Featured Research

Semantic Modeling of Biological Sequences and Bioinformatics

Abstract

Genomic research requires extensive usage of a number of heterogeneous data sources including nucleotides, protein sequences, 3-D protein structures, taxonomies, and research publication databases. The objective of such research is to reveal as much biological knowledge as possible about the properties and functions of biological entities including DNA sequences and proteins. Ultimately, the goal is explore connections among all the data, so that the derived knowledge can be used to untangle biological and evolutionary myths and to improve human lives. Currently it is very difficult to connect all of these data sources seamlessly unless all the data of the same type is transformed into a common format with an ID connecting all of them. The state-of-the-art facilities for searching these data are primarily text-based, requiring users to specify keywords using which they search through each individual data source and return results from each source. The user is then required to create the connections between the results. This is a major problem because this human-computer interaction consumes a large portion of a biologist's time; moreover, researchers do not always know the connections. Often they have to discover the connections using scientific analyses techniques. To solve this problem we propose a semantics-based mechanism for automatically linking and connecting the various data sources and to discover new connections. Our approach is based on a model that explicitly captures the semantics of various heterogeneous data sources and makes them available for searching. Using new operators based on these semantics we demonstrate how connections can be made automatically between the various biological data sources.

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