the bioinformatics chat

Today on the podcast we have Tomasz Kociumaka and Dominik Kempa, the authors of the preprint Collapsing the Hierarchy of Compressed Data Structures: Suffix Arrays in Optimal Compressed Space.

The suffix array is one of the foundational data structures in bioinformatics, serving as an index that allows fast substring searches in a large text. However, in its raw form, the suffix array occupies the space proport...

In this episode, David Dylus talks about Read2Tree, a tool that builds alignment matrices and phylogenetic trees from raw sequencing reads. By leveraging the database of orthologous genes called OMA, Read2Tree bypasses traditional, time-consuming steps such as genome assembly, annotation and all-versus-all sequence comparisons.

Links:

• Inference of phylogenetic trees directly from r...

This is the third and final episode in the AlphaFold series, originally recorded on February 23, 2022, with Amelie Stein, now an associate professor at the University of Copenhagen.

In the episode, Amelie explains what 𝛥𝛥G is, how it informs us whether a particular protein mutation affects its stability, and how AlphaFold 2 helps in this analysis.

A note from Amelie:

So...

This is the second episode in the AlphaFold series, originally recorded on February 14, 2022, with Janani Durairaj, a postdoctoral researcher at the University of Basel.

Janani talks about how she used shape-mers and topic modelling to discover classes of proteins assembled by AlphaFold 2 that were absent from the Protein Data Bank (PDB).

The bioinformatics discussion starts at 03:35.

Li...

In this episode, originally recorded on February 9, 2022, Roman talks to Pedro Beltrao about AlphaFold, the software developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence.

Pedro is an associate professor at ETH Zurich and the coordinator of the structural biology community assessment of AlphaFold2 applications project, which involved over 30 scientists from different institu...

In this episode, Jacob Schreiber interviews Žiga Avsec about a recently released model, Enformer. Their discussion begins with life differences between academia and industry, specifically about how research is conducted in the two settings. Then, they discuss the Enformer model, how it builds on previous work, and the potential that models like it have for genomics research in the future. Finally, they have a h...

The Bioinformatics Contest is back this year, and we are back to discuss it!

This year’s contest winners Maksym Kovalchuk (1st prize) and Matt Holt (2nd prize) talk about how they approach participating in the contest and what strategies have earned them the top scores.

Timestamps and links for the individual problems:

• 00:10:36 Genotype Imputation
• 00:21:26 Causative Mutation
• ...

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 here.

Links:

• Dingo on GitHub
• Searching for COVID-19 treatments using metabol...

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 togethe...

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.

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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 broa...

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 de...

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.

Links:

• Octant Bio
• Towards a comprehensive catalogue of validated and target-linked human ...

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 w...

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 colleagues.

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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.

Links:

• A performant bridge between fi...

In this episode, Jacob Schreiber interviews Devin Schweppe about the analysis of mass spectrometry data in the field of proteomics. They begin by delving into the different types of mass spectrometry methods, including MS1, MS2, and, MS3, and the reasons for using each. They then discuss a recent paper from Devin, Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quant...

In this episode, Will Freyman talks about identity-by-descent (IBD): how it’s used at 23andMe, and how the templated positional Burrows-Wheeler transform can find IBD segments in the presence of genotyping and phasing errors.

Links:

• Fast and robust identity-by-descent inference with the templated positional Burrows-Wheeler transform (William A. Freyman, Kimberly F. McManus, Suyas...

In this episode, Jacob Schreiber interviews David Kelley about machine learning models that can yield insight into the consequences of mutations on the genome. They begin their discussion by talking about Calico Labs, and then delve into a series of papers that David has written about using models, named Basset and Basenji, that connect genome sequence to functional activity and so can be used to quantify the...