A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.
basic_science · Level V
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- Record sourced from PubMed, PMID 38487805.
- Also identified by DOI 10.1016/j.patter.2024.100927 and PMC identifier 10935504.
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Abstract
In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 <i>H</i>. <i>sapiens</i> datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.