#64 Enformer with Žiga Avsec
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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
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#63 Bioinformatics Contest 2021 with Maksym Kovalchuk and James Matthew Holt
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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
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#62 Steady states of metabolic networks and Dingo with Apostolos Chalkis
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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
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#61 GRiNCH with Da-Inn Erika Lee
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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
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#60 Differential gene expression and DESeq2 with Michael Love
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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
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#59 Proteomics calibration with Lindsay Pino
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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. Links: Talus Bioscience Matrix-Matched Calibration Curves for Asssessing Analytical Figures ..read more
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#58 B cell maturation and class switching with Hamish King
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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 shape ..read more
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#57 Enhancers with Molly Gasperini
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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. Links: Octant Bio Towards a comprehensive catalogue of validated and target-linked human enhancers (Molly Gasperini, Jacob M. Tome, and Jay Shendure ..read more
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#56 Polygenic risk scores in admixed populations with Bárbara Bitarello
The Bioinformatics Chat
by Roman Cheplyaka
1y ago
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 Mathieso ..read more
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#55 Phylogenetics and the likelihood gradient with Xiang Ji
The Bioinformatics Chat
by Roman Cheplyaka
2y ago
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. 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 ..read more
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