Seminar: Combinatorial algorithms to study cancer and infectious disease evolution

Abstract

Rapid advancements in sequencing technologies are revolutionizing the field of modern medicine. In recent years, several groundbreaking techniques such as CRISPR-Cas9 genome editing and barcoding of biomolecules from individual cells have emerged. These breakthroughs, coupled with the decreasing costs of genomic sequencing, have resulted in the development of various “*-Seq” protocols for measuring DNA, RNA, and proteins at unprecedented throughput and resolution. The diverse characteristics of these sequencing methods have created a pressing need for specialized algorithms capable of effectively interpreting the vast amounts of sequencing data.

In this talk, I will discuss algorithms I have designed with applications in cancer genomics and infection genomics. First, I will present ConDoR, an algorithm to infer the evolutionary history of a cancer tumor using targeted single-cell DNA sequencing (scDNA-seq) data. Underlying ConDoR is a new evolutionary model, the Constrained k-Dollo model, which generalizes existing models used for cancer evolution. I will show that ConDoR outperforms existing methods for tumor phylogeny inference methods on simulated and real targeted scDNA-seq data. Second, I will present a framework to reconstruct the transmission history of a disease outbreak from genomic sequencing data of the pathogen collected from infected hosts.  Within this framework, I will present algorithms to sample, count and summarize parsimonious transmission histories using genomic and epidemiological data. I will showcase the utility of these algorithms in studying real-world disease outbreak scenarios. Overall, my talk will underscore the crucial role of specialized models and algorithms in studying evolutionary systems using single-cell sequencing data.

_Biography

Dr. Palash Sashittal is a Postdoctoral Research Associate with Prof. Ben Raphael in the Computer Science Department at Princeton University. His research focuses on the design of combinatorial and statistical algorithms to analyze and interpret sequencing data. Recent areas of emphasis include infectious disease evolution and transmission, cancer genome evolution, and cell fate mapping of developmental systems. He received a Ph.D. in Aerospace Engineering and M.S. in Computer Science from the University of Illinois Urbana-Champaign (UIUC), and B.Tech. in Aerospace Engineering from Indian Institute of Technology Bombay (IIT Bombay). Palash’s work has been recognized by multiple awards and honors, including Mistletoe Research Fellowship, Best Paper Award at RECOMB CCB, Cornell Future Faculty Fellowship and Mavis Future Faculty Fellowship (UIUC). Palash is firmly committed to enhancing diversity, equity, and inclusion in STEM through mentoring, outreach, and service activities.