In this episode, Apostolos Chalkis presents sampling steady
states of metabolic networks as an alternative to the widely used flux balance
analysis (FBA). We also discuss dingo, a
Python package written by Apostolos that employs geometric random walks to
sample steady states. You can see dingo in action
In this episode, Jacob Schreiber interviews Da-Inn Erika Lee about
data and computational methods for making sense of 3D genome structure. They
begin their discussion by talking about 3D genome structure at a high level
and the challenges in working with such data. Then, they discuss a method
recently developed by Erika, named GRiNCH, that mines this data to
identify spans of the genome that cluster together in 3D space and
potentially help control gene regulation...
In this episode, Michael Love joins us to talk about the differential gene
expression analysis from bulk RNA-Seq data.
We talk about the history of Mike’s own differential expression package,
DESeq2, as well as other packages in this space, like edgeR and limma, and the
theory they are based upon. Mike also shares his experience of being the
author and maintainer of a popular bioninformatics package...
In this episode, Lindsay Pino discusses the
challenges of making quantitative measurements in the field of proteomics.
Specifically, she discusses the difficulties of comparing measurements across
different samples, potentially acquired in different labs, as well as a method
she has developed recently for calibrating these measurements without the need
for expensive reagents...
In this episode, we learn about B cell maturation and class switching from
Hamish King. Hamish recently published a
paper on this subject in Science Immunology, where he and his coauthors
analyzed gene expression and antibody repertoire data from human tonsils...
In this episode, Jacob Schreiber interviews Molly Gasperini about
enhancer elements. They begin their discussion by talking about Octant Bio,
and then dive into the surprisingly difficult task of defining enhancers and
determining the mechanisms that enable them to regulate gene expression.
Towards a comprehensive catalogue of validated and target-linked human enhancers (Molly Gasperini, Jacob M...
Polygenic risk scores (PRS) rely on the genome-wide association studies (GWAS)
to predict the phenotype based on the genotype. However, the prediction
accuracy suffers when GWAS from one population are used to calculate PRS within
a different population, which is a problem because the majority of the GWAS
are done on cohorts of European ancestry.
In this episode, Bárbara Bitarello helps us
understand how PRS work and why they don’t transfer well across populations...
In this episode, we chat about phylogenetics with Xiang Ji. We start with a
general introduction to the field and then go deeper into the likelihood-based
methods (maximum likelihood and Bayesian inference). In particular, we talk
about the different ways to calculate the likelihood gradient, including a
linear-time exact gradient algorithm recently published by Xiang and his
In this episode, Markus Schmidt explains how seeding in read alignment works.
We define and compare k-mers, minimizers, MEMs, SMEMs, and maximal spanning seeds.
Markus also presents his recent work on computing variable-sized seeds (MEMs,
SMEMs, and maximal spanning seeds) from fixed-sized seeds (k-mers and
minimizers) and his Modular Aligner...