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.
