By Topic

Distributed Sequence Alignment Applications for the Public Computing Architecture

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Pellicer, S. ; Georgia State Univ., Atlanta ; Guihai Chen ; Chan, K.C.C. ; Yi Pan

The public computer architecture shows promise as a platform for solving fundamental problems in bioinformatics such as global gene sequence alignment and data mining with tools such as the basic local alignment search tool (BLAST). Our implementation of these two problems on the Berkeley open infrastructure for network computing (BOINC) platform demonstrates a runtime reduction factor of 1.15 for sequence alignment and 16.76 for BLAST. While the runtime reduction factor of the global gene sequence alignment application is modest, this value is based on a theoretical sequential runtime extrapolated from the calculation of a smaller problem. Because this runtime is extrapolated from running the calculation in memory, the theoretical sequential runtime would require 37.3 GB of memory on a single system. With this in mind, the BOINC implementation not only offers the reduced runtime, but also the aggregation of the available memory of all participant nodes. If an actual sequential run of the problem were compared, a more drastic reduction in the runtime would be seen due to an additional secondary storage I/O overhead for a practical system. Despite the limitations of the public computer architecture, most notably in communication overhead, it represents a practical platform for grid- and cluster-scale bioinformatics computations today and shows great potential for future implementations.

Published in:

NanoBioscience, IEEE Transactions on  (Volume:7 ,  Issue: 1 )