HipMCL: A massively parallel algorithm for Markov Clustering

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.

Berkeley Lab to Lead Five Exascale Projects, Support Six Others

“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.

Meraculous: Deciphering the 'Book of Life’ with supercomputers

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.

A “Meraculous” Way to Conduct Whole-Genome Assemblies

In a paper published August 18, 2011 in PLoS ONE, researchers used meraculous to assemble 75-bp Illuminareads of the yeast Pichiastipitis, a microbial fermenter of the five-carbon sugar xylose for ethanol production that was sequenced by the DOE JGI in 2007.

Big Plant Genomes: Formerly intractable, no longer insurmountable

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).