the bioinformatics chat

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

https://bioinformatics.chat

Eine durchschnittliche Folge dieses Podcasts dauert 1h3m. Bisher sind 63 Folge(n) erschienen. .

Gesamtlänge aller Episoden: 2 days 20 hours 19 minutes

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episode 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...


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   1h0m
 
 

episode 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...


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 2021-07-28  38m
 
 

episode 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...


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 2021-06-23  1h9m
 
 

episode 60: Differential gene expression and DESeq2 with Michael Love


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...


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 2021-05-12  1h31m
 
 

episode 59: Proteomics calibration with Lindsay Pino


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...


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 2021-04-21  48m
 
 

episode 58: B cell maturation and class switching with Hamish King


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...


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 2021-03-31  1h29m
 
 

episode 57: Enhancers with Molly Gasperini


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...


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 2021-03-10  46m
 
 

episode 56: Polygenic risk scores in admixed populations with Bárbara Bitarello


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...


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 2021-02-17  1h30m
 
 

episode 55: Phylogenetics and the likelihood gradient with Xiang Ji


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...


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 2021-01-13  57m
 
 

episode 54: Seeding methods for read alignment with Markus Schmidt


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...


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 2020-12-16  1h0m