Paper Info
Title
MetaPathways v2.0: A master-worker model for environmental Pathway/Genome Database construction on grids and clouds
Abstract
The development of high-throughput sequencing technologies over the past decade has generated a tidal wave of environmental sequence information from a variety of natural and human engineered ecosystems. The resulting flood of information into public databases and archived sequencing projects has exponentially expanded computational resource requirements rendering most local homology-based search methods inefficient. We recently introduced MetaPathways v1.0, a modular annotation and analysis pipeline for constructing environmental Pathway/Genome Databases (ePGDBs) from environmental sequence information capable of using the Sun Grid engine for external resource partitioning. However, a command-line interface and facile task management introduced user activation barriers with concomitant decrease in fault tolerance. Here we present MetaPathways v2.0 incorporating a graphical user interface (GUI) and refined task management methods. The MetaPathways GUI provides an intuitive display for setup and process monitoring and supports interactive data visualization and sub-setting via a custom Knowledge Engine data structure. A master-worker model is adopted for task management allowing users to scavenge computational results from a number of worker grids in an ad hoc, asynchronous, distributed network that dramatically increases fault tolerance. This model facilitates the use of EC2 instances extending ePGDB construction to the Amazon Elastic Cloud.
Year
DOI
Venue
2014
10.1109/CIBCB.2014.6845516
Honolulu, HI
Keywords
Field
DocType
biological techniques,biology computing,fault tolerance,genomics,graphical user interfaces,molecular biophysics,task analysis,Amazon elastic cloud,MetaPathways GUI,MetaPathways v2.0,ad hoc network,asynchronous network,command-line interface,distributed network,ePGDB,environmental pathway/genome database construction,environmental sequence information,external resource partitioning,facile task management,fault tolerance,graphical user interface,interactive data visualization,knowledge engine data structure,master-worker model,refined task management methods,sun grid engine,user activation barriers,worker grids
Data structure,Data visualization,Task management,Computer science,Fault tolerance,Graphical user interface,Artificial intelligence,Bioinformatics,Rendering (computer graphics),Grid,Machine learning,Computational resource
Conference
Citations 
PageRank 
References 
8
0.53
8
Authors
4
Name
Order
Citations
PageRank
Niels W. Hanson1293.58
Kishori M. Konwar210717.49
Shang-Ju Wu380.53
Steven J. Hallam4343.97