Ashwath Kumar, Thesis Defense

In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics
in the School of Biological Sciences

Ashwath Kumar
Defends his thesis:
Quantitative Analysis of CHIP-SEQ Signals and Transcriptomes

Tuesday, May 4th, 2021
10:00 AM Eastern Time
https://bluejeans.com/106700132

Thesis Advisor:
Dr. Yuhong Fan
School of Biological Sciences
Georgia Institute of Technology

Committee Members:
Dr. Yajun Mei School of Industrial and Systems Engineering Georgia Institute of Technology
Dr. King Jordan School of Biological Sciences Georgia Institute of Technology
Dr. Shuyi Nie School of Biological Sciences Georgia Institute of Technology
Dr. Kaixiang Cao School of Medicine Case Western Reserve University

Abstract:
Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) is commonly used to analyze the in vivo interactions between proteins and DNA across the genome. Analysis of ChIP-seq data has largely focused on detection of presence of peaks that represent DNA regions enriched by chromatin immunoprecipitation, i.e. the DNA loci bound by the immunoprecipitated proteins. To properly interpret ChIP-seq data, capturing its quantitative features is imperative. In this dissertation, we develop a statistically robust pipeline, named as ChIP-seq Signal Quantifier (CSSQ), that provides normalized ChIP-seq data, enabling detection and quantification of differential binding (DBs) across the genome, allowing calculable comparisons among multiple ChIP-seq datasets on predefined regions. Using both experimental datasets and computational simulations, we demonstrate the superior performance of CSSQ against existing tools as evidenced by its high sensitivity and specificity, and low false discovery rate. CSSQ is applicable to ChIP-seq datasets with varied signal to noise ratio, significantly improving the accuracy of comparison of ChIP-seq datasets from different experiments, serving as a powerful pipeline suited to garner quantitative information from ChIP-seq datasets for deciphering epigenomes. RNA-seq has become the leading choice for transcriptome analysis. Using RNA-seq and bioinformatics analysis, we characterize gene expression profiles and key cellular processes during stem cell differentiation and cell responses upon nanoparticle exposure. Collectively, these studies show that transcriptome analysis is a powerful tool for characterization and understanding cellular mechanisms.