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NCI Uses Star-P to Parallelize Array Analysis

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Bioinformaticists looking to implement Blast or Smith-Waterman on a multiprocessor cluster have plenty of parallel packages to choose from, but when it comes to parallelizing custom applications, the only option is often just to rewrite the program from scratch.

Interactive Supercomputing is looking to change that through a software product called Star-P, which simplifies the process of porting desktop applications written in high-level languages like Matlab to compute clusters.

In February, the company announced that the National Cancer Institute’s Pediatric Oncology Branch had successfully ported an internally developed software application called CORR4DB from a desktop environment to an eight-processer SGI Altix system to gain a 200-fold speedup.

CORR4DB calculates correlations between genes in microarray gene-expression experiments. The program was written for a single-processor Pentium desktop, which ran out of memory at the 10,000-sample mark, according to a case study provided by ISC. A typical experiment took up to a week to analyze in serial using desktop PCs, the company says.

However, porting the application to a multiprocessor system would have required it to be rewritten, in which case all the benefits of the original Matlab environment would have been lost, says Mark Potts, president of HPC Applications, a consulting firm that NCI contracted to migrate CORR4DB to the Altix cluster.

Without Star-P, “you would have started over and adopted some C or C++ and MPI in order to parallelize this,” says Potts. “In other words, you would have thrown away the familiar framework, and that was its real strength.”

He says that as a result of using the Star-P platform, the NCI researchers saw a 200-fold speedup in their analysis, without losing the familiar interactivity of their desktop application. “The only compromise they had to make was they had to move their data to the server,” Potts says.

Bernadette Toner

Short Reads

Swedish FPGA supercomputing outfit Mitrionics has released BlastP for use on its Mitrion Virtual Processor running FPGA computer systems. This is the second open source Blast application the company has released for use on its new accelerator.

Ingenuity Systems says that it will integrate its Ingenuity Pathway Analysis software with the JMP Genomics software from SAS and the Oncomine cancer gene expression database.

Swedish bioinformatics company Ludesi has announced that it will provide its 2D gel image-analysis software to nutritional and pharmaceutical manufacturer DSM. According to Ludesi, the software helps DSM streamline its proteomics pipeline for studying microbial metabolism.

GenoLogics has announced that the Hospital for Sick Children Research Institute in Toronto will use the company’s Geneus software for collaborative stem cell researcher projects with nine other labs. The hospital will install an early version of the software to manage microarray, flow cytometry, and RNA-preparation information.

Chugai Pharmaceuticals is using Genedata’s Screener software to analyze high-throughput screening data. Genedata says that Chugai has been using its software to develop drugs for cancer as well as joint and bone diseases.

Patents

US Patent 7,167,847. DNA computer and a computation method using the same. Inventor: Fumiyoshi Sasagawa. Assignee: Icagen. Issued: January 30, 2007.

This patent describes “a DNA computer for carrying out computations using DNAs … with a dividing part for dividing a problem that is to be solved into a plurality of partial problems, and an operation part for obtaining a DNA sequence corresponding to a solution to the problem, by combining DNA sequences corresponding to solutions of the plurality of partial problems.”

US Patent 7,177,767. Systems and methods for the detection of short and long samples. Inventors: Vladimir Ostoich, Kennth Aron, Dennis Bleile. Assignee: Abaxis. Issued: February 13, 2007.

The patent covers systems “for detecting the accidental use of short and long samples in the clinical analysis of a sample, specimen, or assay.” A clinical analyzer determines values for measurable characteristics of a sample. “These values are used in combination with reference data stored in a data module to generate a probability that the sample tested is a short sample, a long sample, or an acceptable sample.”

Data Point

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