the bioinformatics chat

the bioinformatics chat

By Roman CheplyakaScienceLife Sciences
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the bioinformatics chat episodes

  • #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.

    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 Reproducible Transcriptomic Analysis with DESeq2 and tximeta

      • And a more comprehensive set of links from Mike himself:

      limma, the original paper and limma-voom:

      https://pubmed.ncbi.nlm.nih.gov/16646809/
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053721/

      edgeR papers:

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3378882/

      The recent manuscript mentioned from the Kendziorski lab, which has a Gamma-Poisson hierarchical structure, although it does not in general reduce to the Negative Binomial:

      https://doi.org/10.1101/2020.10.28.359901

      We talk about robust steps for estimating the middle of the dispersion prior distribution, references are Anders and Huber 2010 (DESeq), Eling et al 2018 (one of the BASiCS papers), and Phipson et al 2016:

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218662/
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167088/
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373812/

      The Stan software:

      https://mc-stan.org/

      We talk about using publicly available data as a prior, references I mention are the McCall et al paper using publicly available data to ask if a gene is expressed, and a new manuscript from my lab that compares splicing in a sample to GTEx as a reference panel:

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013751/
      https://doi.org/10.1101/856401

      Regarding estimating the width of the dispersion prior, references are the Robinson and Smyth 2007 paper, McCarthy et al 2012 (edgeR), and Wu et al 2013 (DSS):

      https://pubmed.ncbi.nlm.nih.gov/17881408/
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3378882/
      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3590927/

      Schurch et al 2016, a RNA-seq dataset with many replicates, helpful for benchmarking:

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878611/

      Stephens paper on the false sign rate (ash):

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5379932/

      Heavy-tailed distributions for effect sizes, Zhu et al 2018:

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581436/

      I credit Kevin Blighe and Alexander Toenges, who help to answer lots of DESeq2 questions on the support site:

      https://www.biostars.org/u/41557/
      https://www.biostars.org/u/25721/

      The EOSS award, which has funded vizWithSCE by Kwame Forbes, and nullranges by Wancen Mu and Eric Davis:

      https://chanzuckerberg.com/eoss/proposals/ensuring-reproducible-transcriptomic-analysis-with-deseq2-and-tximeta/
      https://kwameforbes.github.io/vizWithSCE/
      https://nullranges.github.io/nullranges/

      One of the recent papers from my lab, MRLocus for eQTL and GWAS integration:

      https://mikelove.github.io/mrlocus/

      1 hr 32 min
    • #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. 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 of Merit in Quantitative Proteomics
        • (Lindsay K. Pino, Brian C. Searle, Han-Yin Yang, Andrew N. Hoofnagle, William S. Noble, and Michael J. MacCross)
        49 min
      • #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.
        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 shapes selection dynamics
          • (Hamish W. King, Nara Orban, John C. Riches, Andrew J. Clear, Gary Warnes, Sarah A. Teichmann, Louisa K. James) (paywalled by Science Immunology)
        • Antibody repertoire and gene expression dynamics of diverse human B cell states during affinity maturation
          • (the preprint of the above Science Immunology paper)
        • www.tonsilimmune.org: An immune cell atlas of the human tonsil and B cell maturation
          • 1 hr 30 min
        • #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. Tome, and Jay Shendure)
            • 47 min
          • #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.

            Links:

            • Polygenic Scores for Height in Admixed Populations (Bárbara D. Bitarello, Iain Mathieson)
            • What is ancestry? (Iain Mathieson, Aylwyn Scally)
              • 1 hr 31 min
            • #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.

              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, Marc A Suchard)
              • BEAGLE: the package that implements the gradient algorithm
              • BEAST: the program that implements the Hamiltonian Monte Carlo sampler and the molecular clock models
                • 58 min
              • #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.

                Links:

                • A performant bridge between fixed-size and variable-size seeding
                  • (Arne Kutzner, Pok-Son Kim, Markus Schmidt)
                • MA the Modular Aligner
                • Calibrating Seed-Based Heuristics to Map Short Reads With Sesame
                  • (Guillaume J. Filion, Ruggero Cortini, Eduard Zorita) — another
                  interesting recent work on seeding methods (though we didn’t get to discuss
                  it in this episode)
                  1 hr 1 min
                • #53 Real-time quantitative proteomics with Devin Schweppe

                  In this episode, Jacob Schreiber interviews Devin Schweppe about

                  the analysis of mass spectrometry data in the field of proteomics. They begin
                  by delving into the different types of mass spectrometry methods, including MS1,
                  MS2, and, MS3, and the reasons for using each. They then discuss a recent paper
                  from Devin, Full-Featured, Real-Time Database Searching Platform Enables Fast
                  and Accurate Multiplexed Quantitative Proteomics that involved building a
                  real-time system for quantifying proteomic samples from MS3, and the types of
                  analyses that this system allows one to do.

                  Links:

                  • Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics (Devin K. Schweppe, Jimmy K. Eng, Qing Yu, Derek Bailey, Ramin Rad, Jose Navarrete-Perea, Edward L. Huttlin, Brian K. Erickson, Joao A. Paulo, and Steven P. Gygi)
                  • Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics (Qing Yu, Joao A Paulo, Jose Naverrete-Perea, Graeme C McAlister, Jesse D Canterbury, Derek J Bailey, Aaron M Robitaille, Romain Huguet, Vlad Zabrouskov, Steven P Gygi, Devin K Schweppe)
                  • Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage (Ramin Rad, Jiaming Li, Julian Mintseris, Jeremy O’Connell, Steven P. Gygi, and Devin K. Schweppe)
                  • Schweppe Lab Website (Hiring!)
                    • 1 hr 4 min
                  • #52 How 23andMe finds identical-by-descent segments with William Freyman

                    In this episode, Will Freyman talks about identity-by-descent (IBD): how

                    it’s used at 23andMe, and how the templated
                    positional Burrows-Wheeler transform can find IBD segments in the presence of
                    genotyping and phasing errors.

                    Links:

                    • Fast and robust identity-by-descent inference with the templated positional Burrows-Wheeler transform
                      • (William A. Freyman, Kimberly F. McManus, Suyash S. Shringarpure, Ethan M. Jewett, Katarzyna Bryc, the 23andMe Research Team, Adam Auton)
                    • 23andMe research
                      • 43 min
                    • #51 Basset and Basenji with David Kelley

                      In this episode, Jacob Schreiber interviews David Kelley about

                      machine learning models that can yield insight into the consequences of
                      mutations on the genome. They begin their discussion by talking about
                      Calico Labs, and then delve into a series of papers that David has
                      written about using models, named Basset and Basenji, that connect genome
                      sequence to functional activity and so can be used to quantify the effect of
                      any mutation.

                      Links:

                      • Calico Labs
                      • Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks (David R. Kelley, Jasper Snoek, and John Rinn)
                      • Sequential regulatory activity prediction across chromosomes with convolutional neural networks (David R. Kelley, Yakir A. Reshef, Maxwell Bileschi, David Belanger, Cory Y. McLean, and Jaspar Snoek)
                      • Cross-species regulatory sequence activity prediction (David R. Kelley)
                      • Basenji GitHub Repo
                        • 1 hr 14 min

                      About the bioinformatics chat

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