An In Silico Approach to Cluster CAM Kinase Protein Sequences
U.S.N Murty*, Amit Kumar Banerjee, Neelima Arora
Bioinformatics Group, Biology Division, Indian Institute of Chemical Technology, Hyderabad-500607, A.P., India
- *Corresponding Author:
- Dr. U.S.N Murty
Deputy Director/ Scientist “F” Head, Biology Division,
Indian Institute of Chemical Technology
Hyderabad- 500007, India,
Tel : +914027193134,
Fax : +91 40 27193227,
Email : [email protected]
Received Date: December 12, 2008; Accepted Date: February 20, 2009; Published Date: February 20, 2009
Citation: Murty USN, Amit KB, Neelima A (2009) An In Silico Approach to Cluster CAM Kinase Protein Sequences. J Proteomics Bioinform 2: 097-107. doi: 10.4172/jpb.1000066
Copyright: © 2009 Murty USN, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
As we are ushering in new age of data driven world, we face an enormous challenge of deriving information from heaps of data available. The amount of data being generated is overwhelming and this calls for exploring novel and effective methods for clustering and classification of such data. CAM kinase family is known to contain many enzymes involved in important physiological processes. In the present study, 13 important physicochemical parameters were calculated for 56 sequences of CAM kinase family in silico. Self organizing Maps (SOM) were employed for the classifying and clustering similar sequences and visualization of high dimensional data spaces as they are known for their capability to maintain the essence of topological relationships between the features. SOM effectively yielded 4 clusters which were distinct from each other and marked by characteristic features.