Prof. Ben Langmead: Pan-genomic advances for fighting reference bias

Speaker: Ben Langmead: , John Hopkins University

Date: Wednesday, September 18, 2024

Time: 11:30 AM to 1:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location:

Event Type: Seminar

Room Description: 32G-575

Host: Bonnie Berger

Contact: Anna E Sappington, 443-223-2022, asapp@csail.mit.edu

Relevant URL: https://math.mit.edu/compbiosem/

Speaker URL: None

Speaker Photo:
None

Reminders to: csail-local@lists.csail.mit.edu

Reminder Subject: TALK: Prof. Ben Langmead: Pan-genomic advances for fighting reference bias

Sequencing data analysis often begins with aligning reads to a reference genome, where the reference takes the form of a linear string of bases. But linearity leads to reference bias, a tendency to miss or misreport alignments containing non-reference alleles, which can confound downstream statistical and biological results. This is a major concern in human genomics; we don't want to live in a world where diagnostics and therapeutics are differentially effective depending whether and where our genetic variants happen to match the reference.

Fortunately, computer science and bioinformatics are meeting the moment. We can now index and align sequencing reads to references that include many population variants. I will present some of the major and insights that have shaped this journey from the early days of efficient genome indexing -- especially the Burrows-Wheeler Transform -- continuing through recent methods for indexing graph-shaped references and references that include many genomes. I will emphasize recent results that show how to optimize simple and complex pan-genome representations for effective avoidance of reference bias. Finally, I will outline promising methods for the bias, including new ideas for how to measure bias, new proposals in compressed indexing, and new workflows that integrate genotype imputation to improve reference bias.

Research Areas:
Algorithms & Theory, AI & Machine Learning, Computational Biology

Impact Areas:
Big Data, Health Care

This event is not part of a series.

Created by Anna E Sappington at Friday, September 13, 2024 at 1:36 PM.