Large-scale phylogenetic analysis on current HPC architectures

Abstract. Phylogenetic inference is considered a grand challenge in Bioinformatics due to its immense computational requirements. The increasing popularity and availability of large multi-gene alignments as well as comprehensive datasets of single nucleotide polymorphisms (SNPs) in current biological studies, coupled with rapid accumulation of sequence data in general, pose new challenges for high performance computing. By example of RAxML, which is currently among the fastest and most accurate programs for phylogenetic inference under the Maximum Likelihood (ML) criterion, we demonstrate how the phylogenetic ML function can be efficiently scaled to current supercomputer architectures like the IBM BlueGene/L (BG/L) and SGI Altix. This is achieved by simultaneous exploitation of coarse- and fine-grained parallelism which is inherent to every ML-based biological analysis. Performance is assessed using datasets consisting of 270 sequences and 566,470 base pairs (haplotype map dataset), an...

• Type: Journal
• Year: 2008
• Where: SP
• Authors: Michael Ott, Jaroslaw Zola, Srinivas Aluru, Andrew D. Johnson, Daniel Janies, Alexandros Stamatakis
• Keywords: Base Pairs, Fine-grained Parallelism, Phylogenetic Inference, Security Privacy, SP 2008