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

Dominic Maderazo⁰, Jennifer Flegg⁰, Manjula Algama¹, Mirana Ramialison², Jonathan Keith³
(0) The University of Melbourne
(1) RMIT
(2) The Australian Regenerative Medicine Instute
(3) Monash University

Find me on Wed Nov 25th, 1:30-2:50pm AEDT in Remo, table 50

Abstract
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. 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. The analysis we describe provides a computational method for identification of conserved binding sites.