Phylogenetics is concerned with inferring the genealogical relationships between a group of organisms (or taxa) and this relationship is usually expressed as an evolutionary tree. However, obtaining such trees is very difficult (most approaches use NP-hard optimization criteria). As a result, most phylogenetic analyses rely on heuristics to obtain accurate (best-scoring) trees. It is not uncommon for heuristics to return thousands of best-scoring trees. Hence, fast post-processing techniques are needed in order to summarize effectively the relationships depicted among the evolutionary trees.

In this talk, I will present new post-processing algorithms for phylogenetics based on MapReduce, a parallel framework popularized by Google to design parallel applications for large-scale data applications on large computing clusters. Finally, I will discuss additional applications of our post-processing algorithms to facilitate the reconstruction of accurate evolutionary trees.