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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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Results from the Gene Ontology module. An analysis of the biological process ontology of the cluster 6 of the Plasmodium data [40] revealed that anaerobic glycolysis was the most significant term. The DAG was reduced to show only the terms that are most relevant to glucose metabolism. The grey ellipses contain the genes that are annotated to the connected GO term and the colour of the GO terms indicates different levels of significance, as indicated by the legend.
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Figure 3: Results from the Gene Ontology module. An analysis of the biological process ontology of the cluster 6 of the Plasmodium data [40] revealed that anaerobic glycolysis was the most significant term. The DAG was reduced to show only the terms that are most relevant to glucose metabolism. The grey ellipses contain the genes that are annotated to the connected GO term and the colour of the GO terms indicates different levels of significance, as indicated by the legend.

Mentions: After applying MADIBA, an improvement in the number of annotated genes is apparent compared with the original results. The mean fraction of known genes by cluster was 37.5% compared with 41% when using MADIBA. The Gene Ontology module automatically allocated annotations to the gene clusters with terms including immune evasion, in cluster 1, and cell invasion in cluster 15. In addition, the genes in cluster 2 were correctly identified as involved in gametogenesis and having over-represented protein kinase cascade activity. The metabolic pathways module successfully showed that six of the nine enzymes in the glycolysis pathway were found in cluster 6, with a p-value of 0.04, as calculated by using Fisher's exact test (Figure 2). This result is further supported by the indication that all the enzymes in the pathway were identified by all three annotation sources, as indicated by the yellow boxes, and by using the GO analysis, it was shown that the anaerobic glycolysis term had a highly significant p-value (Figure 3 and Table 1). Using the module specific for Plasmodium characteristics allowed the identification of genes in cluster 3 as interesting drug or vaccine targets, such as PF10_0303, an ookinete surface antigen.


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)

Results from the Gene Ontology module. An analysis of the biological process ontology of the cluster 6 of the Plasmodium data [40] revealed that anaerobic glycolysis was the most significant term. The DAG was reduced to show only the terms that are most relevant to glucose metabolism. The grey ellipses contain the genes that are annotated to the connected GO term and the colour of the GO terms indicates different levels of significance, as indicated by the legend.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Results from the Gene Ontology module. An analysis of the biological process ontology of the cluster 6 of the Plasmodium data [40] revealed that anaerobic glycolysis was the most significant term. The DAG was reduced to show only the terms that are most relevant to glucose metabolism. The grey ellipses contain the genes that are annotated to the connected GO term and the colour of the GO terms indicates different levels of significance, as indicated by the legend.
Mentions: After applying MADIBA, an improvement in the number of annotated genes is apparent compared with the original results. The mean fraction of known genes by cluster was 37.5% compared with 41% when using MADIBA. The Gene Ontology module automatically allocated annotations to the gene clusters with terms including immune evasion, in cluster 1, and cell invasion in cluster 15. In addition, the genes in cluster 2 were correctly identified as involved in gametogenesis and having over-represented protein kinase cascade activity. The metabolic pathways module successfully showed that six of the nine enzymes in the glycolysis pathway were found in cluster 6, with a p-value of 0.04, as calculated by using Fisher's exact test (Figure 2). This result is further supported by the indication that all the enzymes in the pathway were identified by all three annotation sources, as indicated by the yellow boxes, and by using the GO analysis, it was shown that the anaerobic glycolysis term had a highly significant p-value (Figure 3 and Table 1). Using the module specific for Plasmodium characteristics allowed the identification of genes in cluster 3 as interesting drug or vaccine targets, such as PF10_0303, an ookinete surface antigen.

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