Parallel computation of phylogenetic consensus trees

The field of bioinformatics is witnessing a rapid and overwhelming accumulation of molecular sequence data, predominantly driven by novel wet-lab sequencing techniques. This trend poses scalability challenges for tool developers. In the field of phylogenetic inference (reconstruction of evolutionary trees from molecular sequence data), scalability is becoming an increasingly important issue for operations other than the tree reconstruction itself. In this paper we focus on a post-analysis task in reconstructing very large trees, specifically the step of building (extended) majority rules consensus trees from a collection of equally plausible trees or a collection of bootstrap replicate trees. To this end, we present sequential optimizations that establish our implementation as the current fastest exact implementation in phylogenetics, and our novel parallelized routines are the first of their kind. Our sequential optimizations achieve a performance improvement of factor 50 compared to the previous version of our code and we achieve a maximum speedup of 5.5 on a 8-core Nehalem node for building consensi on trees comprising up to 55,000 organisms. The methods developed here are integrated into the widely used open-source tool RAxML for phylogenetic tree