Abstract
Objective:With the over saturating growth of biological sequence databases, handling of these amounts of data has increasingly become a problem. Clustering has become one of the principal research objectives in structural and functional genomics. However, exact clustering algorithms, such as partitioned and hierarchical clustering, scale relatively poorly in terms of run time and memory usage with large sets of sequences.Methods:From these performance limits, heuristic optimizations such as Cuckoo Search Algorithm with genetic operators (ICSA) algorithm have been implemented in distributed computing environment. The proposed ICSA, a global optimized algorithm that can cluster large numbers of protein sequences by running on distributed computing hardware.Results:It allocates both memory and computing resources efficiently. Compare with the latest research results, our method requires only 15% of the execution time and obtains even higher quality information of protein sequence.Conclusion:From the experimental analysis, We noticed that the cluster of large protein sequence data sets using ICSA technique instead of only alignment methods reduce extremely the execution time and improve the efficiency of this important task in molecular biology. Moreover, the new era of proteomics is providing us with extensive knowledge of mutations and other alterations in cancer study.
Highlights
Widely adopted paradigm in cancer diagnosis and treatment is that early cancer detection which increases likelihood of survival (Zhu et al, 2011)
From the experimental analysis, We noticed that the cluster of large protein sequence data sets using Improved Cuckoo Search Algorithm (ICSA) technique instead of only alignment methods reduce extremely the execution time and improve the efficiency of this important task in molecular biology
An experiment conducted on large-scale protein data base to show the success of the novel proposed ICSA algorithm
Summary
Widely adopted paradigm in cancer diagnosis and treatment is that early cancer detection which increases likelihood of survival (Zhu et al, 2011). Protein sequence analysis gives big chance for diagnosing, stratifying, and monitoring disease. This analysis must meet certain needs, in order to be clinically useful. Schloss et al, (2009) developed software package that allows users to use a single piece of software to analyze community sequence data This method provides to user to screen, trim, assign sequences; operational taxonomic units. The existing implementations such as HPC-CLUST (Matias and Mering, 2014), CD-HIT (Li and Godzik, 2006), MOTHUR (Schloss et al, 2009), ESPRIT (Sun et al, 2009) or RDP online clustering (Cole et al, 2009), all struggle with large sets of sequences
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