46 episodes

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

the bioinformatics chat Roman Cheplyaka

    • Life Sciences
    • 5.0, 16 Ratings

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

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

    #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 Koren)
    Canu on GitHub (includes the HiCanu mode)
    The Telomere-to-Telomere (T2T) consortium

    • 1 hr 9 min
    #45 Genome assembly and Canu with Sergey Koren and Sergey Nurk

    #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

    • 1 hr 16 min
    #44 DNA tagging and Porcupine with Kathryn Doroschak

    #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 Queen, Aishwarya Mandyam, Karin Strauss, Luis Ceze, Jeff Nivala)

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

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

    Will Townes proposes a new, simpler way to analyze scRNA-seq data with unique
    molecular identifiers (UMIs). Observing that such data is not zero-inflated,
    Will has designed a PCA-like procedure inspired by generalized linear models
    (GLMs) that, unlike the standard PCA, takes into account statistical
    properties of the data and avoids spurious correlations (such as one or more
    of the top principal components being correlated with the number of non-zero
    gene counts).


    Also check out Will’s paper for a feature selection algorithm based on
    deviance, which we didn’t get a chance to discuss on the podcast.






    Links:



    Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (F. William Townes, Stephanie C. Hicks, Martin J. Aryee, Rafael A. Irizarry)
    GLM-PCA for R
    GLM-PCA for Python
    scry: an R package for feature selection by deviance (alternative to highly variable genes)
    Droplet scRNA-seq is not zero-inflated (Valentine Svensson)

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

    #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

    • 53 min
    #41 Epidemic models with Kris Parag

    #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

    • 1 hr 8 min

Customer Reviews

5.0 out of 5
16 Ratings

16 Ratings

slinkerlee ,

great podcast!

This podcast has great interviews and in-depth coverage of new tools and techniques.

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