IP33: The Dynamic Impact Approach as a web-based platform for analysis of time-course or multiple treatments omics datasets.
|Title||IP33: The Dynamic Impact Approach as a web-based platform for analysis of time-course or multiple treatments omics datasets.|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Bionaz M, Nguyen A|
|Conference Name||International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)|
|Publisher||CEUR-ws.org Volume 1747|
|Conference Location||Corvallis, Oregon, USA|
|Keywords||co-regulation, Dynamic Impact Approach, gene ontology, ontology, pathways|
Life is dynamic. Therefore, a true understanding of processes responsible for maintaining life requires a dynamic approach. Time-course experiments using omics approaches allow to capture such dynamism; however, dataset from time-course experiments, especially if considering multiple treatments, are challenging to analyze statistically. Even more challenging is to unravel the meaning of the changes observed and capture the underline significant biological changes. Bioinformatic tools for the analysis of time-course experiments are scant and many use the enrichment analysis approach. This approach has an inherent incapacity of analyzing time-course and/or multiple-treatment experiments without reducing the dataset to clusters, such as genes with a common patterns (i.e., co-regulated). In order to overcome the limitation of enrichment analysis tools a Dynamic Impact Approach (DIA) tool was developed. DIA uses the statistical significance (i.e., P-value) and the expression ratio of each comparison to calculate an Impact and a Direction of the Impact values for each biological term (i.e., Gene Ontology, pathway) in each condition. The Impact captures the overall effect of the condition studied on the biological term, while the Direction of the Impact captures the dynamism of the effect (i.e., overall activated or inhibited). The reliability of DIA to provide biological insights compared to enrichment approach tools has been demonstrated in several large transcriptomics studies. DIA can analyze any database available; however, it is now capable to analyze KEGG pathways, Gene Ontology terms, and up-stream regulators. Among the major advantage of DIA compared to enrichment approach tools, is the simplicity in providing highly graphical outputs easy to interpret and to integrate. A beta-version of a DIA web-based tool was recently launch and it is available at http://18.104.22.168:3838/dia/.