Enabling Research in Evolutionary Biology
Our focus is on the evolution of hardware and parallel computer architectures as well as on the evolution of molecular sequences.
We understand Bioinformatics as a discipline that develops algorithms, models, and tools that help Biologists to generate new biological insights and knowledge. We try to bridge the gap between the world of systematics and the world of high performance computing.
Due to the increasing descrepancy between the pace of molecular data accumulation and increase in CPU speeds (which is much slower), which we call the "Bio-Gap" we feel that the time has come to establish parallel computing as standard technique in Bioinformatics.
We discovered an analogy between Felsenstein's pruning algorithm and the exact as well as efficient calculation of the win probabilities of teams in knock-out tournaments given a pairwise win probability matrix P. We implemented this method and apply it to predict the knock-out phase of the UEFA EURO 2020 football tournament. The most likely final is France - England and England has a slight advantage of winning it.
See the preprint for further details
Species Counting on Reference trees viA Phylogenetic Placements - bridging the gap between phylogenetic placement results and alpha diversity metrics, with the possibility of further in-depth analysis.
It is available for download here
RootDigger uses a non-reversible Markov model to compute the most likely root location on a given tree.
It is available for download here
Our first tool for gene tree species tree reconciliation. Open-source code is available here, a respective preprint can be found here.
A tool for massively parallel model selection and phylogenetic tree inference on thousands of genes using thousands of cores, based on ModelTest-NG and RAxML-NG. Now published in Bionformatics and available for download here.
The brand new web-server for the completely re-designed version of RAxML, called RAxML-NG is available here.
This is a completely re-designed version of our Evolutionary Placement Algorithm. It is at least 6 times faster than pplacer/EPA and has substantially improved parallel scalability.
It is available for download here
As before EPA-NG user support will be provided via the raxml google group.
All software >>The latest version of RAxML-NG, the complete re-design of RAxML can be found at Alexey's github repository.
RAxML questions, help & bug reports: please use the RAxML google group
The respective paper can be found here
We are always looking for student programmers (HiWis) and students interested in doing bachelor/master theses projects with us. If you are interested please send an email to Alexis at Alexandros dot Stamatakis at h hyphen its dot org