoSSuMsearch algorithm (Beckstette et al, 2006) with all upstream regions of the organism, extracted from the genomic sequence as described previously for C. glutamicum (Kohl et al, 2008). The position weight matrix (PWM)-based model of the GlxR binding motif used in the search was derived from all verified binding sites in C. glutamicum ATCC 13032.…”

Identification and Characterization of VpsR and VpsT Binding Sites in Vibrio cholerae

“…Putative transcription factor-binding sites were identified using the PSSM search module of Biopython. The significance threshold for binding sites in the context of multiple-hypothesis testing was defined by computing the exact probability distributions for site scores under the PSSM and genomic background models with dynamic programming and controlling the rate of false-positive results by defining the probability of finding at least one false-positive result in a sequence of 350 bp (␣ 350 ϭ 0.01) (46,47). Comparative genomics analysis.…”

Fast profile matching algorithms — A survey

“…[39,44,51]), use a brute-force sliding window approach, and the profile matching problem is still regarded as a not yet satisfactorily well-solved problem in computational biology [20]. In recent years a bunch of advanced algorithms based on score properties [53], indexing data structures [6,7,13], Fast Fourier Transform [41], data compression [17], matrix partitioning [33], filtering algorithms [7,33,38], pattern matching [38], and superalphabet [38] have been proposed to reduce the expected time of computation. The aim of this paper is to survey these methods to give the reader an overview of the state of the art of the topic and possibly stimulate future research in the field.…”