
In this episode, Marie Sadler talks
about her recent Cell Genomics paper, Multi-layered genetic approaches to
identify approved drug targets.
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.
Dec 21, 2023
52 min

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.
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.
Sep 29, 2023
56 min

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.
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
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Aug 28, 2023
49 min

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.
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.
Other 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)
A crime in the making: Russia’s atrocities — the podcast episode about the Olenivka prison massacre
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Jul 29, 2023
35 min

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.
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.
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Jul 10, 2023
20 min

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.
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
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Jun 21, 2023
52 min

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.
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)
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Nov 9, 2021
59 min

The Bioinformatics Contest is back this year, and we are back to discuss
it!
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
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Sep 27, 2021
1 hr

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.
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.
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Jul 28, 2021
38 min

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.
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)
If you enjoyed this episode, please consider supporting the podcast on Patreon.
Jun 23, 2021
1 hr 9 min
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