The bioinformatics chat is a podcast about computational biology, bioinformatics, and next generation sequencing.
The bioinformatics chat is produced by Roman Cheplyaka.
December 28, 2018
This time you’ll hear from Fabio Cunial on the topic of Markov models and space-efficient data structures. First we recall what a Markov model is and why variable-order Markov models are an improvement over the standard, fixed-order models. Next we discuss the various data structures and indexes that allowed Fabio and his collaborators to represent these models in a very small space while still keeping the queries efficient. Burrows-Wheeler transform, suffix trees and arrays, tries and suffix link trees, and more!
November 29, 2018
In this episode HoJoon Lee and Seung Woo Cho explain how to perform a CRISPR experiment and how to analyze its results. HoJoon and Seung Woo developed an algorithm that analyzes sequenced amplicons containing the CRISPR-induced double-strand break site and figures out what exactly happened there (e.g. a deletion, insertion, substitution etc.)
October 27, 2018
Relief is a statistical method to perform feature selection. It could be used, for instance, to find genomic loci that correlate with a trait or genes whose expression correlate with a condition. Relief can also be made sensitive to interaction effects (known in genetics as epistasis).
In this episode Trang Lê joins me to talk about Relief and her version of Relief called STIR (STatistical Inference Relief). While traditional Relief algorithms could only rank features and needed a user-supplied threshold to decide which features to select, Trang’s reformulation of Relief allowed her to compute p-values and make the selection process less arbitrary.