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 ..read more

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 s ..read more

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 Amel ..read more

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 ??G 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 ??G 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 A ..read more

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 ..read more

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 ..read more

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 ..read more

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 ..read more

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 cou ..read more

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 Reprodu ..read more