SpIci plus a clustering and visualization tool for large biological networks

Abstract

Biological networks representation and analysis have become an everyday tool for many biologists, as these interaction graphs makes it easier to analyze and understand interactions between individuals, disease transmission, DNA sequence similarities, metabolic pathways, protein interactions, pathways, regulatory cascades and gene expression. However, given the size and complexity of interactive datasets, extracting meaningful information from interaction networks can be a daunting task. Although previously there were some clustering algorithms, they did not present any clustering and visualizing tool that could be used by bioinformaticians making the e ective usefulness of their work limited. In this project, we have integrated seven prominent clustering algorithms, namely, SPICi, SPICi1 +, SPICi2 +, SPICi12 + , MGclus, ClusterOne and WPNCA in a web tool named SPICiPLUS. Each visualization tool has speci c features and thus the tools vary in how they address the outlined challenges. In summary despite that there are a lot of tools available for visualization, choosing tools, namely, Alchemy and Vis in order to develop SPICiPLUS required deep study and analysis. In this project we have developed SPICiPLUS, an open source so ware project for visualizing clustered graph of large biological networks available for humans and model organisms into a uni- ed conceptual framework, by using PHP, C++. In SPICiPLUS, We have incorporated di erent biologically useful features (e.g., zoom-in and zoom-out features so that a part of a large biological network can be visualized convincingly) and also provided a GUI that can present a comparison of the seven clustering algorithms considering di erent standards. We anticipate that apart from being useful to bioinformaticians, this will present new opportunities of designing heuristics for the computer scientist that may evaluate biological networks in a be er way.