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:

September 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 the flagship paper, Expanded encyclopaedias of DNA elements in the human and mouse genomes.
They conclude their discussion by talking about the challenges with
managing a large project as a trainee in a consortium setting.

Links:
Expanded encyclopaedias of DNA elements in the human and mouse genomes (The ENCODE Project Consortium, Jill E. Moore, […], Zhiping Weng)

SCREEN

The ENCODE Portal

The ENCODE3 Publication Bundle

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57min
August 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 interpreted in a new, “most permissive” way.
Loïc proved mathematically that his semantics can reproduce all behaviors
achievable by a compatible quantitative model, whereas the
traditional interpretations in general cannot. Furthermore, it turns out that
deciding whether a certain state in a Boolean network is reachable can be done
much more efficiently in MPBNs than in the traditional interpretations.

Links:
Reconciling Qualitative, Abstract, and Scalable Modeling of Biological Networks (Loïc Paulevé, Juraj Kolčák, Thomas Chatain, Stefan Haar)

mpbn on GitHub: an implementation of reachability and attractor analysis in Most Permissive Boolean Networks

BoNesis on GitHub: synthesis of Most Permissive Boolean Networks from network architecture and dynamical properties

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1h 5min
July 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, predicting ways that existing drugs can
be repurposed, and identifying likely side-effects of combining existing drugs
in novel ways. Focusing on the last of these areas, they then discuss one of
Marinka’s recent papers, Modeling polypharmacy side effects with graph
convolutional networks.

Links:
Modeling polypharmacy side effects with graph convolutional networks (Marinka Zitnik, Monica Agrawal, Jure Leskovec)

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19 (Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Helia Sanchez, Rebecca Marlene Baron, Dina Ghiassian, Joseph Loscalzo, Albert-László Barabási)

AI Cures initiative

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1h 26min
June 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.

Links:
NGLess on GitHub

NG-meta-profiler: fast processing of metagenomes using NGLess, a
domain-specific language
(Luis Pedro Coelho, Renato Alves, Paulo Monteiro, Jaime Huerta-Cepas, Ana Teresa Freitas, Peer Bork)

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58min
May 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 what tricks HiCanu employs to resolve highly similar repeats.

Links:
HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads (Sergey Nurk, Brian P. Walenz, Arang Rhie, Mitchell R. Vollger, Glennis A. Logsdon, Robert Grothe, Karen H. Miga, Evan E. Eichler, Adam M. Phillippy, Sergey Koren)

Canu on GitHub (includes the HiCanu mode)

The Telomere-to-Telomere (T2T) consortium

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1h 10min
May 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.

Links:
Canu on GitHub

SPAdes on GitHub

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1h 17min
April 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 episode, Katie Doroschak explains how Porcupine works—how molbits
are designed and prepared, and how they are directly recognized by the
software without an intermediate basecalling step.

Links:
Porcupine: Rapid and robust tagging of physical objects using nanopore-orthogonal DNA strands (Kathryn Doroschak, Karen Zhang, Melissa Queen, Aishwarya Mandyam, Karin Strauss, Luis Ceze, Jeff Nivala)

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45min
March 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 statistical
properties of the data and avoids spurious correlations (such as one or more
of the top principal components being correlated with the number of non-zero
gene counts).
Also check out Will’s paper for a feature selection algorithm based on
deviance, which we didn’t get a chance to discuss on the podcast.

Links:
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (F. William Townes, Stephanie C. Hicks, Martin J. Aryee, Rafael A. Irizarry)

GLM-PCA for R

GLM-PCA for Python

scry: an R package for feature selection by deviance (alternative to highly variable genes)

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

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1h
February 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 graphs.

Links:
Simplitigs as an efficient and scalable representation of de Bruijn graphs (Karel Břinda, Michael Baym, Gregory Kucherov)

Representation of k-mer sets using spectrum-preserving string sets (Amatur Rahman, Paul Medvedev)

Open mic

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54min
January 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.

Links:
Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation (KV Parag, CA Donnelly)

Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models (KV Parag, CA Donnelly)

The listener survey

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1h 9min

FAQs about the bioinformatics chat:

How many episodes does the bioinformatics chat have?

The podcast currently has 70 episodes available.