#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.

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

May 31, 2019

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.

In this episode Mikhail Kolmogorov from Pavel Pevzner’s lab joins us to talk about some of the ideas developed in the lab that made it possible to build a de Bruijn-like assembly graph from noisy reads. These ideas are now implemented in the Flye assembler, which performs much faster than the existing long read assemblers without sacrificing the quality of the assembly.

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

April 29, 2019

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 representation from the wealth of experimental data from projects like the Roadmap Epigenomics Consortium and ENCODE. This representation allows you to impute genome-wide epigenomics experiments that have not yet been performed.

Jacob also talks about a pitfall he discovered when trying to predict gene expression from a mix of genomic and epigenomic data. As you increase the complexity of a machine learning model, its performance may be increasing for the wrong reason: instead of learning something biologically interesting, your model may simply be memorizing the average gene expression for that gene across your training cell types using the nucleotide sequence.

Previous episodes

#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