the bioinformatics chat

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

Eine durchschnittliche Folge dieses Podcasts dauert 1h4m. Bisher sind 40 Folge(n) erschienen.

#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. Links:

  • mlplasmids: a user-friendly tool to predict plasmid- and chromosome-derived sequences for single species (Sergio Arredondo-Alonso, Malbert R. C. Rogers, Johanna C. Braat, Tess D...



#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 metagenomic studies. The two main themes are:

  • Why one should probably avoid using OTUs (operational taxonomic units) and use exact sequence variants (also called amplicon sequence variants, or ASVs), and how DADA2 manages to deduce the exact sequences present in the sample...


 2019-11-29  1h1m

#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 to Luke telling the story of how he stumbled upon and investigated these fake variants and what their impact is...


 2019-10-22  1h1m

#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 separate from statistical models, whereas Irineo’s new paper shows how causality can be viewed as a relationship between particularly chosen random variables (potential outcomes)...


 2019-09-27  40m

#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 million cells and can be used for scRNA-seq normalization and batch effect removal, dimensionality reduction, visualization, and differential expression...


 2019-08-30  1h20m

#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 the role the DNA shape plays in recognition of the DNA by DNA-binding proteins, such as transcription factors. Hassan also explains how his algorithm, ShapeMF, can deduce the DNA shape motifs from the ChIP-seq data...


 2019-07-26  1h1m

#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 were paired in the same receptor. In this episode Kristina Grigaityte talks about her analysis of 200,000 paired αβ sequences, which have been obtained by targeted single-cell RNA sequencing...


 2019-06-29  1h26m

#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 setting, and the approaches that do work are slower...


 2019-05-31  1h12m

#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 a neural netwok to factorize a three-dimensional tensor of epigenomic data into the three independent factors corresponding to cell types, assay types, and genomic loci. Avocado can extract a low-dimensional, information-rich summary from the wealth of experimental data from projects like the Roadmap Epigenomics Consortium and ENCODE...


 2019-04-29  1h15m

#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.


 2019-03-24  1h46m