Literature DB >> 20513661

FastMEDUSA: a parallelized tool to infer gene regulatory networks.

Serdar Bozdag1, Aiguo Li, Stefan Wuchty, Howard A Fine.   

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

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Year:  2010        PMID： 20513661      PMCID： PMC2894517          DOI： 10.1093/bioinformatics/btq275

Source DB:  PubMed          Journal:  Bioinformatics        ISSN： 1367-4803            Impact factor:   6.937


  7 in total

1.  Regulatory element detection using correlation with expression.

Authors:  H J Bussemaker; H Li; E D Siggia
Journal:  Nat Genet       Date:  2001-02       Impact factor: 38.330

2.  Genome-wide discovery of transcriptional modules from DNA sequence and gene expression.

Authors:  E Segal; R Yelensky; D Koller
Journal:  Bioinformatics       Date:  2003       Impact factor: 6.937

3.  MatInspector and beyond: promoter analysis based on transcription factor binding sites.

Authors:  K Cartharius; K Frech; K Grote; B Klocke; M Haltmeier; A Klingenhoff; M Frisch; M Bayerlein; T Werner
Journal:  Bioinformatics       Date:  2005-04-28       Impact factor: 6.937

4.  A universal framework for regulatory element discovery across all genomes and data types.

Authors:  Olivier Elemento; Noam Slonim; Saeed Tavazoie
Journal:  Mol Cell       Date:  2007-10-26       Impact factor: 17.970

5.  Learning regulatory programs that accurately predict differential expression with MEDUSA.

Authors:  Anshul Kundaje; Steve Lianoglou; Xuejing Li; David Quigley; Marta Arias; Chris H Wiggins; Li Zhang; Christina Leslie
Journal:  Ann N Y Acad Sci       Date:  2007-10-12       Impact factor: 5.691

6.  Unsupervised analysis of transcriptomic profiles reveals six glioma subtypes.

Authors:  Aiguo Li; Jennifer Walling; Susie Ahn; Yuri Kotliarov; Qin Su; Martha Quezado; J Carl Oberholtzer; John Park; Jean C Zenklusen; Howard A Fine
Journal:  Cancer Res       Date:  2009-02-24       Impact factor: 12.701

7.  A predictive model of the oxygen and heme regulatory network in yeast.

Authors:  Anshul Kundaje; Xiantong Xin; Changgui Lan; Steve Lianoglou; Mei Zhou; Li Zhang; Christina Leslie
Journal:  PLoS Comput Biol       Date:  2008-11-14       Impact factor: 4.475
  7 in total
  9 in total

1.  PhosphoChain: a novel algorithm to predict kinase and phosphatase networks from high-throughput expression data.

Authors:  Wei-Ming Chen; Samuel A Danziger; Jung-Hsien Chiang; John D Aitchison
Journal:  Bioinformatics       Date:  2013-07-05       Impact factor: 6.937

Review 2.  Integration and analysis of genome-scale data from gliomas.

Authors:  Gregory Riddick; Howard A Fine
Journal:  Nat Rev Neurol       Date:  2011-07-05       Impact factor: 42.937

3.  Genomic analysis of immune response against Vibrio cholerae hemolysin in Caenorhabditis elegans.

Authors:  Surasri N Sahu; Jada Lewis; Isha Patel; Serdar Bozdag; Jeong H Lee; Joseph E LeClerc; Hediye Nese Cinar
Journal:  PLoS One       Date:  2012-05-31       Impact factor: 3.240

4.  Master regulators, regulatory networks, and pathways of glioblastoma subtypes.

Authors:  Serdar Bozdag; Aiguo Li; Mehmet Baysan; Howard A Fine
Journal:  Cancer Inform       Date:  2014-10-15

5.  The feasibility of genome-scale biological network inference using Graphics Processing Units.

Authors:  Raghuram Thiagarajan; Amir Alavi; Jagdeep T Podichetty; Jason N Bazil; Daniel A Beard
Journal:  Algorithms Mol Biol       Date:  2017-03-20       Impact factor: 1.405

6.  Genomic analysis of stress response against arsenic in Caenorhabditis elegans.

Authors:  Surasri N Sahu; Jada Lewis; Isha Patel; Serdar Bozdag; Jeong H Lee; Robert Sprando; Hediye Nese Cinar
Journal:  PLoS One       Date:  2013-07-24       Impact factor: 3.240

7.  Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.

Authors:  Joana P Gonçalves; Ricardo S Aires; Alexandre P Francisco; Sara C Madeira
Journal:  PLoS One       Date:  2012-05-01       Impact factor: 3.240

8.  Molecular mechanisms of system responses to novel stimuli are predictable from public data.

Authors:  Samuel A Danziger; Alexander V Ratushny; Jennifer J Smith; Ramsey A Saleem; Yakun Wan; Christina E Arens; Abraham M Armstrong; Katherine Sitko; Wei-Ming Chen; Jung-Hsien Chiang; David J Reiss; Nitin S Baliga; John D Aitchison
Journal:  Nucleic Acids Res       Date:  2013-10-31       Impact factor: 16.971

9.  The inferred cardiogenic gene regulatory network in the mammalian heart.

Authors:  Jason N Bazil; Karl D Stamm; Xing Li; Raghuram Thiagarajan; Timothy J Nelson; Aoy Tomita-Mitchell; Daniel A Beard
Journal:  PLoS One       Date:  2014-06-27       Impact factor: 3.240
  9 in total

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