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John Lees' blog

Pathogens, informatics and modelling at EMBL-EBI

Paper summary – Joint sequencing of human and pathogen genomes reveals the genetics of pneumococcal meningitis

This a summary of our paper on a joint pathogen and human GWAS that has just been published in Nature Communications: https://doi.org/10.1038/s41467-019-09976-3 This is the last bit of research from my PhD thesis. Also, this was the first thing I started working on back in 2014 (my first GWAS), and our collaborators have been collecting data since 2006 – so it’s good to see this one out! Overview We collected cases from pneumococcal meningitis patients enrolled in a nationwide Dutch cohort.

Readthedocs failing to build: module 'setuptools.build_meta' has no attribute '__legacy__'

As we all know, it’s critical that your code’s github README.md However, to my horror, I noticed one of my many nice green badges signalling my professionalism had turned an alarming shade of red. What were potential users to think, seeing that the docs for my most recent commit had failed to build on readthedocs The horror Looking at the build logs, one of my dependencies (in this case hdbscan) was failing with the error message:

Conservation of core genes in S. pneumoniae

A question I am sometimes asked is whether a gene of interest, usually being studied in vitro or in vivo, is conserved. Although the availability of population genomic datasets allows this question to be answered, it can be hard to find this kind of analysis in the literature, and doing it yourself is not trivial. This post hopes to be an easy way to access this information for S. pneumoniae.

Using unitigs for bacterial GWAS with pyseer

This post briefly explains how you can now use unitigs, nodes of sequence in a compressed de Bruijn graph enumerated using DBGWAS, in the pyseer software. Broadly, this has the following advantages over k-mer based association: Computational burden: fewer resources used in counting the unitigs, and fewer unitigs that need to have their association tested. Lower multiple testing burden, as unitigs reduce redundancy present in k-mer counting. Easier to interpret: unitigs are usually longer than k-mers, and further context (surrounding sequence) can be analysed by using the graph structure they come from.

Paper summary – PopPUNK for bacterial epidemiology

A paper describing our recent method for bacterial epidemiology PopPUNK has just been published in Genome Research, which you can read here: https://dx.doi.org/10.1101/gr.241455.118 You can install our software by running conda install poppunk and that full details and documentation can be found at https://poppunk.readthedocs.io In this blog post I will attempt to describe some of our key features and findings in a shorter format. Broadly, I think there are three main parts:

Creating a conda package with compilation and dependencies

I’ve just finished, what was for me, a difficult compiler/packaging attempt – creating a working bioconda package for seer. You can look at the pull request to see how many times I failed: https://github.com/bioconda/bioconda-recipes/pull/11263 (I would note I have made this package for legacy users. I would direct anyone interested in the software itself to the reimplementation pyseer) The reason this was difficult was due to my own inclusion of a number of packages, all of which also need compilation, further adding to the complexity.