Repeatability, one of the central tenets of science. In theory, any published study should be repeatable. But, if anyone actually wants to do this, it's not always as straightforward as it sounds.
Molecular people do have it easy in comparison to say ecologists—we use GenBank, the Web based repository of all things DNA. Simple, just want to re-analyse some data, grab it from GenBank. Not so easy*, and here's an example.
Take the recent cypriniform relationships proposed by Mayden and Chen (2010). Being as polite as possible, their results are "interesting" to say the least. Lets assume we want to get our grubby hands on this data and see whether their conclusions have any meaningful support. They used six genes, and a table listing the accession numbers is presented:
These data are not all generated in this study though, so it makes it tricky to access them from the GenBank Web site. Copying and pasting each of them into GenBank is just not an option, so here's a hack that might just save you some time:
(1) Copy and paste the whole table from the pdf into a text file. Save the text file (e.g. as "input.txt").
(2) Open a terminal session, cd to the directory, and copy this command:
grep -o '[A-Z][A-Z][0-9][0-9][0-9][0-9][0-9][0-9]' input.txt | sed -e ':a;N;$!ba;s/\n/", "/g' -e 's/^/acc <- c("/g' -e 's/$/")/g' > output.txt
Eugh, that looks horrible, but what it does is create a ready made vector (called "acc") of accession numbers, which can be copied straight into R. Now, we assume a few things first though: that the table copied perfectly, and the fonts are compatible between pdf and txt; that the accessions are common eight digit GenBank accessions—some of the older GenBank accessions may be six digits (the regex can be modified though).
(3) Now, copy the output straight into R. We could use ape's read.GenBank function, but we would like to access the gene names too**, so we will use Samuel Brown's read.GB function instead.
Run the following R code to download the data and add taxon labels:
dat <- read.GB(acc)
names(dat) <- paste(acc, "|", attr(dat, "gene"), sep="")
(4) Now, this dumps us with all of the data in one vector, but we really want to analyse the seperate loci (e.g. rhodopsin). No worries, grep comes to the rescue again by grabbing all the sequences with "rhodopsin" in the gene attribute:
rho <- grep("rhodopsin", names(dat))
rhoSeqs <- dat[rho,]
The names ascribed to genes in GenBank from different studies may not be consistent, so make sure you check that your identifying phrase will work as expected. Now all you need to do is write this into a fasta file, align it, and you're away.
write.dna(rhoSeqs, file="mayden2010_rho.fas", format="fasta", colw=10000)
* It really should be mandatory that researchers use services such as TreeBASE to upload their alignments.
** read.GenBank in ape 2.7 now has an optional "gene" attribute, but I couldn't get it to work ...