How to: remember that you once knew how to parse KEGG

Recently, someone asked me if I could generate a list of genes associated with a particular pathway. Sure, I said and hacked together some rather nasty code in R which, given a KEGG pathway identifier, used a combination of the KEGG REST API, DBGET and biomaRt to return HGNC symbols.

Coincidentally, someone asked the same question at Biostar. Pierre recommended the TogoWS REST service, which provides an API to multiple biological data sources. An article describing TogoWS was published in 2010.

An excellent suggestion – and one which, I later discovered, I had bookmarked. Twice. As long ago as 2008. This “rediscovery of things I once knew” happens to me with increasing frequency now, which makes me wonder whether (1) we really are drowning in information, (2) my online curation tools/methods require improvement or (3) my mind is not what it was. Perhaps some combination of all three.

Anyway – using Ruby (1.8.7), a list of HGNC symbols given a KEGG pathway, e.g. MAPK signaling, is as simple as:

require 'rubygems'
require 'open-uri'
require 'json/pure'

j = JSON.parse(open("http://togows.dbcls.jp/entry/pathway/hsa04010/genes.json").read)
g = j.first.values.map {|v| /^(.*?);/.match(v)[1] }
# first 5 genes
g[0..4]
# ["MAP3K14", "FGF17", "FGF6", "DUSP9", "MAP3K6"]

This code parses the JSON returned from TogoWS into an array with one element; the element is a hash with key/value pairs of the form:

"9020"=>"MAP3K14; mitogen-activated protein kinase kinase kinase 14 [KO:K04466] [EC:2.7.11.25]"

Values for all keys that I’ve seen to date begin with the HGNC symbol followed by a semicolon, making extraction quite straightforward with a simple regular expression.

Custom CSS for HTML generated using RStudio

People have been telling me for a while that the latest version of RStudio, the IDE for R, is a great way to generate reports. I finally got around to trying it out and for once, the hype is justified. Start with this excellent tutorial from Jeremy Anglim.

Briefly: the process is not so different to Sweave, except that (1) instead of embedding R code in LaTeX, we embed R code in a document written using R Markdown; (2) instead of Sweave, we use the knitr package; (3) the focus is on generating HTML documents for publishing to the Web (see e.g. RPubs), although knitr can also generate PDF documents, just like Sweave.

It took me a little while to figure out a couple of things. First, how best to generate HTML tables, ideally using the xtable package. Second, how to override the default RStudio/R Markdown style. I’ve documented those tasks in this post.

Redmine + Gitolite integration

I’m a big fan of both Redmine, the project management web application and Git, the distributed version control system.

Recently, I learned that it’s possible to integrate Git into Redmine so that git repositories for a project can be created via the Redmine web interface. This is done using plugins which connect Redmine with git hosting software: either gitosis or more recently, gitolite.

Unfortunately, this is a deeply-confusing process for novices like myself. There are multiple forks of the plugins, long threads in the Redmine forums that discuss various hacks/tweaks to make things work and no one authoritative source of documentation. After much experimentation, this is what worked for me. I can’t guarantee success for you.

Proteins in the PDB that differ by one amino acid

A question at BioStar: how to “return all pdb ids to a given one that differ only by one amino acid”?

My answer began: “I think it is not too much work to craft a solution using a few tools”, followed by some incomplete ideas. Let’s see if I was right.

Sequencing for relics from the Sanger era part 1: getting the raw data

Sequencing in the good old days

In another life, way back in the mists of time, I did a Ph.D. Part of my project was to sequence a gene from a bacterium, which encoded an enzyme involved in nitrate metabolism. It took the best part of a year to obtain ~ 2 000 bp of DNA sequence: partly because I was rubbish at sequencing, but also because of the technology at the time. It was an elegant biochemical technique called the dideoxy chain termination method, or “Sanger sequencing” after its inventor. Sequence was visualized by exposing radioactively-labelled DNA to X-ray film, resulting in images like the one at left, from my thesis. Yes, that photograph is glued in place. The sequence was read manually, by placing the developed film on a light box, moving a ruler and writing down the bases.

By the time I started my first postdoc, technology had moved on a little. We still did Sanger sequencing but the radioactive label had been replaced with coloured dyes and a laser scanner, which allowed automated reading of the sequence. During my second postdoc, this same technology was being applied to the shotgun sequencing of complete bacterial genomes. Assembling the sequence reads into contigs was pretty straightforward: there were a few software packages around, but most people used a pipeline of Phred (to call base qualities), Phrap (to assemble the reads) and Consed (for manual editing and gap-filling strategy).

The last time I worked directly on a project with sequencing data was around 2005. Jump forward 5 years to the BioStar bioinformatics Q&A forum and you’ll find many questions related to sequencing. But not sequencing as I knew it. No, this is so-called next-generation sequencing, or NGS. Suddenly, I realised that I am no longer a sequencing expert. In fact:

I am a relic from the Sanger era

I resolved to improve this state of affairs. There is plenty of publicly-available NGS data, some of it relevant to my current work and my organisation is predicting a move away from microarrays and towards NGS in 2012. So I figured: what better way to educate myself than to blog about it as I go along?

This is part 1 of a 4-part series and in this installment, we’ll look at how to get hold of public NGS data.

Interacting with bioinformatics webservers using R

In an ideal world, all bioinformatics tools would be made available via the Web as a web service with an API, as well as a standalone package to download for local use. This is rarely the case and sometimes, even where one or the other is available, factors such as cost come into play. So we resort to web scraping; writing code to interact with the code that lies behind a web server so as to submit queries, retrieve and parse results.

Normally, I’d use something like Ruby’s Mechanize library for this purpose. However, where the purpose is to retrieve delimited data for analysis using R, I figured it was time to try and achieve the entire process within R. So here’s how I used the RCurl and XML packages to interact with the WHAT IF server, which provides tools for the analysis of protein structure.

R: calculations involving months

Ask anyone how much time has elapsed since September last year and they’ll probably start counting on their fingers: “October, November…” and tell you “just over 9 months.”

So, when faced as I was today with a data frame (named dates) like this:

pmid1       year1    month1     pmid2      year2    month2
21355427    2010     Dec        21542215   2011     Mar
21323727    2011     Feb        21521365   2011     Jun
21297532    2011     Feb        21336080   2011     Mar
21291296    2011     Apr        21591868   2011     Jun
...

How to add a 7th column, with the number of months between “year1/month1″ and “year2/month2″?

R 2.12 to 2.13 package upgrade

If you:

• use Linux
• have just upgraded your R installation from 2.12 to 2.13
• installed some/all of your packages in your home area (e.g. ~/R/i486-pc-linux-gnu-library/2.12) and…
• …are wondering why R can’t see them any more

just do this:

# at a shell prompt
cp -r ~/R/i486-pc-linux-gnu-library/2.12 ~/R/i486-pc-linux-gnu-library/2.13
# in R console
update.packages(checkBuilt=TRUE, ask=FALSE)
# back to the shell
rm -rf ~/R/i486-pc-linux-gnu-library/2.12

update: corrected a typo; of course you need “cp -r”

Plotting “time of day” data using ggplot2

William asks:

How can I make a graph that looks like this, “tweet density” style, showing time intervals?

He then helpfully describes his input data: a CSV file with headers “time started, time finished, date”.

On parsing

Parsing – the act of ripping through a file, pulling out the relevant parts and doing something useful with them, is an integral part of bioinformatics. It can be a dull procedure. It can also be challenging, requiring creativity and imagination. Frequently as a bioinformatician, you will generate output from an unfamiliar program, or a colleague will bring you a file that you haven’t encountered. Your task is to figure out how the file is structured, which regular expressions are required to parse it, what kind of output to produce and most importantly, how to handle those rogue files which don’t obey the rules.

Here’s my top ten (language-agnostic) parsing tips, focusing only on non-XML text files.