bioinformatics chat

The bioinformatics chat is a podcast about computational biology, bioinformatics, and next generation sequencing.

The bioinformatics chat is produced by Roman Cheplyaka.

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#36 scVI with Romain Lopez and Gabriel Misrachi

August 30, 2019

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. We also discuss the recently implemented in scVI automatic hyperparameter selection via Bayesian optimization.

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

July 26, 2019

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.

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

June 29, 2019

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. Kristina used the power law distribution to model the T-cell clone sizes, which led her to reject the commonly held assumptions about the independence of the α and β chains. We also talk about Bayesian inference of power law distributions and about mixtures of power laws.

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Previous episodes

#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

#20 B cell receptor substitution profile prediction and SPURF with Kristian Davidsen and Amrit Dhar

#19 Genome fingerprints with Gustavo Glusman

#18 Bioinformatics Contest 2018 with Alexey Sergushichev and Ekaterina Vyahhi

#17 Rarefaction, alpha diversity, and statistics with Amy Willis

#16 Javier Quilez on what makes large sequencing projects successful

#15 Optimal transport for single-cell expression data with Geoffrey Schiebinger

#14 Generating functions for read mapping with Guillaume Filion

#13 Bracken with Jennifer Lu

#12 Modelling the immune system and C-ImmSim with Filippo Castiglione

#11 Collective cell migration with Linus Schumacher

#10 Spatially variable genes and SpatialDE with Valentine Svensson

#9 Michael Tessler and Christopher Mason on 16S amplicon vs shotgun sequencing

#8 Perfect k-mer hashing in Sailfish

#7 Metagenomics and Kraken

#6 Allele-specific expression

#5 Relative data analysis and propr with Thom Quinn

#4 ChIP-seq and GenoGAM with Georg Stricker and Julien Gagneur

#3 miRNA target site prediction and seedVicious with Antonio Marco

#2 Single-cell RNA sequencing with Aleksandra Kolodziejczyk

#1 Transcriptome assembly and Scallop with Mingfu Shao