The way that I calculate pair-wise theta is by using the R library Geneclust (Ancelet and Guillot).  Geneclust does much more than calculate F_ST, but I found this library to be particularly useful at achieving these ends.  I have run across a few other packages that calculate F_ST in R, but not Weir and Cockerham’s unbiased formulation (Please correct me in the comments if you know of other libraries).  To install Geneclust in R, simply type “install.packages(c(“Geneclust”,”deldir”,”fields”,”spatial”)”.  Below is the code I use to calculate pair-wise Fst values.  As with the allele frequency example, I have again used the Beech data as a nice example (here modified as a tab-delimited text file: DataMEC-11-0816.R1).   I have checked these values against FSTAT and they give identical results, but more double-checking couldn’t hurt.   I hope you find this useful – and please feel free to proffer suggestions, recommendations, opinions etc.

Script posted here:  Fst_script.txt

This was followed by the “megaton announcement” of the gridION and minION nanopore sequencers at AGBT in February. Now, we’ll have to see if anyone gets a sequencer funded from all those NSF proposals written prior to Oxford Nanopore’s announcement.

After some grieving, I put together the update for the 2012 Field Guide. I have done my best to make the comparisons apples to apples, but that’s pretty tough to do with the nanopore sequencers (there is no set run time or number of reads, length distributions are unknown, etc.). So, you will see that I’ve had to be kind of creative in some places, and the numbers may well be overly optimistic.

Nick Loman has some nice summaries of the Proton and Nanopore sequencers. Keith Robinson’s OMICS! OMICS! posts on the nanopore sequencers, Ion Torrent, and MiSeq (as well as others) are also insightful.

Hopefully these updated Field Guide tables will be of some utility to folks:

2012 NGS Field Guide

Here is the output from the script at one locus:

and here is a plot of allele frequencies at a different locus:  Allele frequency plot

Please keep in mind that this is only one example of how to calculate allele frequencies.  Feel free to add your own suggestions or improvements in the comments.  If anybody has a script that calculates unbiased allele frequencies (with respect to sample size), I would be happy to post that as well.

One attribute of R that unfortunately makes people choose other platforms is that it is largely a command line interface.  This means that there is very little pointing and clicking involved.  It also means that there is some syntax that has to be learned, which can intimidate those new to coding.  I will admit that there certainly is a learning period involved, but the advantages are well worth the effort.  With the command-line interface you can perform nearly any analysis that you can think up.  As a molecular ecologist this is an invaluable skill as genetic data often must be analyzed in novel or non-traditional ways.  My best advice is simply to dive in!  Immersing yourself in the language (i.e., forcing yourself to do all analyses in R) for a short period of time is the quickest way to learn.  Why not be productive at the same time?  Try to run an ANOVA or t-test on some data you have been waiting to analyze.

In the next few posts I will provide some simple walkthroughs to (1) import data and calculate allele frequencies from genotype data, (2) calculate Weir and Cockerham’s unbiased Fst, (3) look at some multivariate packages for population genetics and genomics and (4) explore ways to create publication-quality figures.  Below are some vital links for getting started with R.  Keep in mind that there is a very large community of R users and much help can be found online with a search engine.   Please feel free to recommend other resources and/or tips in the comments:

The main R webpage:  http://www.r-project.org/

What R is all about:  http://www.r-project.org/about.html

The R card: very useful for learning syntax.

R introductory manuals.

R commander: a point and click graphical user interface for R, which simultaneously displays the code in R – great for users intimidated by the command line.

TINN-R : A essential text editor for Windows users**.

RSeek: A search field that only returns results relevant to R.

QuickR:  Useful walkthroughs for some basic functions/calculations.

The R Journal: useful tidbits for more advanced users.

**(Using a text editor that interfaces with R is a great idea.  That way you can save, easily manipulate, and send code to R.  If you don’t save your code, you will forget it.  It is also a great way to document all of your analytical steps in a study such that you can go back and easily repeat what you did years from now).

“I found this paper fascinating in that it is the first I’ve read that tries to examine the genomic architecture of this important phenomenon. As the authors note, similar analyses have been conducted in the past, but they are often directed at specifically trying to find loci that cause incompatibility between populations in the context of speciation, rather than examining this complex phenomenon.”

The whole post is here. There’s also an excellent perspective piece by Zach Gompert that accompanies the Miller et al. paper, and both will very likely appear in issue 21.6 (mid March).

http://scholarlykitchen.sspnet.org/2012/02/01/the-famous-grouse-do-prominent-scientists-have-biased-perceptions-of-peer-review/…

Among several great nuggets within this paper is what the authors describe as a “[replacement for] a commonly used commercial kit (AMPure XP kit) for SPRI-clean-up steps with a home-made mix”, and in the supplementary information (available to everyone), they provide the components of this home-made mix.

Below, I’ve added a gel image illustrating differences between this home-brew mix and commercial AMPure XP – I refer to the home-brew mix by the name of the beads in solution (SeraMag Speed Beads). The numbers above each lane show the ratio of AMPure or SeraMag Speed Beads used to product being cleaned – in this case, the product being “cleaned” is the same ladder on the far left of each image.

http://regex.learncodethehardway.org/

Decent knowledge of regular expressions combined with a very good text editor and perhaps a data manipulation tool can fundamentally change the way you work, increase repeatability, and improve your science.

A nutator (AKA nutating mixer or rotating mixer) is a gently rocking/rotating platform useful for continuously and gently mixing samples. They are particularly useful if you’re doing bead-binding sorts of operations – like binding biotinylated oligos to streptavidin-coated beads. This is a pretty common workflow for lots of tasks including one that is increasingly useful… target enrichment (AKA sequence capture or solution hybridization).

Anyway, if you have a nutator, then that’s super. I don’t, and they’re far more expensive than they should be. So, I offer you a very inexpensive solution: a rotisserie kit. The kit nets you the all-important spit pole and is a one-stop solution, but you can also buy just the motor and get the rest of what you need at a decent hardware store.

Once you have the kit in hand, you can get fancy if you like (or, if you can weld). If you don’t want to get fancy, string this bad-boy up between two of the thousands of styrofoam coolers you have in the lab. Make some styrofoam squares with holes in the middle (the spit pole goes though these), and affix your sample to be nutated to one side of the square (ensure you’re using decent tubes that close well). Repeat for as many samples as you need to nutate. Make more squares if needed. This also works for strip-tubes and well-sealed plates.