Detection and identification of cis-regulatory elements using change-point and classification algorithms

• Published in: BMC genomics Vol. 23; no. 1; pp. 78 - 16
• Main Authors: Maderazo, Dominic, Flegg, Jennifer A., Algama, Manjula, Ramialison, Mirana, Keith, Jonathan
• Format: Journal Article
• Language: English
• Published: London BioMed Central 25.01.2022; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Alignment; Animal Genetics and Genomics; Bayes Theorem; Bayesian analysis; Bayesian modelling; Bayesian statistical decision theory; Binding Sites; Binding sites (Biochemistry); Biomedical and Life Sciences; Clusters; Computer applications; Conserved non-coding sequences; Gene expression; Gene regulation; Gene therapy; Genetic regulation; Genetic research; Genetic transcription; Genome segmentation; Genome, Human; Genomes; Genomics; Humans; Interrogation; Life Sciences; Methodology; Methodology Article; Methods; Microarrays; Microbial Genetics and Genomics; Ontology; Plant Genetics and Genomics; Proteomics; Putative functional elements; Regulatory sequences; Regulatory Sequences, Nucleic Acid - genetics; Stem cells; Transcription; Australia; Putative functional elements; Conserved non-coding sequences; Bayesian modelling; Genome segmentation
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-021-08190-0

Background Transcriptional regulation is primarily mediated by the binding of factors to non-coding regions in DNA. Identification of these binding regions enhances understanding of tissue formation and potentially facilitates the development of gene therapies. However, successful identification of binding regions is made difficult by the lack of a universal biological code for their characterisation. Results We extend an alignment-based method, changept , and identify clusters of biological significance, through ontology and de novo motif analysis. Further, we apply a Bayesian method to estimate and combine binary classifiers on the clusters we identify to produce a better performing composite. Conclusions The analysis we describe provides a computational method for identification of conserved binding sites in the human genome and facilitates an alternative interrogation of combinations of existing data sets with alignment data.