MADIBA: A web server toolkit for biological interpretation of Plasmodium and plant gene clusters

Philip J Law,Fourie Joubert,Dave K Berger,Clotilde Claudel-Renard,Abraham I Louw

doi:10.1186/1471-2164-9-105

Philip J Law, Fourie Joubert + Show 3 more

Open Access

https://doi.org/10.1186/1471-2164-9-105

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Journal: BMC Genomics	Publication Date: Feb 28, 2008
Citations: 61	License type: cc-by

Affiliation: University of Pretoria, Biogemma (France)

Abstract

BackgroundMicroarray technology makes it possible to identify changes in gene expression of an organism, under various conditions. Data mining is thus essential for deducing significant biological information such as the identification of new biological mechanisms or putative drug targets. While many algorithms and software have been developed for analysing gene expression, the extraction of relevant information from experimental data is still a substantial challenge, requiring significant time and skill.DescriptionMADIBA (MicroArray Data Interface for Biological Annotation) facilitates the assignment of biological meaning to gene expression clusters by automating the post-processing stage. A relational database has been designed to store the data from gene to pathway for Plasmodium, rice and Arabidopsis. Tools within the web interface allow rapid analyses for the identification of the Gene Ontology terms relevant to each cluster; visualising the metabolic pathways where the genes are implicated, their genomic localisations, putative common transcriptional regulatory elements in the upstream sequences, and an analysis specific to the organism being studied.ConclusionMADIBA is an integrated, online tool that will assist researchers in interpreting their results and understand the meaning of the co-expression of a cluster of genes. Functionality of MADIBA was validated by analysing a number of gene clusters from several published experiments – expression profiling of the Plasmodium life cycle, and salt stress treatments of Arabidopsis and rice. In most of the cases, the same conclusions found by the authors were quickly and easily obtained after analysing the gene clusters with MADIBA.

Highlights

Microarray technology makes it possible to identify changes in gene expression of an organism, under various conditions
BMC Genomics 2008, 9:105 http://www.biomedcentral.com/1471-2164/9/105 different biological levels. These include databases on the annotation of genes according to the Gene Ontology (GO) nomenclature [1], metabolic pathways as in KEGG [2], or Transcription Factor Binding Sites (TFBS) in TRANSFAC [3] to annotate promoters
FatiGO [4], GeneLynx [5] and Gostat [6] are powerful tools for GO term identification; GoMiner [7], MAPPFinder [8] and DAVID [9] propose GO and metabolic pathway interpretation; MiCoViTo [10] proposes metabolic pathways and incorporates transcription regulation visualisation; metaSHARK [11] predicts enzymecoding genes from unannotated genome data and places them on generic metabolic pathways; and WebGestalt [12] uses data obtained from different public resources and offers an integrated platform to perform various analyses such as a GO analysis, metabolic pathways and chromosomal distributions

Summary

Introduction

Microarray technology makes it possible to identify changes in gene expression of an organism, under various conditions. Tools within the web interface allow rapid analyses for the identification of the Gene Ontology terms relevant to each cluster; visualising the metabolic pathways where the genes are implicated, their genomic localisations, putative common transcriptional regulatory elements in the upstream sequences, and an analysis specific to the organism being studied. BMC Genomics 2008, 9:105 http://www.biomedcentral.com/1471-2164/9/105 different biological levels These include databases on the annotation of genes according to the Gene Ontology (GO) nomenclature [1], metabolic pathways as in KEGG [2], or Transcription Factor Binding Sites (TFBS) in TRANSFAC [3] to annotate promoters. FatiGO [4], GeneLynx [5] and Gostat [6] are powerful tools for GO term identification; GoMiner [7], MAPPFinder [8] and DAVID [9] propose GO and metabolic pathway interpretation; MiCoViTo [10] proposes metabolic pathways and incorporates transcription regulation visualisation; metaSHARK [11] predicts enzymecoding genes from unannotated genome data and places them on generic metabolic pathways; and WebGestalt [12] uses data obtained from different public resources and offers an integrated platform to perform various analyses such as a GO analysis, metabolic pathways and chromosomal distributions

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