Abstract
With recent improvements in sequencing throughput, huge numbers of genomes can now be sequenced rapidly. However, data analysis methods have not kept up, making it difficult to process the vast amounts of sequence data now available. Thus, there is a strong demand for a faster sequence clustering algorithm. We developed a new fast DNA sequence clustering method called LCS-HIT based on the popular CD-HIT program. The proposed method employs a novel filtering technique based on the longest common subsequence to identify similar sequence pairs. This filtering technique affords a considerable speed-up over CD-HIT without loss of sensitivity. For a dataset with two million DNA sequences, our method was about 7.1, 4.4 and 2.5 times faster than CD-HIT for 100, 150, and 400 bases, respectively.
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© 2012 Springer-Verlag Berlin Heidelberg
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Namiki, Y., Ishida, T., Akiyama, Y. (2012). Fast DNA Sequence Clustering Based on Longest Common Subsequence. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_66
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DOI: https://doi.org/10.1007/978-3-642-31837-5_66
Publisher Name: Springer, Berlin, Heidelberg
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