#43 Generalized PCA for single-cell data with William Townes

More from the bioinformatics chat All episodes →

1. 

   E48 · 29 Jul 2020 · 1 hr 25 min

   #48 Machine learning for drug development with Marinka Zitnik

   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…

2. 

   E47 · 24 Jun 2020 · 58 min

   #47 Reproducible pipelines and NGLess with Luis Pedro Coelho

   NGLess is a programming language specifically targeted at next generation sequencing (NGS) data processing. In this episode we chat with its main developer, Luis Pedro Coelho , about the benefits of domain-specific languages, pros and cons of Haskell in bioinformatics, reproducibility, and of course NGLess itself. Links: NGLess on GitHub NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language (Luis Pedro Coelho, Renato Alves, Paulo Monteiro, Jaime Huerta-Cepas, Ana Teresa Freitas, Peer Bork)

3. 

   E46 · 27 May 2020 · 1 hr 9 min

   #46 HiFi reads and HiCanu with Sergey Nurk and Sergey Koren

   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…

4. 

   E45 · 20 May 2020 · 1 hr 17 min

   #45 Genome assembly and Canu with Sergey Koren and Sergey Nurk

   In this episode, Sergey Nurk and Sergey Koren from the NIH share their thoughts on genome assembly. The two Sergeys tell the stories behind their amazing careers as well as behind some of the best known genome assemblers: Celera assembler, Canu, and SPAdes. Links: Canu on GitHub SPAdes on GitHub

5. 

   E44 · 29 Apr 2020 · 45 min

   #44 DNA tagging and Porcupine with Kathryn Doroschak

   Porcupine is a molecular tagging system—a way to tag physical objects with pieces of DNA called molecular bits , or molbits for short. These DNA tags then can be rapidly sequenced on an Oxford Nanopore MinION device without any need for library preparation. In this episode, Katie Doroschak explains how Porcupine works—how molbits are designed and prepared, and how they are directly recognized by the software without an intermediate basecalling step. Links: Porcupine: Rapid and robust tagging of physical objects using nanopore-orthogonal DNA strands (Kathryn Doroschak, Karen Zhang, Melissa…

6. 

   E42 · 28 Feb 2020 · 53 min

   #42 Spectrum-preserving string sets and simplitigs with Amatur Rahman and Karel Břinda

   In this episode, we hear from Amatur Rahman and Karel Břinda , who independently of one another released preprints on the same concept, called simplitigs or spectrum-preserving string sets. Simplitigs offer a way to efficiently store and query large sets of k-mers—or, equivalently, large de Bruijn graphs. Links: Simplitigs as an efficient and scalable representation of de Bruijn graphs (Karel Břinda, Michael Baym, Gregory Kucherov) Representation of k-mer sets using spectrum-preserving string sets (Amatur Rahman, Paul Medvedev) Open mic

7. 

   E41 · 27 Jan 2020 · 1 hr 8 min

   #41 Epidemic models with Kris Parag

   Kris Parag is here to teach us about the mathematical modeling of infectious disease epidemics. We discuss the SIR model, the renewal models, and how insights from information theory can help us predict where an epidemic is going. Links: Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation (KV Parag, CA Donnelly) Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models (KV Parag, CA Donnelly) The listener survey

8. 

   E40 · 30 Dec 2019 · 45 min

   #40 Plasmid classification and binning with Sergio Arredondo-Alonso and Anita Schürch

   Does a given bacterial gene live on a plasmid or the chromosome? What other genes live on the same plasmid? In this episode, we hear from Sergio Arredondo-Alonso and Anita Schürch , whose projects mlplasmids and gplas answer these types of questions. Links: mlplasmids: a user-friendly tool to predict plasmid- and chromosome-derived sequences for single species (Sergio Arredondo-Alonso, Malbert R. C. Rogers, Johanna C. Braat, Tess D. Verschuuren, Janetta Top, Jukka Corander, Rob J. L. Willems, Anita C. Schürch) gplas: a comprehensive tool for plasmid analysis using short-read graphs (Sergio…

9. 

   E39 · 29 Nov 2019 · 1 hr 2 min

   #39 Amplicon sequence variants and bias with Benjamin Callahan

   In this episode, Benjamin Callahan talks about some of the issues faced by microbiologists when conducting amplicon sequencing and metagenomic studies. The two main themes are: Why one should probably avoid using OTUs (operational taxonomic units) and use exact sequence variants (also called amplicon sequence variants, or ASVs), and how DADA2 manages to deduce the exact sequences present in the sample. Why abundances inferred from community sequencing data are biased, and how we can model and correct this bias. Links: Exact sequence variants should replace operational taxonomic units in…

10. 

    E38 · 22 Oct 2019 · 1 hr 1 min

    #38 Issues in legacy genomes with Luke Anderson-Trocmé

    In this episode, Luke Anderson-Trocmé talks about his findings from the 1000 Genomes Project. Namely, the early sequenced genomes sometimes contain specific mutational signatures that haven’t been replicated from other sources and can be found via their association with lower base quality scores. Listen to Luke telling the story of how he stumbled upon and investigated these fake variants and what their impact is. Links: Legacy Data Confounds Genomics Studies ( bioRxiv , Molecular Biology and Evolution (paywall) ) (Luke Anderson-Trocmé, Rick Farouni, Mathieu Bourgey, Yoichiro Kamatani,…