the bioinformatics chat

By Roman Cheplyaka

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

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FAQs about the bioinformatics chat:

How many episodes does the bioinformatics chat have?

The podcast currently has 70 episodes available.

the bioinformatics chat episodes:

December 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.

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. Verschuuren, Janetta Top, Jukka Corander, Rob J. L. Willems, Anita C. Schürch)

gplas: a comprehensive tool for plasmid analysis using short-read graphs (Sergio Arredondo-Alonso, Martin Bootsma, Yaïr Hein, Malbert R.C. Rogers, Jukka Corander, Rob JL Willems, Anita C. Schürch)

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46min
November 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 sequence variants (also called amplicon sequence variants, or
ASVs), and how DADA2 manages to deduce the exact sequences present in the
sample.

Why abundances inferred from community sequencing data are biased, and
how we can model and correct this bias.

Links:
Exact sequence variants should replace operational taxonomic units in marker-gene data analysis (Benjamin J Callahan, Paul J McMurdie & Susan P Holmes)

DADA2: High-resolution sample inference from Illumina amplicon data (Benjamin J Callahan, Paul J McMurdie, Michael J Rosen, Andrew W Han, Amy Jo A Johnson & Susan P Holmes)

In Nature, There Is Only Diversity (Michael R. McLaren, Benjamin J. Callahan)

Consistent and correctable bias in metagenomic sequencing experiments (Michael R McLaren, Amy D Willis, Benjamin J Callahan)

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1h 2min
October 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 to Luke telling the story
of how he stumbled upon and investigated these fake variants and what their
impact is.

Links:
Legacy Data Confounds Genomics Studies (bioRxiv, Molecular Biology and Evolution (paywall)) (Luke Anderson-Trocmé, Rick Farouni, Mathieu Bourgey, Yoichiro Kamatani, Koichiro Higasa, Jeong-Sun Seo, Changhoon Kim, Fumihiko Matsuda and Simon Gravel)

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1h 2min
September 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 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).

Links:
Causal models on probability spaces (Irineo Cabreros, John D. Storey)

The Book of Why: The New Science of Cause and Effect (Judea Pearl, Dana Mackenzie)

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41min
August 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 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.

Links:
Deep generative modeling for single-cell transcriptomics (Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, Nir Yosef)

scVI on GitHub

Should we zero-inflate scVI?

Hyperparameter search for scVI

Droplet scRNA-seq is not zero inflated (Valentine Svensson)

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1h 21min
July 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 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.

Links:
A De Novo Shape Motif Discovery Algorithm Reveals Preferences of Transcription Factors for DNA Shape Beyond Sequence Motifs (Md. Abul Hassan Samee, Benoit G. Bruneau, Katherine Pollard)

ShapeMF on GitHub

A picture explaining some of the DNA shape features

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1h 2min
June 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 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.

Links:
Single-cell sequencing reveals αβ chain pairing shapes the T cell repertoire. Kristina Grigaityte, Jason A. Carter, Stephen J. Goldfless, Eric W. Jeffery, Ronald J. Hause, Yue Jiang, David Koppstein, Adrian W. Briggs, George M. Church, Francois Vigneault, Gurinder S. Atwal

Bayesian inference of power law distributions. Kristina Grigaityte, Gurinder Atwal

Mathematics in modern immunology. Castro M, Lythe G, Molina-París C, Ribeiro RM.

Power laws, Pareto distributions and Zipf’s law. M. E. J. Newman

So You Think You Have a Power Law — Well Isn’t That Special?

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1h 27min
May 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 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.

Links:
Assembly of Long Error-Prone Reads Using Repeat Graphs (Mikhail Kolmogorov,
Jeffrey Yuan, Yu Lin, Pavel. A. Pevzner.
Nature Biotechnology
(paywalled),
bioRxiv

Flye on GitHub

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1h 13min
April 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 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.

Links:
Avocado on GitHub

Multi-scale deep tensor factorization learns a latent representation of the human epigenome (Jacob Schreiber, Timothy Durham, Jeffrey Bilmes, William Stafford Noble)

Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples (Jacob Schreiber, Jeffrey Bilmes, William Noble)

A pitfall for machine learning methods aiming to predict across cell types (Jacob Schreiber, Ritambhara Singh, Jeffrey Bilmes, William Stafford Noble)

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1h 16min
March 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.

Timestamps and links for the individual problems:
Qualification round
00:07:14 Bee Population

00:14:12 Sequencing Errors

00:30:20 Transposable Elements

Final round
00:41:35 Cancer and Chromosome Rearrangements

00:56:01 Epigenomic Marks

01:10:02 Bacterial Communities

01:27:06 Minimal Genome

01:34:56 Endangered Species

Links:
The contest problems on Stepik

The final scoreboard

Episode #18: Bioinformatics Contest 2018

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1h 47min

FAQs about the bioinformatics chat:

How many episodes does the bioinformatics chat have?

The podcast currently has 70 episodes available.

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