EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences

• DOI: 10.1101/566299
• Publication Date: 2019-03-04T20:46:41Z
• Authors: Xinzhou Ge; Haowen Zhang; Lingjue Xie; Wei Vivian Li; Soo Bin Kwon; Jingyi Jessica Li
• Keywords: Epigenomics; 0206 medical engineering; Bioinformatics and Computational Biology; Datasets as Topic; Bioengineering; Nerve Tissue Proteins; 02 engineering and technology; Databases; Genetic; Information and Computing Sciences; Databases, Genetic; Genetics; Humans; Brain Chemistry; Base Sequence; Human Genome; Computational Biology; Biological Sciences; DNA Methylation; Chromatin; 004; 620; Environmental sciences; Biological sciences; Gene Ontology; Chemical sciences; Methods Online; Generic health relevance; Sequence Alignment; Environmental Sciences; Algorithms; Software; Developmental Biology

ABSTRACTThe availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the effcacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.