Monthly Archives: June 2010

Create your own Google Scholar RSS feed

Google Scholar is a useful tool and now has a dedicated blog. The first post is dedicated to email alerts.

It’s unimaginable, in 2010, that an alert service would not provide an RSS feed, so I can only assume that this feature will appear “in due course”. In the meantime, a quick Google search for create rss feed from website lead me to 7 Tools To Make An RSS Feed Of Any Website. I quickly tested them all and I agree with the author of the article: Feed43 is the winner.

The process for creating a Google Scholar feed is a little complex. Here’s my first attempt.

Update: interesting FriendFeed thread, where people point out that (a) scraping Google Scholar is quite likely to fail and (b) this is not the same as an alert, since results are not ordered by date.
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First cautious steps with CUDA

I recently obtained an account on our GPU cluster, so I thought I should get my head around some of the technology that drives GPU computing.
Put simply, GPUs can be used to perform calculations and since there are many processors on a GPU, this can lead to quite substantial speed increases as compared with CPUs. NVIDIA are leading the way and they provide libraries and software tools for people interested in this field.
Development is typically performed using C, C++ or Fortran. I’m not a compiled languages guy – I could just about manage a hello world in C – so I’m relying on tools built by other people, such as R gputools.
Step 1 is to download and install the required libraries, toolkit and possibly, drivers. I ran into a couple of minor problems on my machine, so I thought I’d document them here.
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biomaRt and GenomeGraphs: a worked example

As promised a few posts ago, another demonstration of the excellent biomaRt package, this time in conjunction with GenomeGraphs.

Here’s what we’re going to do:

  1. Grab some public microarray data
  2. Normalise and get a list of the most differentially-expressed probesets
  3. Use biomaRt to fetch the genes associated with those probesets
  4. Plot the data using GenomeGraphs

If you want to follow along on your own machine, it will need to be quite powerful. We’ll be processing exon arrays, which requires a 64-bit machine with at least 4 GB (and preferably, at least 8-12 GB) of RAM. As usual, I’m assuming some variant of Linux and that you’re comfortable at the command line.
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