Clustering genome data based on approximate matching
by Nagamma Patil; Durga Toshniwal; Kumkum Garg
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 5, No. 2, 2013

Abstract: Genome data mining and knowledge extraction is an important problem in bioinformatics. Some research work has been done for genome identification based on exact matching of n-grams. However, in most real world biological problems, it may not be feasible to have an exact match, so approximate matching may be desired. The problem in using n-grams is that the number of features (4n for DNA sequence and 20n for protein sequence) increases with increase in n. In this paper, we propose an approach for genome data clustering based on approximate matching. Generally genome sequences are very long, so we sample the data into 10,000 base pairs. Given a database of genome sequences, our proposed work includes extraction of total number of approximate matching patterns to a query with given fault tolerance and then using this total number of matches for clustering. Candidate length is varied so as to allow both positive and negative tolerance and hence the number of features used for clustering also varies. K-means, fuzzy C-means (FCM) and possibilistic C-means (PCM) algorithms are used for clustering of the genome data. Experimental results obtained by varying tolerance from 20% to 70% are reported. It has been observed that as tolerance increases, number of genome samples that are correctly clustered also increases and our proposed approach outperforms existing n-gram frequency based approach. Two different genome datasets are used to verify the proposed method namely yeast, E. coli and Drosophila, mouse.

Online publication date: Fri, 28-Feb-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email