MoCoLo: a testing framework for motif co-localization

We present a new analytical method for examining feature interaction by introducing the notion of reciprocal co-occurrence, define statistics to estimate it and hypotheses to test for it. Our approach leverages conditional motif co-occurrence events between features to infer their co-localization. Using reverse conditional probabilities and introducing a novel simulation approach that retains motif properties (e.g. length, guanine-content), our method further accounts for potential confounders in testing. As a proof-of-concept, motif co-localization (MoCoLo) confirmed the co-occurrence of histone markers in a breast cancer cell line. As a novel analysis, MoCoLo identified significant co-localization of oxidative DNA damage within non-B DNA-forming regions that significantly differed between non-B DNA structures. Altogether, these findings demonstrate the potential utility of MoCoLo for testing spatial interactions between genomic features via their co-localization.PMID:38521050 | DOI:10.1093/bib/bbae019

Source: Briefings in Bioinformatics - March 23, 2024 Category: Bioinformatics Authors: Qi Xu Imee M A Del Mundo Maha Zewail-Foote Brian T Luke Karen M Vasquez Jeanne Kowalski Source Type: research