MetAlgNet :Metabolic pathway network reconstruction from algae genome annotation data.

Abstract: Post-genomic molecular biology embodies high-throughput experimental techniques and hence it is a data-rich field. The goal of development of this tool is to utilize free available biological data of green algae in order to produce new metabolic pathway knowledge and to aid mining of newly generated data. The variety of biological sequence and functional information are stored in different online database, so getting annotation information of genome from different database is challenging task for reconstruction of pathway.Here we apply data integration approach to provide rich representation that enables pathway names based text mining of biological data in terms of integrated networks and conceptual spaces. The publicly available green algae genome annotated data can be used to aid mining of important biological enzymes in metabolic networks. We developed an integrative bioinformatics approach that utilizes publicly available knowledge of enzyme-metabolites interactions, network topological analysis like betweenness, closeness and degree for assigning node importance with quantitative values. The application of our software is revealed importance of role of potential enzymes in biological functions in view of network centrality values, which were calculated by various algorithms. The results provided in this work indicate that integration of heterogeneous biological data facilitates advanced mining of data to create metabolic pathway networks. The methods can be applied for gaining insight into functions of enzymes, metabolites and other molecules, as well as for offering interpretation of functional evolution of metabolites with help of topological analysis and reconstruction of phylogenetic tree from sequence data.