Last week I attended a particularly interesting meeting that we had co-organised with colleagues at AHRC. This was a workshop on the wide-ranging, important and fundamental topic of data visualisation. Biological data visualisation can be defined as “a branch of bioinformatics concerned with the application of computer graphics, scientific visualization, and information visualization to different areas of the life sciences”. We recognised, as did the make-up of delegates at the workshop, that this included skills in and understanding of perception, cognition and design.
Plenaries came from Katy Börner and Reinhard Schneider, with other talks by Nick Holliman, Sara Fabrikant, Achilleas Frangakis and Jessie Kennedy. A splendid poster session and some stunning computer-based demonstrations showed the range of activities and software that is being developed and deployed for visualisation purposes. There was a huge amount of interesting material, too much to review properly, and so I shall be egregiously selective. Katy Börner talked about a variety of Science of Science tools she and her colleagues have developed for looking at science funding, networks and the like; the portal is here, and some of the interesting findings of the formal aspects of team science have recently been summarised. I blogged before about the wonderful scimaps exhibition, and a new book contains many of the relevant graphics.
A very small and slightly random subset of discussions and links include ones about Euler diagrams with Gem Stapleton, x-ray tomography of plant roots with Philipp Thurner, affective (or I would say closed loop) data visualisation with Brigitta Zics, the VivoWeb, visualisations of motor car company ownership, the very nice MATSE microarray analysis environment, Biolayout Express, volume rendering software, Utopia documents (in which I have some involvement), concepts such as ‘data wrangling’,‘visualisation cul-de-sacs’ and ‘good visualisations take you on a journey’, some well-established good colour schemes for maps, an interesting workshop next year, and so on. The existence of so much stuff out there, including one of my favourites, made it obvious that there is a real need for a portal. At all events, I am pretty sure that the success of this meeting will provoke a follow-up.
I also attended our ‘next generation’ conference, not of DNA sequencing methods but of our PhD students and postdocs. I was able to hear an unusual presentation by Peter Cook before providing some of my own thoughts in a dinner speech. I managed to talk to a good number of the delegates, and thereby came to hear about a lot of very interesting projects that we are funding.
Regarding networks, this week saw the publication of an update of the community consensus paper of the yeast metabolic network. It should be a useful resource for those wishing to do industrial biotechnology using this organism.
Other papers that caught my eye included one on the benefits to be had from using knowledge of biochemical pathways to improve the signal in genome-wide association studies and another pointing up the epigenetic differences between queen and worker bees. Certainly the analysis of the epigenomes of next generation scientists will require some very good visualisation tools!
- Börner, K. (2010). Atlas of Science. MIT Press, Boston
- Börner, K., Contractor, N., Falk-Krzesinski, H. J., Fiore, S. M., Hall, K. L., Keyton, J., Spring, B., Stokols, D., Trochim, W. & Uzzi, B. (2010). A multi-level systems perspective for the science of team science. Sci Transl Med 2, 49cm24
- Dobson, P. D. et mult. al. (2010). Further developments towards a genome-scale metabolic model of yeast. BMC Systems Biology 4, 145. Full free text
- Herrgård, M. J. et mult. al. (2008). A consensus yeast metabolic network obtained from a community approach to systems biology. Nature Biotechnol. 26, 1155-1160
- Lyko, F., Foret, S., Kucharski, R., Wolf, S., Falckenhayn, C. & Maleszka, R. (2010). The honey bee epigenomes: differential methylation of brain DNA in queens and workers. PLoS Biol 8, e1000506. Full free text
- Wang, K., Li, M. & Hakonarson, H. (2010). Analysing biological pathways in genome-wide association studies. Nat Rev Genet 11, 843-854