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Ultra-fast sequence clustering from similarity networks with SiLiX

Abstract : Background: The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time. Results: We present the software package SiLiX that implements a novel method which reconsiders single linkage clustering with a graph theoretical approach. A parallel version of the algorithms is also presented. As a demonstration of the ability of our software, we clustered more than 3 millions sequences from about 2 billion BLAST hits in 7 minutes, with a high clustering quality, both in terms of sensitivity and specificity. Conclusions: Comparing state-of-the-art software, SiLiX presents the best up-to-date capabilities to face the problem of clustering large collections of sequences. SiLiX is freely available at
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Contributor : Stéphane Delmotte Connect in order to contact the contributor
Submitted on : Wednesday, May 16, 2012 - 2:34:54 PM
Last modification on : Monday, April 11, 2022 - 5:00:12 PM

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V. Miele, Simon Penel, L. Duret. Ultra-fast sequence clustering from similarity networks with SiLiX. BMC Bioinformatics, BioMed Central, 2011, 12(116), pp.1-9. ⟨10.1186/1471-2105-12-116⟩. ⟨hal-00698365⟩



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