Abstract
Motivation:
In order to construct gene regulatory networks of higher organisms from gene expression and promoter sequence data efficiently, we developed FastMEDUSA. In this parallelized version of the regulatory network-modeling tool MEDUSA, expression and sequence data are shared among a user-defined number of processors on a single multi-core machine or cluster. Our results show that FastMEDUSA allows a more efficient utilization of computational resources. While the determination of a regulatory network of brain tumor in
Homo sapiens
takes 12 days with MEDUSA, FastMEDUSA obtained the same results in 6 h by utilizing 100 processors.
Availability:
Source code and documentation of FastMEDUSA are available at
https://wiki.nci.nih.gov/display/NOBbioinf/FastMEDUSA
Contact:
hfine@mail.nih.gov
Supplementary information:
Supplementary data
are available at
Bioinformatics
online.
