the bioinformatics chat – Details, episodes & analysis

In this episode, Marie Sadler talks about her recent Cell Genomics paper, Multi-layered genetic approaches to identify approved drug targets.

Previous studies have found that the drugs that target a gene linked to the disease are more likely to be approved. Yet there are many ways to define what it means for a gene to be linked to the disease. Perhaps the most straightforward approach is to rely on the genome-wide association studies (GWAS) data, but that data can also be integrated with quantitative trait loci (eQTL or pQTL) information to establish less obvious links between genetic variants (which often lie outside of genes) and genes. Finally, there’s exome sequencing, which, unlike GWAS, captures rare genetic variants. So in this paper, Marie and her colleagues set out to benchmark these different methods against one another.

Listen to the episode to find out how these methods work, which ones work better, and how network propagation can improve the prediction accuracy.

Links:

• Multi-layered genetic approaches to identify approved drug targets (Marie C. Sadler, Chiara Auwerx, Patrick Deelen, Zoltán Kutalik)
• Marie on GitHub
• Interview with Mariana Mamonova, the Ukrainian marine infantry combat medic who spent 6 months in russian captivity while pregnant

Thank you to Jake Yeung, Michael Weinstein, and other Patreon members for supporting this episode.

Today on the podcast we have Tomasz Kociumaka and Dominik Kempa, the authors of the preprint Collapsing the Hierarchy of Compressed Data Structures: Suffix Arrays in Optimal Compressed Space.

The suffix array is one of the foundational data structures in bioinformatics, serving as an index that allows fast substring searches in a large text. However, in its raw form, the suffix array occupies the space proportional to (and several times larger than) the original text.

In their paper, Tomasz and Dominik construct a new index, δ-SA, which on the one hand can be used in the same way (answer the same queries) as the suffix array and the inverse suffix array, and on the other hand, occupies the space roughly proportional to the gzip’ed text (or, more precisely, to the measure δ that they define — hence the name).

Moreover, they mathematically prove that this index is optimal, in the sense that any index that supports these queries — or even much weaker queries, such as simply accessing the i-th character of the text — cannot be significantly smaller (as a function of δ) than δ-SA.

Links:

• Collapsing the Hierarchy of Compressed Data Structures: Suffix Arrays in Optimal Compressed Space (Dominik Kempa, Tomasz Kociumaka)

Thank you to Jake Yeung and other Patreon members for supporting this episode.

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/

If you enjoyed this episode, please consider supporting the podcast on Patreon.

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)

If you enjoyed this episode, please consider supporting the podcast on Patreon.

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

If you enjoyed this episode, please consider supporting the podcast on Patreon.

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)

If you enjoyed this episode, please consider supporting the podcast on Patreon.

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)

If you enjoyed this episode, please consider supporting the podcast on Patreon.

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

If you enjoyed this episode, please consider supporting the podcast on Patreon.

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)

If you enjoyed this episode, please consider supporting the podcast on Patreon.

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!)

If you enjoyed this episode, please consider supporting the podcast on Patreon.