Identifying novel molecular insights from life science massive data

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BackgroundBiological datasets have grown exponentially in size and complexity making them part of the world of big data. Hundreds of repositories organize biological research and make results available to biologists to be subjected to additional analyses. At the moment, the data from these repositories cannot be optimally exploited because of incompatible data types and of the separation of data into specialized repositories that cannot be combined. Therefore, navigation through many databases is required to perform searches and researchers do not always have the expertise or computational knowledge to integrate the different sources of data.Research ProblemGene expression experiments obtained from high throughput technologies (i.e. sequencing and microarray) are widely used to obtain gene signatures to define a particular phenotype. To be valuable, gene signatures are required to provide information on specific molecular process involved and a larger system-view context. In addition, these signatures need to go beyond the simple integration of datasets or databases, to provide valid and accurate insight on biological function and pathways of new data. To answer these needs, we have developed Kibio.science, a new portal that combines Elasticsearch, a search engine, and Kibi, a web visualization plugin, to create the relational searches between biological databases. With this proposal, we will implement analytical tools to take advantage of high-throughput data, clinical and pathological information.ImpactsKibio.science is a centralized platform that make it easy to visualize, interpret and perform biological analysis of multiple data types. The major contribution of this research program will be to provide analytical tools that facilitate functional interpretation and visualization of experimental data (e.g. gene signatures). We will create new statistical models to measure quality and accuracy of gene signatures in addition to provide statistics on co-mentions/correlations of biomolecules. Furthermore, to enhance the analytical power of kibio.science, specialized dynamic visualizations dedicated to clinical or research purposes will be implemented. Combined with new analytic tools, this will provide ways of translating massive life science data into actionable knowledge.