The Exelixis Lab


Enabling Research in Evolutionary Biology

Our Mission - Enable 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.

2020 summer school on computational molecular evolution

Applications are now closed for the 12th summer school on computational molecular evolution organized by Ziheng Yang, Cilia Antoniou, Adam Leache and Alexis Stamatakis. It will be held once again at the Hellenic Center for Marine Research near Heraklion, Crete, Greece.

See the course page for further details

New Software

GeneRax: Species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss

Our first tool for gene tree species tree reconciliation. Open-source code is available here, a respective preprint can be found here.

ParGenes: massively parallel inference of gene trees

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.

RAxML-NG Web-Server

The brand new web-server for the completely re-designed version of RAxML, called RAxML-NG is available here.

Complete re-design of Evolutionary Placement Algorithm

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.

Low Level Phylogenetic Likelihood Library version 2

This is version 2 of our low level phylogenetic likelihood library.

It now includes site repeats (see paper) to boost computations likelihood computations. It can be downloaded here and has been already integrated into RAxML-NG and Modeltest-NG (see below).

Completely re-designed Modeltest Software

We have completeley re-designed the famous Modeltest software. It's much faster, better, and more stable now.

The all new Modeltest can be downloaded here.

RAxML-NG

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

Students from KIT

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

New Papers

Our 5 Most Recent Papers

  1. Dora Serdari, Evangelia-Georgia Kostaki, Dimitrios Paraskevis, Alexandros Stamatakis, Paschalia Kapli​, "Automated, phylogeny-based genotype delimitation of the Hepatitis Viruses HBV and HCV", PeerJ, 7:e7754, open access, 2019.
  2. Paula Breitling, Ben Bettisworth, Lukasz Reszczynski, Olga Chernomor, Alexandros Stamatakis, "Empirical Analysis of Phylogenetic Quasi-Terraces", bioRxiv, 810309, 2019, preprint.
  3. Nicolas Comte, Benoit Morel, Damir Hasic, Laurent Guéguen, Bastien Boussau, Vincent Daubin, Simon Penel, Celine Scornavacca, Manolo Gouy, Alexandros Stamatakis, Eric Tannier, David P. Parsons, "Treerecs: an integrated phylogenetic tool, from sequences to reconciliations", bioRxiv, 782946, 2019, preprint.
  4. Benoit Morel, Alexey M. Kozlov, Alexandros Stamatakis, Gergely Szöllősi. "GeneRax: A tool for species tree-aware maximum likelihood based gene tree inference under gene duplication, transfer, and loss.", bioRxiv, 779066, 2019, preprint.
  5. Sarah Lutteropp, Alexey M. Kozlov, Alexandros Stamatakis. "A Fast and Memory-Efficient Implementation of the Transfer Bootstrap", bioRxiv, 734848, 2019, preprint.
All publications >>