A Game Changer: Metagenomic Clustering Powered by HPC
The distributed-memory parallel HipMCL software, powered by state-of-the-art sparse matrix algorithms, allows biologists to harness the capabilities of massively parallel supercomputers to cluster large-scale protein networks.
“Exascale Solutions for Microbiome Analysis” will be led by Associate Lab Director Yelick, with support from Los Alamos National Laboratory and DOE’s Joint Genome Institute. The project will use machine learning algorithms and a high performance metagenome assembler based on the Meraculous application to study microbial diversity with the goal of developing new products and identifying new life forms. Meraculous is a NESAP application.
By applying some novel algorithms, computational techniques and the innovative programming language Unified Parallel C (UPC) to the cutting-edge de novo genome assembly tool Meraculous, a team of scientists from the Lawrence Berkeley National Laboratory (Berkeley Lab)’s Computational Research Division (CRD), Joint Genome Institute (JGI) and UC Berkeley, simplified and sped up genome assembly, reducing a months-long process to mere minutes.
Through a combination of high-throughput sequencing, high performance computing, and genetic mapping. In prior work, exabiome researchers and collaborators derived a sequence assembly for the highly repetitive plant genome of bread wheat (Triticum aestivum).