Alle afleveringen van the bioinformatics chat

#70 Prioritizing drug target genes with Marie Sadler

21 december 2023 · 52 min

Afspelen

In this episode, Marie Sadler talks
about her recent Cell Genomics paper, Multi-layered genetic approaches to
identify approved drug targets. Lees meer

Previous studies have found that the drugs that target a gene linked to the
disease are more likely to be approved. Yet there are many ways to define what
it means for a gene to be linked to the disease. Perhaps the most
straightforward approach is to rely on the genome-wide association studies (GWAS) data,
but that data can also be integrated with quantitative trait loci (eQTL or pQTL) information
to establish less obvious links between genetic variants (which often lie
outside of genes) and genes. Finally, there’s exome sequencing, which, unlike
GWAS, captures rare genetic variants. So in this paper, Marie and her
colleagues set out to benchmark these different methods against one another.

Listen to the episode to find out how these methods work, which ones
work better, and how network propagation can improve the prediction accuracy.

Links:

Multi-layered genetic approaches to identify approved drug targets
(Marie C. Sadler, Chiara Auwerx, Patrick Deelen, Zoltán Kutalik)

Marie on GitHub

Interview with Mariana Mamonova, the Ukrainian marine infantry combat medic who spent 6 months in russian captivity while pregnant

Thank you to Jake Yeung, Michael Weinstein, and other Patreon members for supporting this episode.

#69 Suffix arrays in optimal compressed space and δ-SA with Tomasz Kociumaka and Dominik Kempa

29 september 2023 · 57 min

Afspelen

Today on the podcast we have Tomasz Kociumaka and Dominik Kempa,
the authors of the preprint
Collapsing the Hierarchy of Compressed Data Structures: Suffix Arrays in Optimal Compressed Space. Lees meer

The suffix array is one of the foundational data structures in bioinformatics,
serving as an index that allows fast substring searches in a large text.
However, in its raw form, the suffix array occupies the space proportional to (and
several times larger than) the original text.

In their paper, Tomasz and Dominik construct a new index, δ-SA, which on the
one hand can be used in the same way (answer the same queries) as the suffix
array and the inverse suffix array, and on the other hand, occupies the space
roughly proportional to the gzip’ed text (or, more precisely, to the measure δ
that they define — hence the name).

Moreover, they mathematically prove that this index is optimal, in the sense
that any index that supports these queries — or even much weaker queries, such
as simply accessing the i-th character of the text — cannot be significantly
smaller (as a function of δ) than δ-SA.

Links:

Collapsing the Hierarchy of Compressed Data Structures: Suffix Arrays in Optimal Compressed Space (Dominik Kempa, Tomasz Kociumaka)

Thank you to Jake Yeung and other Patreon members for supporting this episode.

#68 Phylogenetic inference from raw reads and Read2Tree with David Dylus

28 augustus 2023 · 49 min

Afspelen

In this episode,
David Dylus talks about
Read2Tree,
a tool that builds alignment matrices and phylogenetic trees from raw
sequencing reads.
By leveraging the database of orthologous genes called OMA, Read2Tree bypasses traditional, time-consuming steps such as genome assembly, annotation and all-versus-all sequence comparisons. Lees meer

Links:

Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree
(David Dylus, Adrian Altenhoff, Sina Majidian, Fritz J. Sedlazeck, Christophe Dessimoz)

Background story

Read2Tree on GitHub

OMA browser

The Guardian’s podcast about Victoria Amelina and Volodymyr Vakulenko

#67 AlphaFold and variant effect prediction with Amelie Stein

29 juli 2023 · 35 min

Afspelen

This is the third and final episode in the AlphaFold series, originally recorded on February 23, 2022,
with Amelie Stein, now an associate professor at the University of Copenhagen. Lees meer

In the episode, Amelie explains what 𝛥𝛥G is, how it informs us
whether a particular protein mutation affects its stability, and how AlphaFold 2
helps in this analysis.

A note from Amelie:

Something that has happened in the meantime is the publication of methods
that predict 𝛥𝛥G with ML methods, so much faster than Rosetta. One of
them, RaSP, is from our group, while
ddMut is from another subset of
authors of the AF2 community assessment paper.

#66 AlphaFold and shape-mers with Janani Durairaj

10 juli 2023 · 21 min

Afspelen

This is the second episode in the AlphaFold series, originally recorded on February 14, 2022,
with Janani Durairaj, a postdoctoral
researcher at the University of Basel. Lees meer

Janani talks about how she used shape-mers and topic modelling to discover
classes of proteins assembled by AlphaFold 2 that were absent from the Protein
Data Bank (PDB).

The bioinformatics discussion starts at 03:35.

Links:

A structural biology community assessment of AlphaFold2 applications
(Mehmet Akdel, Douglas E. V. Pires, Eduard Porta Pardo, Jürgen Jänes, Arthur O. Zalevsky, Bálint Mészáros, Patrick Bryant, Lydia L. Good, Roman A. Laskowski, Gabriele Pozzati, Aditi Shenoy, Wensi Zhu, Petras Kundrotas, Victoria Ruiz Serra, Carlos H. M. Rodrigues, Alistair S. Dunham, David Burke, Neera Borkakoti, Sameer Velankar, Adam Frost, Jérôme Basquin, Kresten Lindorff-Larsen, Alex Bateman, Andrey V. Kajava, Alfonso Valencia, Sergey Ovchinnikov, Janani Durairaj, David B. Ascher, Janet M. Thornton, Norman E. Davey, Amelie Stein, Arne Elofsson, Tristan I. Croll & Pedro Beltrao)

The Protein Universe Atlas

What is hidden in the darkness? Deep-learning assisted large-scale protein family curation uncovers novel protein families and folds (Janani Durairaj, Andrew M. Waterhouse, Toomas Mets, Tetiana Brodiazhenko, Minhal Abdullah, Gabriel Studer, Mehmet Akdel, Antonina Andreeva, Alex Bateman, Tanel Tenson, Vasili Hauryliuk, Torsten Schwede, Joana Pereira)

Geometricus: Protein Structures as Shape-mers derived from Moment Invariants on GitHub

The group page

The Folded Weekly newsletter

A New York Times article about the Kramatorsk missile strike. The Instagram video, part of which you can hear at the beginning of the episode, appears to have been deleted.

#65 AlphaFold and protein interactions with Pedro Beltrao

21 juni 2023 · 52 min

Afspelen

In this episode, originally recorded on February 9, 2022,
Roman talks to Pedro Beltrao
about AlphaFold, the software developed by DeepMind that predicts a protein’s
3D structure from its amino acid sequence. Lees meer

Pedro is an associate professor at ETH Zurich and the coordinator of
the structural biology community assessment of AlphaFold2 applications project,
which involved over 30 scientists from different institutions.

Pedro talks about the origins of the project,
its main findings, the importance of the confidence metric that AlphaFold
assigns to its predictions, and Pedro’s own area of interest — predicting
pockets in proteins and protein-protein interactions.

Links:

A structural biology community assessment of AlphaFold2 applications
(Mehmet Akdel, Douglas E. V. Pires, Eduard Porta Pardo, Jürgen Jänes, Arthur O. Zalevsky, Bálint Mészáros, Patrick Bryant, Lydia L. Good, Roman A. Laskowski, Gabriele Pozzati, Aditi Shenoy, Wensi Zhu, Petras Kundrotas, Victoria Ruiz Serra, Carlos H. M. Rodrigues, Alistair S. Dunham, David Burke, Neera Borkakoti, Sameer Velankar, Adam Frost, Jérôme Basquin, Kresten Lindorff-Larsen, Alex Bateman, Andrey V. Kajava, Alfonso Valencia, Sergey Ovchinnikov, Janani Durairaj, David B. Ascher, Janet M. Thornton, Norman E. Davey, Amelie Stein, Arne Elofsson, Tristan I. Croll & Pedro Beltrao)

Pedro’s group at ETH Zurich

#64 Enformer: predicting gene expression from sequence with Žiga Avsec

9 november 2021 · 1 uur

Afspelen

In this episode, Jacob Schreiber interviews Žiga Avsec about
a recently released model, Enformer. Their discussion begins with life
differences between academia and industry, specifically about how research
is conducted in the two settings. Then, they discuss the Enformer model,
how it builds on previous work, and the potential that models like it have
for genomics research in the future. Finally, they have a high-level discussion
on the state of modern deep learning libraries and which ones they use in their
day-to-day developing. Lees meer

Links:

Effective gene expression prediction from sequence by integrating long-range interactions (Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli & David R. Kelley )

DeepMind Blog Post (Žiga Avsec)

#63 Bioinformatics Contest 2021 with Maksym Kovalchuk and James Matthew Holt

27 september 2021 · 1 uur 1 min

Afspelen

The Bioinformatics Contest is back this year, and we are back to discuss
it! Lees meer

This year’s contest winners
Maksym Kovalchuk (1st prize) and
Matt Holt (2nd prize)
talk about how they approach
participating in the contest and what strategies have earned them the top
scores.

Timestamps and links for the individual problems:

00:10:36 Genotype Imputation

00:21:26 Causative Mutation

00:30:27 Superspreaders

00:37:22 Minor Haplotype

00:46:37 Isoform Matching

Links:

Matt’s solutions

Max’s solutions

#62 Steady states of metabolic networks and Dingo with Apostolos Chalkis

28 juli 2021 · 38 min

Afspelen

In this episode, Apostolos Chalkis presents sampling steady
states of metabolic networks as an alternative to the widely used flux balance
analysis (FBA). We also discuss dingo, a
Python package written by Apostolos that employs geometric random walks to
sample steady states. You can see dingo in action
here. Lees meer

Links:

Dingo on GitHub

Searching for COVID-19 treatments using metabolic networks

Tweag open source fellowships

This episode was originally published on the Compositional podcast.

#61 3D genome organization and GRiNCH with Da-Inn Erika Lee

23 juni 2021 · 1 uur 10 min

Afspelen

In this episode, Jacob Schreiber interviews Da-Inn Erika Lee about
data and computational methods for making sense of 3D genome structure. They
begin their discussion by talking about 3D genome structure at a high level
and the challenges in working with such data. Then, they discuss a method
recently developed by Erika, named GRiNCH, that mines this data to
identify spans of the genome that cluster together in 3D space and
potentially help control gene regulation. Lees meer

Links:

GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization (Da-Inn Lee and Sushmita Roy)

GRiNCH Project Page

In silico prediction of high-resolution Hi-C interaction matrices (Shilu Zhang, Deborah Chasman, Sara Knaack, and Sushmita Roy)

#60 Differential gene expression and DESeq2 with Michael Love

12 mei 2021 · 1 uur 31 min

Afspelen

In this episode, Michael Love joins us to talk about the differential gene
expression analysis from bulk RNA-Seq data. Lees meer

We talk about the history of Mike’s own differential expression package,
DESeq2, as well as other packages in this space, like edgeR and limma, and the
theory they are based upon. Mike also shares his experience of being the
author and maintainer of a popular bioninformatics package.

Links:

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
(Love, M.I., Huber, W. & Anders, S.)

DESeq2 on Bioconductor

Chan Zuckerberg Initiative: Ensuring Reproducible Transcriptomic Analysis with DESeq2 and tximeta

And a more comprehensive set of links from Mike himself:

limma, the original paper and limma-voom:

https://pubmed.ncbi.nlm.nih.gov/16646809/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053721/

edgeR papers:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3378882/

The recent manuscript mentioned from the Kendziorski lab, which has a Gamma-Poisson hierarchical structure, although it does not in general reduce to the Negative Binomial:

https://doi.org/10.1101/2020.10.28.359901

We talk about robust steps for estimating the middle of the dispersion prior distribution, references are Anders and Huber 2010 (DESeq), Eling et al 2018 (one of the BASiCS papers), and Phipson et al 2016:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218662/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167088/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373812/

The Stan software:

https://mc-stan.org/

We talk about using publicly available data as a prior, references I mention are the McCall et al paper using publicly available data to ask if a gene is expressed, and a new manuscript from my lab that compares splicing in a sample to GTEx as a reference panel:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013751/
https://doi.org/10.1101/856401

Regarding estimating the width of the dis…

#59 Proteomics calibration with Lindsay Pino

21 april 2021 · 48 min

Afspelen

In this episode, Lindsay Pino discusses the
challenges of making quantitative measurements in the field of proteomics.
Specifically, she discusses the difficulties of comparing measurements across
different samples, potentially acquired in different labs, as well as a method
she has developed recently for calibrating these measurements without the need
for expensive reagents. The discussion then turns more broadly to questions in
genomics that can potentially be addressed using proteomic measurements. Lees meer

Links:

Talus Bioscience

Matrix-Matched Calibration Curves for Asssessing Analytical Figures of Merit in Quantitative Proteomics
(Lindsay K. Pino, Brian C. Searle, Han-Yin Yang, Andrew N. Hoofnagle, William S. Noble, and Michael J. MacCross)

#58 B cell maturation and class switching with Hamish King

31 maart 2021 · 1 uur 29 min

Afspelen

In this episode, we learn about B cell maturation and class switching from
Hamish King. Hamish recently published a
paper on this subject in Science Immunology, where he and his coauthors
analyzed gene expression and antibody repertoire data from human tonsils.
In the episode Hamish talks about some of the interesting B cell states he
uncovered and shares his thoughts on questions such as «When does a B cell
decide to class-switch?» and «Why is the antibody isotype correlated with its
affinity?» Lees meer

Links:

Single-cell analysis of human B cell maturation predicts how antibody class switching shapes selection dynamics
(Hamish W. King, Nara Orban, John C. Riches, Andrew J. Clear, Gary Warnes, Sarah A. Teichmann, Louisa K. James) (paywalled by Science Immunology)

Antibody repertoire and gene expression dynamics of diverse human B cell states during affinity maturation
(the preprint of the above Science Immunology paper)

www.tonsilimmune.org: An immune cell atlas of the human tonsil and B cell maturation

#57 Enhancers with Molly Gasperini

10 maart 2021 · 47 min

Afspelen

In this episode, Jacob Schreiber interviews Molly Gasperini about
enhancer elements. They begin their discussion by talking about Octant Bio,
and then dive into the surprisingly difficult task of defining enhancers and
determining the mechanisms that enable them to regulate gene expression. Lees meer

Links:

Octant Bio

Towards a comprehensive catalogue of validated and target-linked human enhancers (Molly Gasperini, Jacob M. Tome, and Jay Shendure)

#56 Polygenic risk scores in admixed populations with Bárbara Bitarello

17 februari 2021 · 1 uur 30 min

Afspelen

Polygenic risk scores (PRS) rely on the genome-wide association studies (GWAS)
to predict the phenotype based on the genotype. However, the prediction
accuracy suffers when GWAS from one population are used to calculate PRS within
a different population, which is a problem because the majority of the GWAS
are done on cohorts of European ancestry. Lees meer

In this episode, Bárbara Bitarello helps us
understand how PRS work and why they don’t transfer well across populations.

Links:

Polygenic Scores for Height in Admixed Populations (Bárbara D. Bitarello, Iain Mathieson)

What is ancestry? (Iain Mathieson, Aylwyn Scally)

#55 Phylogenetics and the likelihood gradient with Xiang Ji

13 januari 2021 · 57 min

Afspelen

In this episode, we chat about phylogenetics with Xiang Ji. We start with a
general introduction to the field and then go deeper into the likelihood-based
methods (maximum likelihood and Bayesian inference). In particular, we talk
about the different ways to calculate the likelihood gradient, including a
linear-time exact gradient algorithm recently published by Xiang and his
colleagues. Lees meer

Links:

Gradients Do Grow on Trees: A Linear-Time O(N)-Dimensional Gradient for Statistical Phylogenetics
(Xiang Ji, Zhenyu Zhang, Andrew Holbrook, Akihiko Nishimura, Guy Baele, Andrew Rambaut, Philippe Lemey, Marc A Suchard)

BEAGLE: the package that implements the gradient algorithm

BEAST: the program that implements the Hamiltonian Monte Carlo sampler and the molecular clock models

#54 Seeding methods for read alignment with Markus Schmidt

16 december 2020 · 1 uur 1 min

Afspelen

In this episode, Markus Schmidt explains how seeding in read alignment works.
We define and compare k-mers, minimizers, MEMs, SMEMs, and maximal spanning seeds.
Markus also presents his recent work on computing variable-sized seeds (MEMs,
SMEMs, and maximal spanning seeds) from fixed-sized seeds (k-mers and
minimizers) and his Modular Aligner. Lees meer

Links:

A performant bridge between fixed-size and variable-size seeding
(Arne Kutzner, Pok-Son Kim, Markus Schmidt)

MA the Modular Aligner

Calibrating Seed-Based Heuristics to Map Short Reads With Sesame
(Guillaume J. Filion, Ruggero Cortini, Eduard Zorita) — another
interesting recent work on seeding methods (though we didn’t get to discuss
it in this episode)

#53 Real-time quantitative proteomics with Devin Schweppe

18 november 2020 · 1 uur 3 min

Afspelen

In this episode, Jacob Schreiber interviews Devin Schweppe about
the analysis of mass spectrometry data in the field of proteomics. They begin
by delving into the different types of mass spectrometry methods, including MS1,
MS2, and, MS3, and the reasons for using each. They then discuss a recent paper
from Devin, Full-Featured, Real-Time Database Searching Platform Enables Fast
and Accurate Multiplexed Quantitative Proteomics that involved building a
real-time system for quantifying proteomic samples from MS3, and the types of
analyses that this system allows one to do. Lees meer

Links:

Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics (Devin K. Schweppe, Jimmy K. Eng, Qing Yu, Derek Bailey, Ramin Rad, Jose Navarrete-Perea, Edward L. Huttlin, Brian K. Erickson, Joao A. Paulo, and Steven P. Gygi)

Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics (Qing Yu, Joao A Paulo, Jose Naverrete-Perea, Graeme C McAlister, Jesse D Canterbury, Derek J Bailey, Aaron M Robitaille, Romain Huguet, Vlad Zabrouskov, Steven P Gygi, Devin K Schweppe)

Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage (Ramin Rad, Jiaming Li, Julian Mintseris, Jeremy O’Connell, Steven P. Gygi, and Devin K. Schweppe)

Schweppe Lab Website (Hiring!)

#52 How 23andMe finds identical-by-descent segments with William Freyman

27 oktober 2020 · 43 min

Afspelen

In this episode, Will Freyman talks about identity-by-descent (IBD): how
it’s used at 23andMe, and how the templated
positional Burrows-Wheeler transform can find IBD segments in the presence of
genotyping and phasing errors. Lees meer

Links:

Fast and robust identity-by-descent inference with the templated positional Burrows-Wheeler transform
(William A. Freyman, Kimberly F. McManus, Suyash S. Shringarpure, Ethan M. Jewett, Katarzyna Bryc, the 23andMe Research Team, Adam Auton)

23andMe research

#51 Basset and Basenji with David Kelley

7 oktober 2020 · 1 uur 14 min

Afspelen

In this episode, Jacob Schreiber interviews David Kelley about
machine learning models that can yield insight into the consequences of
mutations on the genome. They begin their discussion by talking about
Calico Labs, and then delve into a series of papers that David has
written about using models, named Basset and Basenji, that connect genome
sequence to functional activity and so can be used to quantify the effect of
any mutation. Lees meer

Links:

Calico Labs

Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks (David R. Kelley, Jasper Snoek, and John Rinn)

Sequential regulatory activity prediction across chromosomes with convolutional neural networks (David R. Kelley, Yakir A. Reshef, Maxwell Bileschi, David Belanger, Cory Y. McLean, and Jaspar Snoek)

Cross-species regulatory sequence activity prediction (David R. Kelley)

Basenji GitHub Repo

#50 ENCODE3 with Jill Moore

10 september 2020 · 56 min

Afspelen

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. Lees meer

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

#49 Most Permissive Boolean Networks with Loïc Paulevé

19 augustus 2020 · 1 uur 4 min

Afspelen

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. Lees meer

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

#48 Machine learning for drug development with Marinka Zitnik

29 juli 2020 · 1 uur 25 min

Afspelen

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. Lees meer

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

#47 Reproducible pipelines and NGLess with Luis Pedro Coelho

24 juni 2020 · 58 min

Afspelen

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. Lees meer

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)

#46 HiFi reads and HiCanu with Sergey Nurk and Sergey Koren

27 mei 2020 · 1 uur 9 min

Afspelen

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. Lees meer

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

#45 Genome assembly and Canu with Sergey Koren and Sergey Nurk

20 mei 2020 · 1 uur 17 min

Afspelen

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. Lees meer

Links:

Canu on GitHub

SPAdes on GitHub

#44 DNA tagging and Porcupine with Kathryn Doroschak

29 april 2020 · 45 min

Afspelen

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. Lees meer

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)

#43 Generalized PCA for single-cell data with William Townes

27 maart 2020 · 1 uur

Afspelen

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). Lees meer

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)

#42 Spectrum-preserving string sets and simplitigs with Amatur Rahman and Karel Břinda

28 februari 2020 · 53 min

Afspelen

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. Lees meer

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

#41 Epidemic models with Kris Parag

27 januari 2020 · 1 uur 8 min

Afspelen

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. Lees meer

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

#40 Plasmid classification and binning with Sergio Arredondo-Alonso and Anita Schürch

30 december 2019 · 45 min

Afspelen

Does a given bacterial gene live on a plasmid or the chromosome? What
other genes live on the same plasmid? Lees meer

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)

#39 Amplicon sequence variants and bias with Benjamin Callahan

29 november 2019 · 1 uur 2 min

Afspelen

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: Lees meer

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)

#38 Issues in legacy genomes with Luke Anderson-Trocmé

22 oktober 2019 · 1 uur 1 min

Afspelen

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. Lees meer

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)

#37 Causality and potential outcomes with Irineo Cabreros

27 september 2019 · 41 min

Afspelen

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). Lees meer

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)

#36 scVI with Romain Lopez and Gabriel Misrachi

30 augustus 2019 · 1 uur 20 min

Afspelen

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. Lees meer

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)

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

26 juli 2019 · 1 uur 2 min

Afspelen

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. Lees meer

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

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

29 juni 2019 · 1 uur 27 min

Afspelen

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. Lees meer

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?

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

31 mei 2019 · 1 uur 13 min

Afspelen

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. Lees meer

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

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

29 april 2019 · 1 uur 15 min

Afspelen

In this episode, we hear from Jacob Schreiber about his algorithm,
Avocado. Lees meer

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)

#31 Bioinformatics Contest 2019 with Alexey Sergushichev and Gennady Korotkevich

24 maart 2019 · 1 uur 46 min

Afspelen

The third Bioinformatics Contest took place in
February 2019. Lees meer

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

#30 Bayesian inference of chromatin structure from Hi-C data with Simeon Carstens

27 februari 2019 · 1 uur 6 min

Afspelen

Hi-C is a sequencing-based assay that provides information about the 3-dimensional organization of the genome.
In this episode, Simeon Carstens explains how he
applied the Inferential Structure Determination (ISD) framework to build a 3D
model of chromatin and fit that model to Hi-C data using Hamiltonian Monte
Carlo and Gibbs sampling. Lees meer

Links:

Bayesian inference of chromatin structure ensembles from population Hi-C data (Simeon Carstens, Michael Nilges, Michael Habeck)

Inferential Structure Determination of Chromosomes from Single-Cell Hi-C Data (Simeon Carstens, Michael Nilges, Michael Habeck)

#29 Haplotype-aware genotyping from long reads with Trevor Pesout

27 januari 2019 · 1 uur 12 min

Afspelen

Long read sequencing technologies, such as Oxford Nanopore and PacBio,
produce reads from thousands to a million base pairs in length,
at the cost of the increased error rate. Trevor Pesout
describes how he and his colleagues leverage long reads for simultaneous
variant calling/genotyping and phasing. This is possible thanks to a clever
use of a hidden Markov model, and two different algorithms based on this model
are now implemented in
the MarginPhase and WhatsHap tools. Lees meer

Links:

Preprint: Haplotype-aware genotyping from noisy long reads (Jana Ebler, Marina Haukness, Trevor Pesout, Tobias Marschall, Benedict Paten)

#28 Space-efficient variable-order Markov models with Fabio Cunial

28 december 2018 · 1 uur 9 min

Afspelen

This time you’ll hear from Fabio Cunial on the topic of Markov models and
space-efficient data structures. First we recall what a Markov model is and
why variable-order Markov models are an improvement over the standard,
fixed-order models. Next we discuss the various data structures and indexes
that allowed Fabio and his collaborators to represent these models in a very
small space while still keeping the queries efficient. Burrows-Wheeler
transform, suffix trees and arrays, tries and suffix link trees, and more! Lees meer

Links:

The preprint: A framework for space-efficient variable-order Markov models

The book: Genome-Scale Algorithm Design

The GitHub repo

#27 Classification of CRISPR-induced mutations and CRISPRpic with HoJoon Lee and Seung Woo Cho

29 november 2018 · 57 min

Afspelen

In this episode, HoJoon Lee and Seung Woo Cho explain how to perform a CRISPR
experiment and how to analyze its results. HoJoon and Seung Woo developed an
algorithm that analyzes sequenced amplicons containing the CRISPR-induced
double-strand break site and figures out what exactly happened there (e.g.
a deletion, insertion, substitution etc.) Lees meer

Links:

CRISPRpic: Fast and precise analysis for CRISPR-induced mutations via prefixed index counting

CRISPRpic on GitHub

#26 Feature selection, Relief and STIR with Trang Lê

27 oktober 2018 · 1 uur 9 min

Afspelen

Relief is a statistical method to perform feature selection. It could be used,
for instance, to find genomic loci that correlate with a trait or genes whose
expression correlate with a condition. Relief can also be made sensitive to
interaction effects (known in genetics as epistasis). Lees meer

In this episode, Trang Lê joins me
to talk about Relief and her version of Relief called STIR (STatistical
Inference Relief). While traditional Relief algorithms could only rank
features and needed a user-supplied threshold to decide which features to
select, Trang’s reformulation of Relief allowed her to compute p-values
and make the selection process less arbitrary.

Links:

Paper: STatistical Inference Relief (STIR) feature selection

STIR on GitHub

Relief on Wikipedia

The original Relief paper by Kira and Rendell (1992)

Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans

#25 Transposons and repeats with Kaushik Panda and Keith Slotkin

24 september 2018 · 1 uur 41 min

Afspelen

Kaushik Panda and Keith Slotkin come on the podcast to educate us about
repetitive DNA and transposable elements. We talk LINEs, SINEs, LTRs, and even
Sleeping Beauty transposons! Kaushik and Keith explain why repeats matter for your
whole-genome analysis and answer listeners’ questions. Lees meer

Links:

Keith’s paper: The case for not masking away repetitive DNA

Questions for this episode on Reddit

#24 Read correction and Bcool with Antoine Limasset

31 augustus 2018 · 1 uur

Afspelen

Antoine Limasset joins me to talk about NGS read correction.
Antoine and his colleagues built the read correction tool Bcool based on the
de Bruijn graph, and it corrects reads far better than any of the current methods
like Bloocoo, Musket, and Lighter. Lees meer

We discuss why and when read correction is needed, how Bcool works, and why
it performs better but slower than k-mer spectrum methods.

Links:

Preprint: Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs

Bcool on GitHub

#23 RNA design, EteRNA and NEMO with Fernando Portela

27 juli 2018 · 1 uur 31 min

Afspelen

In this episode, I talk to Fernando Portela,
a software engineer and
amateur scientist
who works on RNA design — the problem of composing an RNA sequence
that has a specific secondary structure. Lees meer

We talk about how Fernando and others compete and collaborate in designing RNA
molecules in the online game EteRNA and about Fernando’s new
RNA design algorithm, NEMO, which outperforms all prior published methods by a wide margin.

Links:

The EteRNA game

The preprint about NEMO

NEMO project page

Single-cell RNABIO & organoids meeting in Kiev

#22 smCounter2: somatic variant calling and UMIs with Chang Xu

29 juni 2018 · 1 uur 4 min

Afspelen

In this episode I’m joined by Chang Xu. Chang is a senior biostatistician
at QIAGEN and an author of smCounter2, a low-frequency somatic variant caller.
To distinguish rare somatic mutations from sequencing errors, smCounter2
relies on unique molecular identifiers, or UMIs, which help identify multiple
reads resulting from the same physical DNA fragment. Lees meer

Chang explains what UMIs are, why they are useful, and how smCounter2 and other
tools in this space use UMIs to detect low-frequency variants.

Links:

smCounter2 preprint

smCounter2 github repository

smCounter publication

Review of somatic SNV callers

#21 Linear mixed models, GWAS, and lme4qtl with Andrey Ziyatdinov

31 mei 2018 · 51 min

Afspelen

Linear mixed models are used to analyze GWAS data and detect QTLs.
Andrey Ziyatdinov recently released an R package, lme4qtl, that can be used to
formulate and fit these models.
In this episode, Andrey and I discuss linear mixed models, genome-wide association studies, and strengths and weaknesses of lme4qtl. Lees meer

Links:

Paper: lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

lme4qtl on GitHub