the bioinformatics chat

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.

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

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.

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)

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.

In this episode, Michael Love joins us to talk about the differential gene expression analysis from bulk RNA-Seq data. 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 dispersion prior, references are the Robinson and Smyth 2007 paper, McCarthy et al 2012 (edgeR), and Wu et al 2013 (DSS): https://pubmed.ncbi.nlm.nih.gov/17881408/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3378882/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590927/ Schurch et al 2016, a RNA-seq dataset with many replicates, helpful for benchmarking: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878611/ Stephens paper on the false sign rate (ash): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5379932/ Heavy-tailed distributions for effect sizes, Zhu et al 2018: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581436/ I credit Kevin Blighe and Alexander Toenges, who help to answer lots of DESeq2 questions on the support site: https://www.biostars.org/u/41557/ https://www.biostars.org/u/25721/ The EOSS award, which has funded vizWithSCE by Kwame Forbes, and nullranges by Wancen Mu and Eric Davis: https://chanzuckerberg.com/eoss/proposals/ensuring-reproducible-transcriptomic-analysis-with-deseq2-and-tximeta/ https://kwameforbes.github.io/vizWithSCE/ https://nullranges.github.io/nullranges/ One of the recent papers from my lab, MRLocus for eQTL and GWAS integration: https://mikelove.github.io/mrlocus/

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?» 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

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. 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)

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. 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)

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. 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

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

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

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