There’s loads of these on https://twitter.com/accidental__aRt, here are some of mine from the past couple of years:

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. We were also able to match these with bacterial isolates collected in the nationwide reference lab. For both the patients and the bacteria, we then collected population-matched control samples to perform a case-control genome-wide association study (GWAS), plus some other statistical genetics. We accumulated similar data from case-control cohorts in other populations, again in both patients and bacteria, to increase the number of samples, and perform meta-analysis.

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

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.

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.

More details below.

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: