With my team I am involved in developing software for alignment and phylogeny inference (eg, check recent CodonPhyML and ProGraphMSA+TR). However, typically we are most concerned with algorithms rather than post hoc visualization. I wonder, what is the typical expectation of researchers using such software, in terms of visualization. Do you use certain software because of nice visualization properties, if so which software? Which features you would have liked to have in the software you currently use? This question is a poll, which will contribute to the discussion during the VIZBI 2014 conference: http://www.vizbi.org/2014. I intend to summarize all the answers and pass this information on to other developers and visualization experts during VIZBI.
As a user of phylogeny inference programs (and an old student of arcane things), I think there is a need for user-friendly interfaces. Instead of line commands, it is now possible to have a series of items to click on, or spaces to write in whatever options. Color-coded alignments, for example, are of great help --even if that doesn't add anything to the algorithms working before or afterwards. The idea would be to reduce as much as possible the time spent learning the details and commands of any program, and to maximize the visibility of differences in outcome.
@Cristian: Thank you for your answer! Do you have any favourite alignment/phylogeny programs that you use for your research? What are they and do you have any specific wishes for certain visialization features to be implemented?
Hi Maria,
Your work is really nice and useful. The phylogeny.fr platform is an excellent resource for me. PhyML is by far the best... well, I will probably have to change that after using CodonPhyML! Anyway, I would like to have readily available features such as 3D visualization of data (such as principal components, or minimum spanning tree) where I can see how the inferred tree fits in. The alignments could suggest visually what codons are saying (maybe nothing better than the string of nucleotides). Also, it seems quite relevant to get model selection into the workstream --all too often, taxonomists ignore this critical step. Still thinking...
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