A New Exhaustive Method and Strategy for Finding Motifs in ChIP-Enriched Regions

Abstract

ChIP-seq, which combines chromatin immunoprecipitation (ChIP) with next-generation parallel sequencing, allows for the genome-wide identification of protein-DNA interactions. This technology poses new challenges for the development of novel motif-finding algorithms and methods for determining exact protein-DNA binding sites from ChIP-enriched sequencing data. State-of-the-art heuristic, exhaustive search algorithms have limited application for the identification of short (, ) motifs (, ) contained in ChIP-enriched regions. In this work we have developed a more powerful exhaustive method (FMotif) for finding long (, ) motifs in DNA sequences. In conjunction with our method, we have adopted a simple ChIP-enriched sampling strategy for finding these motifs in large-scale ChIP-enriched regions. Empirical studies on synthetic samples and applications using several ChIP data sets including 16 TF (transcription factor) ChIP-seq data sets and five TF ChIP-exo data sets have demonstrated that our proposed method is capable of finding these motifs with high efficiency and accuracy. The source code for FMotif is available at http://211.71.76.45/FMotif/.

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.…”

“…The algorithms in the first category represent motifs as words. Some of these algorithms, such as F-motif [18] and weeder2 [19], use pattern-driven ideas. They exhaustively verify all possible strings of the motif length over the DNA alphabet and then output the strings that satisfy specified motif property.…”

An Intuitive Wizard to Identify Pattern in Similarity Expressed Gene Set of Rice

“…Few other algorithms like F-motif [16] used words to search for motifs instead of character wise search. The algorithm like MCES [17] adopted the process of mining the substrings in input sequences with high occurrence frequency and then combined them as motifs.…”

An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data

“…More accurate binding regions (peaks) can be derived from ChIP-seq experiments, thus leading to more reliable prediction performance [9]. However, the peaks detected from ChIP-seq data can be up to a few hundred base pairs, while the documented cis-regulatory motifs are usually only as long as 8-20 bp [74]. Therefore, an ab initio motif discovery method is still indispensable to (i) identify the accurate binding sites from these ChIP-seq peaks, and (ii) build conserved motif profiles for further study in transcriptional regulation.…”

“…MEME and WEEDER [75], cannot be directly used on ChIPseq peaks, as they are designed for co-regulated promoter sequences with limited size. Recently, some efforts have been made to rectify this problem by modifying traditional motiffinding tools to adapt to the ChIP-seq data [68,74,76] or designing specific strategies for ChIP-seq-based motif finding [73,77]. The computational challenges of these tools include, but not limited to, (i) huge amounts of sequenced ChIP-seq reads can make motif finding a computationally infeasible problem [9]; (ii) failure to identify the motifs associated with cofactors of the ChIP-ed TF [73] or cis-regulatory modules [78]; (iii) lack of insight in integration of ChIP-seq data sets from multiple TFs [79]; (iv) the traditional false-positive issue in motif prediction, caused by the noise in ChIP-seq technology [74]; (v) lack of an efficient way to determine the correct lengths of motifs except exhaustively enumerating each length within an interval [18,80,81]; and (vi) weak support in elucidation of the mutual interactions among multiple motifs from larger ChIP-ed data sets [82][83][84], which is important in disease diagnosis through gene regulatory network construction.…”