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

Abstract

The planted (l, d) motif search is one of the most widely studied problems in bioinformatics, which plays an important role in the identification of transcription factor binding sites in DNA sequences. However, it is still a challenging task to identify highly degenerate motifs, since current algorithms either output the exact results with a high computational cost or accomplish the computation in a short time but very often fall into a local optimum. In order to make a better trade-off between accuracy and efficiency, we propose a new pattern-driven algorithm, named PairMotif+. At first, some pairs of l-mers are extracted from input sequences according to probabilistic analysis and statistical method so that one or more pairs of motif instances are included in them. Then an approximate strategy for refining pairs of l-mers with high accuracy is adopted in order to avoid the verification of most candidate motifs. Experimental results on the simulated data show that PairMotif+ can solve various (l, d) problems within an hour on a PC with 2.67 GHz processor, and has a better identification accuracy than the compared algorithms MEME, AlignACE and VINE. Also, the validity of the proposed algorithm is tested on multiple real data sets.

PairMotifChIP: A Fast Algorithm for Discovery of Patterns Conserved in Large ChIP-seq Data Sets

“…We select four compared algorithms: MEME-ChIP [23], F-motif [18], PairMotif+ [13], and qPMS9 [16]. MEME-ChIP is a widely used motif discovery algorithm for ChIP-seq data based on PWM.…”

“…In our previous work, PairMotif [12] and PairMotif+ [13], we mainly process promoter sequences, which correspond to a small t . The basic idea is to extract some pairs of l -mers in the input, making them contain at least one pair of motif instances, and then refine each pair of l -mers to get motifs.…”

“…We calculate it by combining the methods [13, 22] for evaluating the following probabilities. One is the probability that the Hamming distance between a motif m and a random motif instance m ′ is i  (0 ≤ i ≤ d ), denoted by Pr( d H ( m , m ′) = i ).…”

“…By the theorem of total probability, p k ′ is evaluated using (10) where the value of P ( d H ( m 1 , m 2 ) ≤ k ∣〈 a , b 〉) is calculated in terms of the actual situation [13]. $\begin{array}{c}T3p_k^{\prime }\\ =\underset{\mathrm{normal0}\le a,b\le d}{{\displaystyle false\sum }}\mathrm{P}\mathrm{r}\left(〈a,b〉\right)\times \mathrm{P}\mathrm{r}\left(d_H\left(m_{\mathrm{1}},m_{\mathrm{2}}\right)\le k\hspace{0.17em}\mid \hspace{0.17em}〈a,b〉\right).\end{array}$ …”

“…Currently, there are a lot of motif discovery algorithms to deal with small-scale data sets, such as Weeder [11], PairMotif [12], PairMotif+ [13], MEME [14], PMS8 [15], and qPMS9 [16]; for more algorithms, refer to [7, 17]. Because of high time or space complexity, these algorithms cannot be used for motif discovery in high-throughput sequencing data sets directly.…”