A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets

Author:

Zhang Yipu1,Wang Ping1

Affiliation:

1. Department of Automation, School of Electronics and Control Engineering, Chang’An University, Xi’an 710064, China

Abstract

New high-throughput technique ChIP-seq, coupling chromatin immunoprecipitation experiment with high-throughput sequencing technologies, has extended the identification of binding locations of a transcription factor to the genome-wide regions. However, the most existing motif discovery algorithms are time-consuming and limited to identify binding motifs in ChIP-seq data which normally has the significant characteristics of large scale data. In order to improve the efficiency, we propose a fast cluster motif finding algorithm, named as FCmotif, to identify the(l,d)motifs in large scale ChIP-seq data set. It is inspired by the emerging substrings mining strategy to find the enriched substrings and then searching the neighborhood instances to construct PWM and cluster motifs in different length. FCmotif is not following the OOPS model constraint and can find long motifs. The effectiveness of proposed algorithm has been proved by experiments on the ChIP-seq data sets from mouse ES cells. The whole detection of the real binding motifs and processing of the full size data of several megabytes finished in a few minutes. The experimental results show that FCmotif has advantageous to deal with the(l,d)motif finding in the ChIP-seq data; meanwhile it also demonstrates better performance than other current widely-used algorithms such as MEME, Weeder, ChIPMunk, and DREME.

Funder

Fundamental Research Funds for the Central Universities

Publisher

Hindawi Limited

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A comparative benchmark of classic DNA motif discovery tools on synthetic data;Briefings in Bioinformatics;2021-08-05

2. Discovering Mutated Motifs in DNA Sequences: A Comparative Analysis;Proceedings of International Conference on Artificial Intelligence and Applications;2020-07-02

3. An Entropy-Based Position Projection Algorithm for Motif Discovery;BioMed Research International;2016

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