Predicting Gene Regulation by the Synthetic Transcription Factor PcTF using Histone and Chromatin Features

Student: Racheal Atanga

Mentors: Dr. Christopher Plaisier – SBHSE
Dr. Vincent Pizziconi – SBHSE
Dr. James Abbas – SBHSE

YouTube Link: View the video link below before joining the zoom meeting

Zoom link: https://asu.zoom.us/j/3892001234

Zoom meeting time:10am - noon

Abstract

The synthetic Polycomb transcription factor (PcTF) has been determined to reactivate interferon genes by targeting histone marks on the gene where suppression has been caused by polycomb proteins. There is the need to learn more about PcTFs activity and identify which specific genes respond to facilitate clinical translation for cancer treatment. The aim of these studies is to develop a predictive machine learning classification model for determining histone features that predict when PcTF is likely to be bound and functional in the MCF7 breast cancer cell line. A major part of this work has required gathering data about the histones and other DNA binding proteins from MCF7 cell lines from ENCODE and from SRA databases. Data from these sources was used as input for the ChromHMM software which makes higher level features that combine the chromatin marks. The next steps would be to develop the histone marks and ChromHMM into features that can used by machine learning methods. Our hope is that this model can then be used to predict PcTF activity for other established cell lines, and eventually allow us to predict genes that will be responsive to PcTF in patient tumor cells.