the bioinformatics chat

By Roman Cheplyaka

What's the bioinformatics chat about?

A podcast about computational biology, bioinformatics, and next generation sequencing.

Download our free app to listen on your phone

the bioinformatics chat episodes:

12.21.2023
#70 Prioritizing drug target genes with Marie Sadler
In this episode, Marie Sadler talks about her recent Cell Genomics paper, Multi-layered genetic approaches to identify approved drug targets. Previous studies have found that the drugs that target a gene linked to the disease are more likely to be approved. Yet there are...
09.29.2023
#69 Suffix arrays in optimal compressed space and δ-SA with Tomasz Kociumaka and Dominik Kempa
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...
08.28.2023
#68 Phylogenetic inference from raw reads and Read2Tree with David Dylus
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. ...
07.29.2023
#67 AlphaFold and variant effect prediction with Amelie Stein
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
07.10.2023
#66 AlphaFold and shape-mers with Janani Durairaj
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...
06.21.2023
#65 AlphaFold and protein interactions with Pedro Beltrao
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...
11.09.2021
#64 Enformer: predicting gene expression from sequence with Žiga Avsec
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...
09.27.2021
#63 Bioinformatics Contest 2021 with Maksym Kovalchuk and James Matthew Holt
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. ...
07.28.2021
#62 Steady states of metabolic networks and Dingo with Apostolos Chalkis
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...
06.23.2021
#61 3D genome organization and GRiNCH with Da-Inn Erika Lee
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...
05.12.2021
#60 Differential gene expression and DESeq2 with Michael Love
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,...
04.21.2021
#59 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...
03.31.2021
#58 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...
03.10.2021
#57 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. ...
02.17.2021
#56 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...
01.13.2021
#55 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...
12.16.2020
#54 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...
11.18.2020
#53 Real-time quantitative proteomics with Devin Schweppe
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...
10.27.2020
#52 How 23andMe finds identical-by-descent segments with William Freyman
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. ...
10.07.2020
#51 Basset and Basenji with David Kelley
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...
09.10.2020
#50 ENCODE3 with Jill Moore
In this episode, Jacob Schreiber interviews Jill Moore about recent research from the ENCODE Project. They begin their discussion with an overview and goals of the ENCODE Project, and then discuss a bundle of papers that were recently published in various Nature journals and...
08.19.2020
#49 Most Permissive Boolean Networks with Loïc Paulevé
In systems biology, Boolean networks are a way to model interactions such as gene regulation or cell signaling. The standard interpretations of Boolean networks are the synchronous, asynchronous, and fully asynchronous semantics. In this episode, Loïc Paulevé explains how the same Boolean networks can be...
07.29.2020
#48 Machine learning for drug development with Marinka Zitnik
In this episode, Jacob Schreiber interviews Marinka Zitnik about applications of machine learning to drug development. They begin their discussion with an overview of open research questions in the field, including limiting the search space of high-throughput testing methods, designing drugs entirely from scratch,...
06.24.2020
#47 Reproducible pipelines and NGLess with Luis Pedro Coelho
NGLess is a programming language specifically targeted at next generation sequencing (NGS) data processing. In this episode we chat with its main developer, Luis Pedro Coelho, about the benefits of domain-specific languages, pros and cons of Haskell in bioinformatics, reproducibility, and of course NGLess itself. ...
05.27.2020
#46 HiFi reads and HiCanu with Sergey Nurk and Sergey Koren
In this episode, I continue to talk (but mostly listen) to Sergey Koren and Sergey Nurk. If you missed the previous episode, you should probably start there. Otherwise, join us to learn about HiFi reads, the tradeoff between read length and quality, and...
05.20.2020
#45 Genome assembly and Canu with Sergey Koren and Sergey Nurk
In this episode, Sergey Nurk and Sergey Koren from the NIH share their thoughts on genome assembly. The two Sergeys tell the stories behind their amazing careers as well as behind some of the best known genome assemblers: Celera assembler, Canu, and SPAdes. ...
04.29.2020
#44 DNA tagging and Porcupine with Kathryn Doroschak
Porcupine is a molecular tagging system—a way to tag physical objects with pieces of DNA called molecular bits, or molbits for short. These DNA tags then can be rapidly sequenced on an Oxford Nanopore MinION device without any need for library preparation. In this...
03.27.2020
#43 Generalized PCA for single-cell data with William Townes
Will Townes proposes a new, simpler way to analyze scRNA-seq data with unique molecular identifiers (UMIs). Observing that such data is not zero-inflated, Will has designed a PCA-like procedure inspired by generalized linear models (GLMs) that, unlike the standard PCA, takes into account...
02.28.2020
#42 Spectrum-preserving string sets and simplitigs with Amatur Rahman and Karel Břinda
In this episode, we hear from Amatur Rahman and Karel Břinda, who independently of one another released preprints on the same concept, called simplitigs or spectrum-preserving string sets. Simplitigs offer a way to efficiently store and query large sets of k-mers—or, equivalently, large de Bruijn...
01.27.2020
#41 Epidemic models with Kris Parag
Kris Parag is here to teach us about the mathematical modeling of infectious disease epidemics. We discuss the SIR model, the renewal models, and how insights from information theory can help us predict where an epidemic is going. ...
12.30.2019
#40 Plasmid classification and binning with Sergio Arredondo-Alonso and Anita Schürch
Does a given bacterial gene live on a plasmid or the chromosome? What other genes live on the same plasmid? In this episode, we hear from Sergio Arredondo-Alonso and Anita Schürch, whose projects mlplasmids and gplas answer these types of questions. ...
11.29.2019
#39 Amplicon sequence variants and bias with Benjamin Callahan
In this episode, Benjamin Callahan talks about some of the issues faced by microbiologists when conducting amplicon sequencing and metagenomic studies. The two main themes are: Why one should probably avoid using OTUs (operational taxonomic units) and use exact...
10.22.2019
#38 Issues in legacy genomes with Luke Anderson-Trocmé
In this episode, Luke Anderson-Trocmé talks about his findings from the 1000 Genomes Project. Namely, the early sequenced genomes sometimes contain specific mutational signatures that haven’t been replicated from other sources and can be found via their association with lower base quality scores. Listen...
09.27.2019
#37 Causality and potential outcomes with Irineo Cabreros
In this episode, I talk with Irineo Cabreros about causality. We discuss why causality matters, what does and does not imply causality, and two different mathematical formalizations of causality: potential outcomes and directed acyclic graphs (DAGs). Causal models are usually considered external to and...
08.30.2019
#36 scVI with Romain Lopez and Gabriel Misrachi
In this episode, we hear from Romain Lopez and Gabriel Misrachi about scVI—Single-cell Variational Inference. scVI is a probabilistic model for single-cell gene expression data that combines a hierarchical Bayesian model with deep neural networks encoding the conditional distributions. scVI scales to over one...
07.26.2019
#35 The role of the DNA shape in transcription factor binding with Hassan Samee
Even though the double-stranded DNA has the famous regular helical shape, there are small variations in the geometry of the helix depending on what exact nucleotides its made of at that position. In this episode of the bioinformatics chat, Hassan Samee talks about...
06.29.2019
#34 Power laws and T-cell receptors with Kristina Grigaityte
An αβ T-cell receptor is composed of two highly variable protein chains, the α chain and the β chain. However, based only on bulk DNA or RNA sequencing it is impossible to determine which of the α chain and β chain sequences...
05.31.2019
#33 Genome assembly from long reads and Flye with Mikhail Kolmogorov
Modern genome assembly projects are often based on long reads in an attempt to bridge longer repeats. However, due to the higher error rate of the current long read sequencers, assemblers based on de Bruijn graphs do not work well in this...
04.29.2019
#32 Deep tensor factorization and a pitfall for machine learning methods with Jacob Schreiber
In this episode, we hear from Jacob Schreiber about his algorithm, Avocado. Avocado uses deep tensor factorization to break a three-dimensional tensor of epigenomic data into three orthogonal dimensions corresponding to cell types, assay types, and genomic loci. Avocado can extract a low-dimensional, information-rich latent...
03.24.2019
#31 Bioinformatics Contest 2019 with Alexey Sergushichev and Gennady Korotkevich
The third Bioinformatics Contest took place in February 2019. Alexey Sergushichev, one of the organizers of the contest, and Gennady Korotkevich, the 1st prize winner, join me to discuss this year’s problems. ...
02.27.2019
#30 Bayesian inference of chromatin structure from Hi-C data with Simeon Carstens
Hi-C is a sequencing-based assay that provides information about the 3-dimensional organization of the genome. In this episode, Simeon Carstens explains how he applied the Inferential Structure Determination (ISD) framework to build a 3D model of chromatin and fit that model to Hi-C...
01.27.2019
#29 Haplotype-aware genotyping from long reads with Trevor Pesout
Long read sequencing technologies, such as Oxford Nanopore and PacBio, produce reads from thousands to a million base pairs in length, at the cost of the increased error rate. Trevor Pesout describes how he and his colleagues leverage long reads for simultaneous variant calling/genotyping...
12.28.2018
#28 Space-efficient variable-order Markov models with Fabio Cunial
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...
11.29.2018
#27 Classification of CRISPR-induced mutations and CRISPRpic with HoJoon Lee and Seung Woo Cho
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...
10.27.2018
#26 Feature selection, Relief and STIR with Trang Lê
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...
09.24.2018
#25 Transposons and repeats with Kaushik Panda and Keith Slotkin
Kaushik Panda and Keith Slotkin come on the podcast to educate us about repetitive DNA and transposable elements. We talk LINEs, SINEs, LTRs, and even Sleeping Beauty transposons! Kaushik and Keith explain why repeats matter for your whole-genome analysis and answer listeners’ questions. ...
08.31.2018
#24 Read correction and Bcool with Antoine Limasset
Antoine Limasset joins me to talk about NGS read correction. Antoine and his colleagues built the read correction tool Bcool based on the de Bruijn graph, and it corrects reads far better than any of the current methods like Bloocoo, Musket, and Lighter. We...
07.27.2018
#23 RNA design, EteRNA and NEMO with Fernando Portela
In this episode, I talk to Fernando Portela, a software engineer and amateur scientist who works on RNA design — the problem of composing an RNA sequence that has a specific secondary structure. We talk about how Fernando and others compete and collaborate in designing...
06.29.2018
#22 smCounter2: somatic variant calling and UMIs with Chang Xu
In this episode I’m joined by Chang Xu. Chang is a senior biostatistician at QIAGEN and an author of smCounter2, a low-frequency somatic variant caller. To distinguish rare somatic mutations from sequencing errors, smCounter2 relies on unique molecular identifiers, or UMIs, which help...
05.31.2018
#21 Linear mixed models, GWAS, and lme4qtl with Andrey Ziyatdinov
Linear mixed models are used to analyze GWAS data and detect QTLs. Andrey Ziyatdinov recently released an R package, lme4qtl, that can be used to formulate and fit these models. In this episode, Andrey and I discuss linear mixed models, genome-wide association studies,...

What's the bioinformatics chat about?

Download our free app to listen on your phone

the bioinformatics chat episodes:

#70 Prioritizing drug target genes with Marie Sadler

#69 Suffix arrays in optimal compressed space and δ-SA with Tomasz Kociumaka and Dominik Kempa

#68 Phylogenetic inference from raw reads and Read2Tree with David Dylus

#67 AlphaFold and variant effect prediction with Amelie Stein

#66 AlphaFold and shape-mers with Janani Durairaj

#65 AlphaFold and protein interactions with Pedro Beltrao

#64 Enformer: predicting gene expression from sequence with Žiga Avsec

#63 Bioinformatics Contest 2021 with Maksym Kovalchuk and James Matthew Holt

#62 Steady states of metabolic networks and Dingo with Apostolos Chalkis

#61 3D genome organization and GRiNCH with Da-Inn Erika Lee

#60 Differential gene expression and DESeq2 with Michael Love

#59 Proteomics calibration with Lindsay Pino

#58 B cell maturation and class switching with Hamish King

#57 Enhancers with Molly Gasperini

#56 Polygenic risk scores in admixed populations with Bárbara Bitarello

#55 Phylogenetics and the likelihood gradient with Xiang Ji

#54 Seeding methods for read alignment with Markus Schmidt

#53 Real-time quantitative proteomics with Devin Schweppe

#52 How 23andMe finds identical-by-descent segments with William Freyman

#51 Basset and Basenji with David Kelley

#50 ENCODE3 with Jill Moore

#49 Most Permissive Boolean Networks with Loïc Paulevé

#48 Machine learning for drug development with Marinka Zitnik

#47 Reproducible pipelines and NGLess with Luis Pedro Coelho

#46 HiFi reads and HiCanu with Sergey Nurk and Sergey Koren

#45 Genome assembly and Canu with Sergey Koren and Sergey Nurk

#44 DNA tagging and Porcupine with Kathryn Doroschak

#43 Generalized PCA for single-cell data with William Townes

#42 Spectrum-preserving string sets and simplitigs with Amatur Rahman and Karel Břinda

#41 Epidemic models with Kris Parag

#40 Plasmid classification and binning with Sergio Arredondo-Alonso and Anita Schürch

#39 Amplicon sequence variants and bias with Benjamin Callahan

#38 Issues in legacy genomes with Luke Anderson-Trocmé

#37 Causality and potential outcomes with Irineo Cabreros

#36 scVI with Romain Lopez and Gabriel Misrachi

#35 The role of the DNA shape in transcription factor binding with Hassan Samee

#34 Power laws and T-cell receptors with Kristina Grigaityte

#33 Genome assembly from long reads and Flye with Mikhail Kolmogorov

#32 Deep tensor factorization and a pitfall for machine learning methods with Jacob Schreiber

#31 Bioinformatics Contest 2019 with Alexey Sergushichev and Gennady Korotkevich

#30 Bayesian inference of chromatin structure from Hi-C data with Simeon Carstens

#29 Haplotype-aware genotyping from long reads with Trevor Pesout

#28 Space-efficient variable-order Markov models with Fabio Cunial

#27 Classification of CRISPR-induced mutations and CRISPRpic with HoJoon Lee and Seung Woo Cho

#26 Feature selection, Relief and STIR with Trang Lê

#25 Transposons and repeats with Kaushik Panda and Keith Slotkin

#24 Read correction and Bcool with Antoine Limasset

#23 RNA design, EteRNA and NEMO with Fernando Portela

#22 smCounter2: somatic variant calling and UMIs with Chang Xu

#21 Linear mixed models, GWAS, and lme4qtl with Andrey Ziyatdinov