iTriplet, a rule-based nucleic acid sequence motif finder

Abstract

BackgroundWith the advent of high throughput sequencing techniques, large amounts of sequencing data are readily available for analysis. Natural biological signals are intrinsically highly variable making their complete identification a computationally challenging problem. Many attempts in using statistical or combinatorial approaches have been made with great success in the past. However, identifying highly degenerate and long (>20 nucleotides) motifs still remains an unmet challenge as high degeneracy will diminish statistical significance of biological signals and increasing motif size will cause combinatorial explosion. In this report, we present a novel rule-based method that is focused on finding degenerate and long motifs. Our proposed method, named iTriplet, avoids costly enumeration present in existing combinatorial methods and is amenable to parallel processing.ResultsWe have conducted a comprehensive assessment on the performance and sensitivity-specificity of iTriplet in analyzing artificial and real biological sequences in various genomic regions. The results show that iTriplet is able to solve challenging cases. Furthermore we have confirmed the utility of iTriplet by showing it accurately predicts polyA-site-related motifs using a dual Luciferase reporter assay.ConclusioniTriplet is a novel rule-based combinatorial or enumerative motif finding method that is able to process highly degenerate and long motifs that have resisted analysis by other methods. In addition, iTriplet is distinguished from other methods of the same family by its parallelizability, which allows it to leverage the power of today's readily available high-performance computing systems.

PairMotif+: A Fast and Effective Algorithm for De Novo Motif Discovery in DNA sequences

“…6 shows sequence logos 34 of the predicted motifs, which graphically shows the degree of motif conservation measured by relative entropy. Note that, many existing recognition algorithms 1, 3-5, 18, 29 also test their validity on these data sets. Since all of these algorithms (including PairMotif+) show a good performance on these data sets, here we do not make comparisons.…”

“…For a given motif length l , the larger the degenerate positions d , the more difficult it is to identify the planted ( l , d ) motif in input sequences. Specifically, some researchers 3, 4 use 2 d -neighborhood probability (i.e., the probability that the Hamming distance between two random l -mers is not larger than 2 d ) to measure the difficulty of solving different PMS instances, since it is a good indicator to reflect the degree of degeneracy of PMS instances 3.…”

Reference sequence selection for motif searches

“…Specifically, they generate candidate motifs by using all possible h-tuple T = (x), Xz ... Xh) composed of h 1-length strings coming from h distinct reference sequences. In existing pattern-driven PMS algorithms, h is 1 for PMSP [5] and PMSPrune [6]; h is 2 for PairMotif [7], qPMS7 [8] and TravStrR [9]; h is 3 for iTriplet [10] and PMS5 [11]; for PMS8 [12] and qPMS9 [13], h is greater than or equal to 3 and self adaptive in dealing with different PMS problem instances. Moreover, these algorithms use k = t -q + h reference sequences to generate candidate motifs, ensuring that there exists at least one h-tuple T so that each I-length string in T is a moti f instance.…”

“…For the exact algorithms proposed in earlier years, such as WINNOWER [3], their search space is composed of (n -I + 1 Y possible alignments of motif instances. Tn recent years, the exact algorithms verifY all the I-length patterns in the O(III ' ) search space, and they are called the pattern-driven PMS algorithms [5][6][7][8][9][10][11][12][13].…”

An Efficient Exact Algorithm for the Motif Stem Search Problem over Large Alphabets

“…They take all string patterns of length l over S as candidate motifs, and output the patterns that can span all input sequences. Typical patterndriven algorithms aim to reduce candidate motifs through various means [11], [12], [13], [14], [15], [16], [17], [18]. Some other pattern-driven algorithms represent the input sequences as a suffix tree to accelerate the verification of candidate motifs [19], [20], [21].…”