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

Law PJ, Claudel-Renard C, Joubert F, Louw AI, Berger DK - BMC Genomics (2008)

Bottom Line: 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.MADIBA 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.In most of the cases, the same conclusions found by the authors were quickly and easily obtained after analysing the gene clusters with MADIBA.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics and Computational Biology Unit, African Centre for Gene Technologies (ACGT), Department of Biochemistry, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, 0002, South Africa. plaw@tuks.co.za

ABSTRACT

Background: Microarray 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.

Description: MADIBA (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.

Conclusion: MADIBA 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.

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A schematic representation of the flow of data through MADIBA. After a microarray experiment, data are normalised and then clustered, since it is hypothesised that the genes in a cluster have common biological implications. A cluster of genes is submitted to MADIBA, either as nucleotide sequences, or gene identifiers. This list of genes can then be subjected to five analysis modules – Gene Ontology Analysis, Metabolic Pathways Analysis, Transcription Regulation Analysis, Chromosomal Localisation Analysis and an Organism Specific Analysis. Also shown are the data that are required by each of the analysis modules. The results from the analyses can be exported as a PDF file, or as plain text.
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Figure 1: A schematic representation of the flow of data through MADIBA. After a microarray experiment, data are normalised and then clustered, since it is hypothesised that the genes in a cluster have common biological implications. A cluster of genes is submitted to MADIBA, either as nucleotide sequences, or gene identifiers. This list of genes can then be subjected to five analysis modules – Gene Ontology Analysis, Metabolic Pathways Analysis, Transcription Regulation Analysis, Chromosomal Localisation Analysis and an Organism Specific Analysis. Also shown are the data that are required by each of the analysis modules. The results from the analyses can be exported as a PDF file, or as plain text.

Mentions: The gene list is stored for one week on the server, and a unique identifier is provided to allow users to later access and retrieve their data. As each analysis module is accessed, the gene list is used to retrieve the necessary information required by that module from the database. Figure 1 illustrates the architecture and basic data flow of an analysis in MADIBA as described in the next section.


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

Law PJ, Claudel-Renard C, Joubert F, Louw AI, Berger DK - BMC Genomics (2008)

A schematic representation of the flow of data through MADIBA. After a microarray experiment, data are normalised and then clustered, since it is hypothesised that the genes in a cluster have common biological implications. A cluster of genes is submitted to MADIBA, either as nucleotide sequences, or gene identifiers. This list of genes can then be subjected to five analysis modules – Gene Ontology Analysis, Metabolic Pathways Analysis, Transcription Regulation Analysis, Chromosomal Localisation Analysis and an Organism Specific Analysis. Also shown are the data that are required by each of the analysis modules. The results from the analyses can be exported as a PDF file, or as plain text.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2277412&req=5

Figure 1: A schematic representation of the flow of data through MADIBA. After a microarray experiment, data are normalised and then clustered, since it is hypothesised that the genes in a cluster have common biological implications. A cluster of genes is submitted to MADIBA, either as nucleotide sequences, or gene identifiers. This list of genes can then be subjected to five analysis modules – Gene Ontology Analysis, Metabolic Pathways Analysis, Transcription Regulation Analysis, Chromosomal Localisation Analysis and an Organism Specific Analysis. Also shown are the data that are required by each of the analysis modules. The results from the analyses can be exported as a PDF file, or as plain text.
Mentions: The gene list is stored for one week on the server, and a unique identifier is provided to allow users to later access and retrieve their data. As each analysis module is accessed, the gene list is used to retrieve the necessary information required by that module from the database. Figure 1 illustrates the architecture and basic data flow of an analysis in MADIBA as described in the next section.

Bottom Line: 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.MADIBA 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.In most of the cases, the same conclusions found by the authors were quickly and easily obtained after analysing the gene clusters with MADIBA.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics and Computational Biology Unit, African Centre for Gene Technologies (ACGT), Department of Biochemistry, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, 0002, South Africa. plaw@tuks.co.za

ABSTRACT

Background: Microarray 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.

Description: MADIBA (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.

Conclusion: MADIBA 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.

Show MeSH