DORIS on the high seas

Yesterday was the first test of the full DORIS marine mapping system I’m developing with Amber Teacher and David Hodgson at Exeter University. We went out on a fishing boat from Mylor harbour for a 5 hour trip along the Cornish coast. It’s a quiet season for lobsters at the moment, so this was an opportunity to practice the sampling without too much pressure. Researcher Charlie Ellis was working with Hannah Knott, who work with the National Lobster Hatchery and need to take photos of hundreds of lobsters and combine them with samples of their genetic material.


By going out on the boats they get accurate GPS positioning in order to determine detailed population structures, and can sample lobsters that are small or with eggs and need to be returned to the sea as well as the ones the fishermen take back to shore to be sold. Each photograph consists of a cunning visual information system of positioning objects to indicate sex, whether they are for return or removal and a ruler for scale.




Doris: Lobster mapping

A new project, coming from Borrowed Scenery’s Zizim project, converted into a scientific research tool in collaboration with the College of Life and Environmental Sciences at Exeter University and Helsinki University. Doris is named after the sea nymph from Greek mythology, and will be used for mapping Lobster catches on fishing boats so researchers working at the National Lobster Hatchery in Padstow can easily build up a picture of how the animal’s condition relates to location, sea conditions and tide.

Here is an initial plan for how the thing will work:

The main complexities include locating open data sources for sea states and tides and creating an interface that works easily enough on a small fishing boat under various weather conditions – for example touch screens aren’t much use if you’re wearing gloves. Approaches to try include using the physical buttons, shaking, or voice input. As with previous FoAM projects Boskoi and Zizim, this will be built on the Ushahidi platform. Source repo location to follow…

Hapstar graphs in the wild

Some examples of graphs that scientists have created and published using Hapstar, all these images were taken from the papers that cite the hapstar publication, with links to them below. I think the range of representations of this genetic information indicate some exciting new directions we can take the software in. There are also some possibilities regarding the minimum spanning tree, finding ways to visualise and explore the range of possible MST’s for a given graph.

IVENS, ABF, et al. “Reproduction and dispersal in an ant‐associated root aphid community.” Molecular Ecology (2012).

Wielstra, Ben, and Jan Arntzen. “Postglacial species displacement in Triturus newts deduced from asymmetrically introgressed mitochondrial DNA and ecological niche models.” BMC Evolutionary Biology 12.1 (2012): 161.

Kesäniemi, J. E., Rawson, P. D., Lindsay, S. M. and Knott, K. E. (2012), Phylogenetic analysis of cryptic speciation in the polychaete Pygospio elegans. Ecology and Evolution, 2: 994–1007. doi: 10.1002/ece3.226

Vos M, Quince C, Pijl AS, de Hollander M, Kowalchuk GA (2012) A Comparison of rpoB and 16S rRNA as Markers in Pyrosequencing Studies of Bacterial Diversity. PLoS ONE 7(2): e30600. doi:10.1371/journal.pone.0030600

Hapstar version 0.7

Hapstar has achieved some popularity in the genetics community, the paper has currently racked up 13 citations, including one for it’s use to create a figure for a Nature paper.

This version addresses some requests from users, including the ability to save graphs midway though balancing. This means it’s easier to run them for long periods of time. It can be downloaded from the official page on the FoAM site.


Hapstar is the new name for my little bioinformatics project, visualising population structure from differences between genetic sequences. The last few days I’ve done a lot of work on the GUI, allowing you to drag nodes around by hand, zoom in and out and navigate the graph. Also quite a bit of graph theory, validating the input graphs by detecting if they are a single connected component – or contain isolated graphs. It’s already been used to produce figures for an upcoming publication, so hopefully I can show some real examples soon.


A small bioinformatics project in progress:

Haplotype networks and Minimum Spanning Networks are commonly used for representing associations between sequences. HapNet is a tool for viewing both types of networks, using the output data generated from Arlequin. HapNet automatically formats the network in the optimal layout for easy visualisation, and publication-ready figures can be exported in several formats.

After calculating the minimum spanning trees of the networks, my initial reaction was to use graphviz for this, as it seems perfect for the job. However, I had a lot of trouble with the different length edges, and the need to represent distance with intermediate nodes which have to be on a straight path. As I’d already written force directed graph drawing for daisy it wasn’t too hard to adapt. Source code here, and the start of a proper webpage on the libarynth.