NDLI: Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing

Content Provider	ACM Digital Library
Author	Belcastro, Vincenzo Gregoretti, Francesco Oliva, Gennaro Di bernardo, Diego D'angelo, Giovanni Santoro, Michele Siciliano, Velia
Abstract	Regulation of gene expression is a carefully regulated phenomenon in the cell. "Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover "communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.
Starting Page	668
Ending Page	678
Page Count	11
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2011.60
Volume Number	9
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-05-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Reverse engineering, gene regulatory network, clustering algorithm, parallel computing.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing

Parallel Computing Algorithms for Reverse-Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles

Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

Genetic Networks and Soft Computing

Genetic Networks and Soft Computing

Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.

Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing

Similar Documents

Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing

Parallel Computing Algorithms for Reverse-Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles

Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

Genetic Networks and Soft Computing

Genetic Networks and Soft Computing

Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.

Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing