Visualization and Analytics of Biological Data by Using Different Tools and Techniques

The importance of graph analytics cannot be undermined. It has always been a question for the researcher that how to deal with dense graphs. The visual graph analytics is one of the best sources for creating its remarkable, distinct impact in the field of data science. The graph analytics and big data has fascinated a wide range of attention from the researchers and scientist from all over the world. By using the most advanced tools for the graph, the analytics can lead most useful and productive results in various domains which include life sciences, business, computer sciences, engineering and so on. Biological data can be represented in interpretable form when exposed to graph analytic tools, which may lead to meaningful insights. This paper is aimed at the visualization of the graph with two different techniques. Various procedures were used in this research such as the collection of datasets from heterogeneous biological data sources, data integration, and formation of the new dataset (MYBIOGRID). Designing queries in Neo4j using Cypher Query Language to visualize MYBIOGRID and to determine the relationship using the property graph model. In the next step the uploading data to CIRCOS is performed and visualization of motif similarity is done. The result from this study indicates that visualization of similarity matrix of repetitive patterns thus representing the most similar and least similar patterns in the sequence. Graph databases play a vital role in graph analytics but in memory storage makes analysis very time consuming if the massive amount of data sets is to be processed. Each tool has its specific parameters, which make it a good candidate for analysis and comparison.