In pursuit of my DIY genomics posts, I've been playing around with the Galaxy bioinformatics web tools. The team responsible for the South African genomes published the data to Galaxy, and their uploads are easy to get -- either to download, or to work with the online Galaxy platform.
Working with a resource like this helps to illustrate both how tremendously useful bioinformatics tools can be, but also how frustrating it can be to figure them out. Some things are a breeze, although others are completely obscure. Documentation for the uploads is skimpy so far -- one thing that drove me up the wall is that SNPs are listed by genome, but without indicating genotypes -- is the individual a homozygote or a heterozygote? The paper by Schuster and colleagues describes their genotype calling procedure, but the results turn out not to be posted along with their other data. I'm sure they'll become available as the data are updated, but I did waste some time figuring out how the releases correspond to descriptions in the online supplementary material from the paper.
Despite occasional frustrations, we seem to be heading in the direction of all-in-one online bioinformatics toolkits. Galaxy, for example, lists several advantages on a promo page. A couple of entries:
Now your results are reproducible! | When publishing results, replace “the data were analyzed using a collection of in-house scripts” with a URL pointing to Galaxy’s history. Your reviewers will have no further questions. That’s reproducible genomics!
...
No tools for new datatypes | Some datatypes generated by high throughput genomics are so new that there are no tools to analyze them. For example, how do you extract sequences of coding exons from the latest 28-way alignments of vertebrate genomes or analyze quality scores from 454/Solexa/SOLiD? With Galaxy.
I live at the mathematical end of this stuff. I work with models of populations and assume that sequences are known, you know, as if we looked at them and read off the ACGT's. But in reality, a lot of complexity lies between models and the biochemistry. Going from sequencing reads to genomes, and aligned genomes, involves a lot of analysis. Many of the details differ entirely between different sequencing platforms. As we continue to move toward whole-genome analyses of populations and other species, it's really important to have an abstraction that allows for different underlying sequencing models, while allowing replication of the population genetics modeling.
The disadvantage of a single widely used tool is that it can limit creativity and lock people into a certain way of processing data. Locked-in assumptions sometimes lead to wrong conclusions -- as we've seen in human genetics many times over the years. But the advantage is that it allows everybody access to the same methods and data, so that results can be replicated and augmented with new observations.
