NDLI: Massive fungal biodiversity data re-annotation with multi-level clustering

Content Provider	PubMed Central
Author	Vu, Duong Szöke, Szániszló Christian, Wiwie Baumbach, Jan Cardinali, Gianluigi Röttger, Richard Robert, Vincent
Copyright Year	2014
Abstract	With the availability of newer and cheaper sequencing methods, genomic data are being generated at an increasingly fast pace. In spite of the high degree of complexity of currently available search routines, the massive number of sequences available virtually prohibits quick and correct identification of large groups of sequences sharing common traits. Hence, there is a need for clustering tools for automatic knowledge extraction enabling the curation of large-scale databases. Current sophisticated approaches on sequence clustering are based on pairwise similarity matrices. This is impractical for databases of hundreds of thousands of sequences as such a similarity matrix alone would exceed the available memory. In this paper, a new approach called MultiLevel Clustering (MLC) is proposed which avoids a majority of sequence comparisons, and therefore, significantly reduces the total runtime for clustering. An implementation of the algorithm allowed clustering of all 344,239 ITS (Internal Transcribed Spacer) fungal sequences from GenBank utilizing only a normal desktop computer within 22 CPU-hours whereas the greedy clustering method took up to 242 CPU-hours.
Related Links	http://dx.doi.org/10.1038/srep06837
Starting Page	6837
File Format	PDF
ISSN	20452322
e-ISSN	20452322
Journal	Scientific Reports
Volume Number	4
Language	English
Publisher	Nature Publishing Group
Publisher Date	2014-10-01
Access Restriction	Open
Rights Holder	Nature Publishing Group
Subject Keyword	Science and technology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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