A podcast about computational biology, bioinformatics, and next generation sequencing.
Proteomics calibration with Lindsay Pino
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. The discussion then turns more broadly to questions in
genomics that can potentially be addressed using proteomic measurements.
Matrix-Matched Calibration Curves for Asssessing Analytical Figures of Merit in Quantitative Proteomics
(Lindsay K. Pino, Brian C. Searle, Han-Yin Yang, Andrew N. Hoofnagle, William S. Noble, and Michael J. MacCross)
B cell maturation and class switching with Hamish King
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 the episode Hamish talks about some of the interesting B cell states he
uncovered and shares his thoughts on questions such as «When does a B cell
decide to class-switch?» and «Why is the antibody isotype correlated with its
Single-cell analysis of human B cell maturation predicts how antibody class switching shapes selection dynamics
(Hamish W. King, Nara Orban, John C. Riches, Andrew J. Clear, Gary Warnes, Sarah A. Teichmann, Louisa K. James) (paywalled by Science Immunology)
Antibody repertoire and gene expression dynamics of diverse human B cell states during affinity maturation
(the preprint of the above Science Immunology paper)
www.tonsilimmune.org: An immune cell atlas of the human tonsil and B cell maturation
Enhancers with Molly Gasperini
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. Tome, and Jay Shendure)
Polygenic risk scores in admixed populations with Bárbara Bitarello
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.
Polygenic Scores for Height in Admixed Populations (Bárbara D. Bitarello, Iain Mathieson)
What is ancestry? (Iain Mathieson, Aylwyn Scally)
Phylogenetics and the likelihood gradient with Xiang Ji
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
Gradients Do Grow on Trees: A Linear-Time O(N)-Dimensional Gradient for Statistical Phylogenetics
(Xiang Ji, Zhenyu Zhang, Andrew Holbrook, Akihiko Nishimura, Guy Baele, Andrew Rambaut, Philippe Lemey, Marc A Suchard)
BEAGLE: the package that implements the gradient algorithm
BEAST: the program that implements the Hamiltonian Monte Carlo sampler and the molecular clock models
Seeding methods for read alignment with Markus Schmidt
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.
A performant bridge between fixed-size and variable-size seeding
(Arne Kutzner, Pok-Son Kim, Markus Schmidt)
MA the Modular Aligner
Calibrating Seed-Based Heuristics to Map Short Reads With Sesame
(Guillaume J. Filion, Ruggero Cortini, Eduard Zorita) — another
interesting recent work on seeding methods (though we didn’t get to discuss
it in this episode)
This podcast has great interviews and in-depth coverage of new tools and techniques.