Monthly Archives: July 2012

generate log-uniform distribution with R

Hi All,

does anyone know how to generate a log-uniform distribution with R?



Job advert for a Postdoctoral Researcher (Data Analyst)

An exciting opportunity exists for a researcher with experience in population genetic analyses, with relevant bioinformatic skills to join the NERC Biomolecular Analysis Facility (NBAF) node in Sheffield. The appointee will assist Facility users with study design, data analysis and training, and contribute to the node’s research and development activities.

For additional information about the job please click on “Apply online” on the following webpage

Closing Date: 9th August 2012

Informal enquiries can be made to Professor Terry Burke ( or Dr Deborah Dawson (

Windows!…double glazing this week

…from Tuesday 31st July we will have workmen in fitting double glazing to our windows. If you have a window bench you will need to move your stuff across at some point. I have been assured that apart from this there will be minimum disruption- let me know if any problems. Cheers, Andy.

Postdoc in Hull

Title: The evolutionary genomics of sexual recombination
The 3 year PDRA will analyse ~20 newly sequenced nematode genomes, which
cover a diversity of reproductive modes (amphimixis, automixis, apomixis) in
a phylogenetic design. The PDRA will investigate TEs, gene families, and
patterns of molecular sequence evolution to better understand the influence
of meiosis and inbreeding on genome diversity and content.

Details of the project, and how to apply, can be found here
Closing date is 12 August 2012

YUEG meeting York Mon 10th Sept

The 2012 Yorkshire Universities Evolutionary Group meeting will be in York on Monday 10th September – with poster sessions and talks.

It’s always a fun day out, finishing in the pub and normally several people from the lab go.

Terry’s group meeting this Friday (27/7) 12-1 B52

Petanque? Monday 1pm

Monday- it is sunny and warm for a change…Petanque? 1pm park 🙂 all welcome

Friday night fever !!!

For those of you who need an excuse for a drink, I have very nice one. My graduation!!! At Interval starting from 5:30 today (20/07) (I didn’t mention the ending time…). I will let the mood lead us but it is very likely that it will end up with disco… see you and be prepared for photo time with me


Why corrections for multiple tests should NOT be applied to HWE p-values

Explanation from Jon (posted by Debs) =

The problem is this – Bonferroni (or sequential Bonferroni) corrections are very conservative, which in normal hypothesis testing is what you want to be.

The more tests you do, the more likely it is that one will be significant by chance (we call this a Type 1 error, or a false positive). Therefore, by dividing the normal significance threshold (P < 0.05) by the number of tests (the number of loci examined), you reduce the chances of getting a Type 1 error. However, when testing microsats we don’t want to miss loci with null alleles (which cause departures from HWE) and end up typing them in lots of individuals, when in fact they are rubbish loci. By setting a significance threshold too stringently we end up including too many ‘bad’ loci in downstream applications. Therefore, it is best to use a threshold of P < 0.05 for each locus we test. This is particularly problematic when only typing a small number of individuals (e.g. 24) because a locus with a null at really high frequency still might not be Bonferroni significant. .. Bottom line is that one shouldn’t use Bonferroni corrections for HWE tests (, and 24 individuals are not really enough to estimate allele frequencies with much confidence.)

Pruning relatives from a large dataset

Hi all,

Sanad and I have a problem and we wonder whether anyone has encountered something similar (and knows a solution). In his spiny mouse dataset we have used microsats to measure relatedness between all typed individuals. For most downstream population genetic analyses (e.g. testing for departures from HWE, performing analyses in STRUCTURE etc)  assumptions of individuals being unrelated are made.  Violating these assumptions can cause real problems – see for example the recent paper in MER from Jianlang Wang’s group on what this does to STRUCTURE analyses. In the spiny mice we have quite a lot of pairs (>500) with an r > 0.25). Therefore, we wish to prune individuals from the dataset such that nobody has an r >= 0.25 to anything else. This sounds straightforward, but in practice is quite tricky because there are so many pairwise combinations and if you remove one individual from a dyad at random you may end up throwing away too much data.

Therefore, our question is this.
Does anyone know of an efficient way (i.e. a program) for removing the fewest possible individuals while ensuring no dyads have a r above a  given threshold (we chose 0.25 fairly arbitralily).

Many thanks
Jon & Sanad