Tanglegrams are a visual method to compare two phylogenetic trees with the same set of tip labels. This can be useful for comparing trees produced by different methods on the same alignment, or on different alignments of the sample set. Tanglegrams work by connecting the matching tips of the trees, then rotating subtrees to minimise the number of crossings. The algorithm was published in 2011, and continues to be used in a range of publications (for example genomic epidemiology).

I have added the likelihood ratio test (LRT) for logistic regression into seer, in addition to the existing Wald test as noted in issue 42. As this is likely to remain undocumented elsewhere, here are some brief notes:

• Both the p-value from the Wald test, and the p-value from the new LRT are in the output.
• The LRT is expected to be a more powerful test in some situations. I would recommend its use over the Wald test.
• Testing has shown some clear cases (e.g. when population structure is not a major effect) where the Wald test performs poorly, and the LRT recovers the power of a chi-squared test.
• I have also put in a LRT for linear regression, but based on an estimate of the residual errors (which therefore gives slightly different results to R at small sample sizes). I don’t expect it to make much, if any, difference in this case.

There’s a nice article on the Wald, LRT and score tests here.

I want to count the number of unique patterns in a vcf file. First I convert it to text with bcftools query:

bcftools query -f '[%GT]\n' vcf_in.vcf.gz > patterns.txt

The resulting patterns.txt is about 100Gb. The best way I found to count the unique patterns in this was with the following command:

LC_ALL=C sort -u --parallel=4 -S 990M -T ~/tmp_sort_files patterns.txt | wc -l

This used 1063Mb RAM, took 1521s and used a maximum of around 75Gb tmp space on my home (as the /tmp drive on the cluster ran out of space).

I was working on an OS X system which kept getting annoying pop-ups about the system needing clean up, anti-virus software etc. I was able to see that the window was titled ‘helperamc’.

It turns out this was a remnant from Advanced Mac Cleaner, the use of which I won’t comment on here. The user of the system had tried to remove it when upgrading OS X version, but the annoying advertising component remained.

A recent paper by Earle et. al. nicely showed the use of linear mixed models to determine drug resistance related genetic variants. Part of the software provided is an R package called bugwas, which will make the nice plots in figure 1 for you.

Here are some notes on how to get it to run, and correctly format the input files

Getting gemma to work

You’ll need to use the author’s modified version of gemma, which can be downloaded here. This may not run on your system, due to the blas and lapack libraries being in unexpected places. You can easily solve this by making some symlinks.

Calculates the mean depth of mapped coverage over the whole genome from a mapped bam, using bedtools

bedtools genomecov -ibam  -g bedtools_genome.txt \

To generate bedtools_genome.txt use

samtools faidx reference.fa
cut -f 1-2 reference.fa.fai > bedtools_genome.txt