Keshav Gandhi

Home Institution
The University of Illinois at Urbana-Champaign

Year Participated
2022

Year in School
Undergraduate

REU Faculty Mentor
Mohammed El-Kebir

Research Area Interest
Bioinformatics and Computational Biology

Project Title
Poly-G simulation and deconvolution to model cancer progression

Biography & Research Abstract

Abstract:

Microsatellites are repetitive units composed of DNA base pairs. They are ubiquitous across an organism’s genome and are characterized by their high mutation rate. In particular, poly-guanine (poly-G) sites are a type of microsatellite useful in analyzing cancer metastasis and tumor heterogeneity due to their high mutation rate, presence in many tissue types, ability to remain agnostic to selective pressure, and utility in analyzing a tumor’s phylogenetic history.

PCR-based assays and capillary electrophoresis are used to determine the precise nature of the poly-G genotypes for a given sample. However, because the enzyme Taq polymerase occasionally creates replication slippage errors during the PCR process, this process is inexact, and genotypes are represented using a “stutter” distribution. Only the highest intensity electrophoresis peaks in these distributions characterize the true genotype of the sample.

Across a given number of poly-G sites and samples, these intensity distributions form a tensor. This project’s goal is to approximate this tensor by creating a genotype tensor and clonal abundance matrix. This will be accomplished via data simulation, statistical comparison to real data, and methods of tensor decomposition.

Bio:

Keshav is a rising sophomore at the University of Illinois at Chicago who is majoring in Biological Sciences & Statistics and minoring in Computer Science on a pre-med track. He has worked in research labs in Urbana–Champaign for the past three years and plans to continue pursuing research with the El-Kebir Lab as well as NCSA's Genomics Group. He is interested in integrating machine learning, artificial intelligence, and computational genomics with cancer/autoimmune disease prevention, diagnosis, and treatment.