A new exhaustive method and strategy for finding motifs in ChIP-enriched regions.
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 (l, d) motifs (l ≤ 10, d ≤ 2) contained in ChIP-enriched regions. In this work we have developed a more powerful exhaustive method (FMotif) for finding long (l, d) 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/.
School of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, China ; Department of Bioengineering/Bioinformatics, University of Illinois at Chicago, Chicago, Illinois, United States of America.
Caiyan
C
Jia
Center for Healthcare Studies, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America ; Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
Matthew B
MB
Carson
School of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, China.
Yang
Y
Wang
School of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis, Beijing Jiaotong University, Beijing, China.
Youfang
Y
Lin
Department of Bioengineering/Bioinformatics, University of Illinois at Chicago, Chicago, Illinois, United States of America ; Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai JiaoTong University, Shanghai, China.
Hui
H
Lu
D000465:Algorithms; D000818:Animals; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000378:metabolism; D053595:Embryonic Stem Cells; D059014:High-Throughput Nucleotide Sequencing; D051379:Mice; D059372:Nucleotide Motifs; D056510:Position-Specific Scoring Matrices; D012680:Sensitivity and Specificity; D014157:Transcription Factors / Q000378:metabolism
D004268:DNA-Binding Proteins; D014157:Transcription Factors