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.

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:

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.

I was getting the following error, attempting to run conda-build on a package, using a conda env: Traceback (most recent call last): File "/nfs/users/nfs_j/jl11/pathogen_nfs/large_installations/miniconda3/envs/conda_py36/bin/conda-build", line 7, in <module> from conda_build.cli.main_build import main File "/nfs/users/nfs_j/jl11/pathogen_nfs/large_installations/miniconda3/envs/conda_py36/lib/python3.6/site-packages/conda_build/cli/main_build.py", line 18, in <module> import conda_build.api as api File "/nfs/users/nfs_j/jl11/pathogen_nfs/large_installations/miniconda3/envs/conda_py36/lib/python3.6/site-packages/conda_build/api.py", line 22, in <module> from conda_build.config import Config, get_or_merge_config, DEFAULT_PREFIX_LENGTH as _prefix_length File "/nfs/users/nfs_j/jl11/pathogen_nfs/large_installations/miniconda3/envs/conda_py36/lib/python3.6/site-packages/conda_build/config.py", line 17, in <module> from .variants import get_default_variant File "/nfs/users/nfs_j/jl11/pathogen_nfs/large_installations/miniconda3/envs/conda_py36/lib/python3.6/site-packages/conda_build/variants.py", line 15, in <module> from conda_build.

In our software PopPUNK we draw a plot of a Gaussian mixture model that uses both the implementation and the excellent example in the scikit-learn documentation: Gaussian mixture model with mixture components plotted as ellipses My input is 2D distance, which I first use StandardScaler to normalise each axes between 0 and 1, which helps standardise methods across various parts of the code. This is fine if you then create these plots in the scaled space, and as it is a simple linear scaling it is generally trivial to convert back into the original co-ordinates: