Open access to vast amount of expression sequence tags (ESTs) data in the public databases has provided a powerful platform for gene identification, gene expression studies and comparative/functional genomic studies. To facilitate management of large-scale EST data, high performance cluster and analysis softwares, especially parallel softwares, are fundamentally essential. We reported herein a convenient approach to construct a high performance computating (HPC) cluster based on popular Rocks and a perl-scripted analysis pipeline for EST pre-processing, clustering, assembling and annotation and any other desired analysis modules through parallel computing. We tested the system using different datasets on increasing nodes. Our present results showed that the cluster and pipeline accelerate the EST analysis without artificial interference.
Posted by
Peter Suber at 12/05/2005 11:02:00 AM.
The open access movement:
Putting peer-reviewed scientific and scholarly literature
on the internet. Making it available free of charge and
free of most copyright and licensing restrictions.
Removing the barriers to serious research.
I recommend the OA tracking project (OATP) as the best way to stay on top of new OA developments. You can read the OATP feed on a blog-like web page or subscribe to it by RSS, email, or Twitter. You can also help build the feed by tagging new developments you encounter.
