Literature DB >> 12057687

Functional genomics as applied to mapping transcription regulatory networks.

Nila Banerjee1, Michael Q Zhang.   

Abstract

The sequencing of the human genome and the entire genomes of many model organisms has resulted in the identification of many genes. Many large-scale experiments for generating gene disruptions and analyzing the phenotypes are underway to ascertain gene function. A future challenge will be to determine interaction and regulation of all the genes of an organism. Recent advances in functional genomic technology have begun to shine light on such gene network problems at both transcriptomic and proteomic levels. Functional genomics will not only elucidate what the genes do, but will also help determine when, where and how they are expressed as an orchestrated system. In this review, we discuss the functional genomics approaches to extract knowledge about transcription regulatory mechanisms from combinations of sequence data, microarray data and ChIP data. We focus in particular on the budding yeast Saccharomyces cerevisiae.

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Year:  2002        PMID: 12057687     DOI: 10.1016/s1369-5274(02)00322-3

Source DB:  PubMed          Journal:  Curr Opin Microbiol        ISSN: 1369-5274            Impact factor:   7.934


  23 in total

1.  Identifying cooperativity among transcription factors controlling the cell cycle in yeast.

Authors:  Nilanjana Banerjee; Michael Q Zhang
Journal:  Nucleic Acids Res       Date:  2003-12-01       Impact factor: 16.971

2.  REDUCE: An online tool for inferring cis-regulatory elements and transcriptional module activities from microarray data.

Authors:  Crispin Roven; Harmen J Bussemaker
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

3.  Reconciling gene expression data with known genome-scale regulatory network structures.

Authors:  Markus J Herrgård; Markus W Covert; Bernhard Ø Palsson
Journal:  Genome Res       Date:  2003-10-14       Impact factor: 9.043

4.  Prospects of a computational origin of life endeavor.

Authors:  Barak Shenhav; Doron Lancet
Journal:  Orig Life Evol Biosph       Date:  2004-02       Impact factor: 1.950

5.  Homocysteine- and cysteine-mediated growth defect is not associated with induction of oxidative stress response genes in yeast.

Authors:  Arun Kumar; Lijo John; Md Mahmood Alam; Ankit Gupta; Gayatri Sharma; Beena Pillai; Shantanu Sengupta
Journal:  Biochem J       Date:  2006-05-15       Impact factor: 3.857

6.  The wavelet-based cluster analysis for temporal gene expression data.

Authors:  J Z Song; K M Duan; T Ware; M Surette
Journal:  EURASIP J Bioinform Syst Biol       Date:  2007

Review 7.  Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images.

Authors:  Svetlana Surkova; Ekaterina Myasnikova; Hilde Janssens; Konstantin N Kozlov; Anastasia A Samsonova; John Reinitz; Maria Samsonova
Journal:  Fly (Austin)       Date:  2008-03-08       Impact factor: 2.160

8.  KT/HAK/KUP potassium transporters gene family and their whole-life cycle expression profile in rice (Oryza sativa).

Authors:  Madhur Gupta; Xuhua Qiu; Lei Wang; Weibo Xie; Chengjun Zhang; Lizhong Xiong; Xingming Lian; Qifa Zhang
Journal:  Mol Genet Genomics       Date:  2008-09-23       Impact factor: 3.291

9.  Methods for Acquisition of Quantitative Data from Confocal Images of Gene Expression in situ.

Authors:  S Yu Surkova; E M Myasnikova; K N Kozlov; A A Samsonova; J Reinitz; M G Samsonova
Journal:  Cell tissue biol       Date:  2008-04

10.  RegAnalyst: a web interface for the analysis of regulatory motifs, networks and pathways.

Authors:  Deepak Sharma; Debasisa Mohanty; Avadhesha Surolia
Journal:  Nucleic Acids Res       Date:  2009-05-21       Impact factor: 16.971

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