Resource: Second Life Interactive Dendrogram Rezzer (SLIDR)

About half a year ago, I began experimenting with Second Life as a tool for virtual conferences (I should add that my experiences have since improved). However, I believe that imitating real life in a virtual world is not necessarily the best way to use the technology – it may be better to use virtual reality for doing the things that are difficult to do in the real world. A good example of this is Hiro’s Molecule Rezzer, which is one of the best known scientific tools in Second Life. It, and its much improved successor Orac, allows people to easily construct molecular models of small molecules in Second Life.

After speaking with several other researchers in Second Life, who like I are interested in evolution, I set out to build a similar tool for visualization of phylogenetic trees. The result is SLIDR (Second Life Interactive Dendrogram Rezzer), which based on a tree in Newick format constructs a dendrogram object. The first version of SLIDR can handle trees both with and without branch lengths; however, I have not yet implemented support for labels on internal nodes or for bootstrap values.

The picture below shows an example of a dendrogram that was automatically generated by SLIDR based on a Newick tree:

There is a bit more to SLIDR than this, though. After the dendrogram has been built, it can be loaded with a photo and/or a sound for each of the leaf nodes. When click on a node, the corresponding sound will be played and the photo will be shown on the associated screen (the white box in front of which I stand):

I plan to work with collaborators in Second Life to construct dendrograms for evolution of bats (including their echolocation sounds and photos of the animals) and for the fully sequenced Drosophila genomes. Please do hesitate to contact me if you would like to use SLIDR on another project. I intend to make SLIDR available as open source software once I have implemented support for the full Newick format.

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Resource: STRING v8.1

After months of hard work from the entire STRING team – thanks everyone -  I am pleased to be able to say that STRING v8.1 has now been put into production. Here is a screen shot of the start page:

This is a minor release of STRING, which means that the imported databases of microarray expression data, protein interactions, genetic interactions, and pathways as well as text-mining evidence have all been updated. We have also fixed a bug that affected the minority of bacteria that have multiple chromosomes.

Another notable feature of STRING v8.1 is the new interactive network viewer that is implemented in Adobe Flash:

For further details please see the post on the official STRING/STITCH blog.

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Resource: The BuzzCloud visualization of buzzwords

“Oh, you work on systems biology? So do I!”

New buzzwords to describe scientific disciplines and technologies seem to pop up every year. For the fun of it, I have developed a small web resource, BuzzClouds, that provides a visual overview of the latest buzzwords in biomedicine.

Without destroying your weekend with mathematical formulas, here is how the BuzzCloud selection and visualization method works:

• A list of potential buzzwords is constructed by extracting all one- and two-word phrases ending on -ics, -ology, -omy, -phy, -chemistry, -medicine, or -sciences. These endings were select to get buzzwords that correspond to scientific disciplines and technologies.
• The potential buzzwords are ranked according to a score that takes into account their frequencies within the past year and within the preceding decade (for details see this review article). To get a high score, a buzzword must be both frequent and new. The top-50 buzzwords are included in the cloud.
• The size of each buzzword is proportional to the logarithm of its frequency during the past year. Common buzzwords are thus large where as rare buzzwords are small.
• The brightness of each buzzword shows the frequency of the buzzword within the past year relative to the preceding decade. New buzzwords are thus bright whereas the older ones are darker.
• Finally, each buzzword is assignd a tint that goes from yellow via white to cyan based on how often it occurs in scientific journals (yellow) as opposed to medical journals (cyan).

When run for the year 2007, the end result looks like this (BuzzClouds for other years are available from the web resource):

I think the method does a pretty decent job despite the occasional mistakes such as nice technology and timely topics. In terms of scientific buzzwords, quantitative proteomics is booming, systems biology still hot although it is getting a bit long in the tooth, and synthetic biology is rapidly gaining popularity. And nanotechnology seems to be popular within the medical domain, giving rise to buzzwords like nanomedicine and nanotherapeutics.

Maybe I should write a buzzword-compliant, interdisciplinary grant application that combines click chemistry and synthetic biology to develop novel nanotherapeutics.

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