the bioinformatics chat Roman Cheplyaka

In this episode, Jacob Schreiber interviews Žiga Avsec about
a recently released model, Enformer. Their discussion begins with life
differences between academia and industry, specifically about how research
is conducted in the two settings. Then, they discuss the Enformer model,
how it builds on previous work, and the potential that models like it have
for genomics research in the future. Finally, they have a high-level discussion
on the state of modern deep learning libraries and which ones they use in their
day-to-day developing.






Links:



Effective gene expression prediction from sequence by integrating long-range interactions (Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli & David R. Kelley )
DeepMind Blog Post (Žiga Avsec)

The Bioinformatics Contest is back this year, and we are back to discuss
it!


This year’s contest winners
Maksym Kovalchuk (1st prize) and
Matt Holt (2nd prize)
talk about how they approach
participating in the contest and what strategies have earned them the top
scores.






Timestamps and links for the individual problems:



00:10:36 Genotype Imputation
00:21:26 Causative Mutation
00:30:27 Superspreaders
00:37:22 Minor Haplotype
00:46:37 Isoform Matching


Links:



Matt’s solutions
Max’s solutions

In this episode, Apostolos Chalkis presents sampling steady
states of metabolic networks as an alternative to the widely used flux balance
analysis (FBA). We also discuss dingo, a
Python package written by Apostolos that employs geometric random walks to
sample steady states. You can see dingo in action
here.






Links:



Dingo on GitHub
Searching for COVID-19 treatments using metabolic networks
Tweag open source fellowships
This episode was originally published on the Compositional podcast.

In this episode, Jacob Schreiber interviews Da-Inn Erika Lee about
data and computational methods for making sense of 3D genome structure. They
begin their discussion by talking about 3D genome structure at a high level
and the challenges in working with such data. Then, they discuss a method
recently developed by Erika, named GRiNCH, that mines this data to
identify spans of the genome that cluster together in 3D space and
potentially help control gene regulation.






Links:


GRiNCH: simultaneous smoothing and detection of topological units of genome organization from sparse chromatin contact count matrices with matrix factorization (Da-Inn Lee and Sushmita Roy)
GRiNCH Project Page
In silico prediction of high-resolution Hi-C interaction matrices(Shilu Zhang, Deborah Chasman, Sara Knaack, and Sushmita Roy)

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/

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)