the bioinformatics chat

the bioinformatics chat

By Roman CheplyakaScienceLife Sciences
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the bioinformatics chat episodes

  • #9 Michael Tessler and Christopher Mason on 16S amplicon vs shotgun sequencing

    Michael Tessler

    and
    Christopher Mason
    join me to talk about their comparison of
    16S amplicon sequencing and
    shotgun sequencing for quantifying microbial diversity.

    Links:

    • The 2017 Nature paper that we discuss: Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing

    • Michael’s et al. 2016 paper that describes their original 16S study: A Global eDNA Comparison of Freshwater Bacterioplankton Assemblages Focusing on Large-River Floodplain Lakes of Brazil

    • The sequencing data for these studies is available from NCBI:

      PRJNA310230
      (16S),
      PRJNA389803
      (shotgun)

    • Michael’s website

    • Christopher’s lab website

    • The Integrative Biology & Medicine
      • conference, featuring the talk on Postmodern Synthesis by Eugene Koonin
      46 min
    • #8 Perfect k-mer hashing in Sailfish

      The original version of Sailfish,

      an RNA-Seq quantification tool, used minimal perfect hash functions to
      replace k-mers with unique integers.
      (The current version appears to be using a Cuckoo hashmap instead.)

      This is my attempt to explain how a minimal perfect hash function could be

      built. The algorithm described here is not exactly the same as the one
      Sailfish used, but it follows the same idea.

      Sections:

      • Sailfish and perfect hashing (1:15)
      • Perfect hashing based on binary search or hash tables (4:34)
      • Random hash functions (7:34)
      • Perfect hash function based on an acyclic graph (12:16)

        • Links:

        • The Sailfish paper
        • The paper describing the perfect hashing algorithm
        • The birthday paradox
        • Integrative Biology & Medicine
          • 23 min
        • #7 Metagenomics and Kraken

          What is metagenomics and how is it different from phylotyping?

          What is Kraken and how can it be faster than BLAST?

          Let’s try to sort this out.

          Sections:

          • Culturing and its limitation (00:18)
          • Metagenomics vs phylotyping (1:43)
          • BLAST (5:53)
          • The idea behind Kraken (8:14)
          • How Kraken organizes its database (18:08)

            • Links:

            • The paper about Kraken
            • A freely accessible (though a bit dated) book on metagenomics

              • Correction: in this episode, I incorrectly state that Kraken operates on

              phylogenetic trees, whereas in fact it operates on taxonomic trees.

              In practice this means that when Kraken cannot decide among several species,

              it assigns the read to their lowest common taxon (genus, family etc.),
              not their latest common evolutionary ancestor.

              29 min
            • #6 Allele-specific expression

              I talk about allele-specific expression: why it arises and how it can be

              reliably detected.

              Sections:

              • The biology of allele-specific expression (2:17)
              • Detecting allele-specific expression with RNA-seq (7:46)
              • Mapping and sequencing biases (16:39)
              • The experiment in yeast (19:47)
              • Statistical models (21:44)

                • Links:

                • A powerful and flexible statistical framework for testing hypotheses of
                  • allele-specific gene expression from RNA-seq data
                  • Supplemenal information
                  • Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data
                  • The talk by John Marioni
                  • The blog post explaining the RSEM model
                    • 34 min
                  • #5 Relative data analysis and propr with Thom Quinn

                    In this episode, Thom Quinn and I explore different ways

                    to transform and analyze relative data arising in genomics.

                    We also discuss

                    propr, Thom’s R
                    package to compute various proportionality measures.

                    Links:

                    • Thom’s preprint about propr —
                      • take a look if you feel lost in all the quantities that we discuss :)
                    • David Lovell original paper introducing proportionality for relative data
                      • and a very detailed appendix
                    • Ionas Erb’s paper introducing the ρ metric
                    • Vignettes for propr
                      • 56 min
                    • #4 ChIP-seq and GenoGAM with Georg Stricker and Julien Gagneur

                      In this episode, I meet with Georg Stricker and Julien Gagneur from the Technical University of Munich

                      to discuss ChIP-seq data analysis and their tool, GenoGAM.

                      Links:

                      • Preprint about GenoGAM
                      • Georg on GitHub
                      • Julien’s lab on Twitter
                      • [BC]2 — The Basel Computational Biology Conference (Basel, September 2017), where you can meet Georg
                      • The European Human Genetics Conference (Copenhagen, May 2017), where you can meet Daniel Bader from Julien’s lab

                        • Register for the Summer School in Bioinformatics & NGS Data Analysis (#NGSchool2017) (Poland, September 2017)

                        56 min
                      • #2 Single-cell RNA sequencing with Aleksandra Kolodziejczyk

                        In this episode

                        Aleksandra Kolodziejczyk
                        talks about single-cell RNA sequencing.

                        Links:

                        • A review paper by Aleksandra
                        • Comparative analysis of single-cell RNA sequencing methods, including the cost table
                        • Power Analysis of Single Cell RNA‐Sequencing Experiments
                        • Monocle: a toolkit for analyzing single-cell gene expression experiments
                        • Questions from listeners on
                          • bioinformatics.chat
                          and
                          reddit
                          1 hr 9 min

                        About the bioinformatics chat

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