the bioinformatics chat

the bioinformatics chat

By Roman CheplyakaScienceLife Sciences
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the bioinformatics chat episodes

  • #70 Prioritizing drug target genes with Marie Sadler

    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.

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

      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.

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

        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
          • 50 min
        • #67 AlphaFold and variant effect prediction with Amelie Stein

          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
            • 36 min
          • #66 AlphaFold and shape-mers with Janani Durairaj

            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.
              • 21 min
            • #65 AlphaFold and protein interactions with Pedro Beltrao

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

                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)
                  • 1 hr
                • #63 Bioinformatics Contest 2021 with Maksym Kovalchuk and James Matthew Holt

                  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
                      • 1 hr 1 min
                    • #62 Steady states of metabolic networks and Dingo with Apostolos Chalkis

                      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.
                        • 39 min
                      • #61 3D genome organization and GRiNCH with Da-Inn Erika Lee

                        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)
                          • 1 hr 10 min

                        About the bioinformatics chat

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