The Community for Technology Leaders
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 1827-1831
William L. Poehlman , Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634
Mats Rynge , Information Sciences Institute, University of Southern California, Marina Del Rey, CA 90292
D. Balamurugan , Computation Institute, University of Chicago, Chicago, IL 60637
Nicholas Mills , Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634
Frank A. Feltus , Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634
ABSTRACT
Gene Co-expression Network (GCN) analysis is a method to characterize the complexity underlying biological systems. With an increasing availability of datasets available for mining complex gene expression patterns, novel algorithms and computational frameworks must be developed to take advantage of the wealth of information. OSG-KINC is a Pegasus workflow that enables highly parallel execution of KINC — Knowledge Independent Network Construction — using resources available on the Open Science Grid (OSG). A yeast GCN was constructed using the OSG-KINC workflow, providing an example GCN resource for biological hypothesis testing. Timing experiments demonstrate that the number of jobs submitted by the user significantly affects the performance of the workflow. An overview of workflow usage, bottlenecks, and efforts for improvement is provided. OSG-KINC is freely available at https://github.com/feltus/OSG-KINC under GNU General Public License version 3.
INDEX TERMS
Gene expression, Correlation, RNA, Biological information theory, Genomics, Software
CITATION

W. L. Poehlman, M. Rynge, D. Balamurugan, N. Mills and F. A. Feltus, "OSG-KINC: High-throughput gene co-expression network construction using the open science grid," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 1827-1831.
doi:10.1109/BIBM.2017.8217938
320 ms
(Ver 3.3 (11022016))