Statistical Methods for the Discovery of Co-Operative Transcription Factors: The Co-bind Code Revised

Authors:
Giovanni Lavorgna;Paolo Palazzari;Alessandro Marongiu;Vittorio Rosato;Simone Melchionna;Paolo Verrecchia
Affiliations:
Hospital San Raffaele, Milan;ENEA, Computing and Modeling Unit, Rome;ENEA, Informatic Unit, Portici, Rome;ENEA, Computing and Modeling Unit, Rome;Albatel SpA - Rome and INFM University "La Sapienza", Rome;Albatel SpA, Rome
Venue:
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 7 - Volume 08
Year:
2005

Citing 2
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An Exact Method for Finding Short Motifs in Sequences, with Application to the Ribosome Binding Site Problem

Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Monte Carlo Strategies in Scientific Computing

Monte Carlo Strategies in Scientific Computing

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Abstract

Discovering co-operative Transcription Factors (TF's) within the genome is a computationally challenging problem, tackled through Monte Carlo-like analysis by the Co-Bind code, developed at the Department of Genetics of the St. Louis Washington University. Due to its statistical nature, Co-Bind is characterized by very long execution times, order of days on current high-end workstations, and could benefit from parallelization and a wise optimization, performed at both the algorithmic and coding levels. This work presents the results achieved by parallelizing Co-Bind and optimising the parallel code and shows that, on a 16-processor architecture, a speedup greater than two orders of magnitude is achieved with respect to the serial version released by the code's authors.