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Journal Articles Journal of Computational Biology Year : 2019

A De Novo Robust Clustering Approach for Amplicon-Based Sequence Data

Abstract

When analyzing microbial communities, an active and computational challenge concerns the categorization of 16S rRNA gene sequences into operational taxonomic units (OTUs). Established clustering tools use a one pass algorithm in order to tackle high numbers of gene sequences and produce OTUs in reasonable time. However, all of the current tools are based on a crisp clustering approach, where a gene sequence is assigned to one cluster. The weak quality of the output compared to more complex clustering algorithms, forces the user to post-process the obtained OTUs. Providing a membership degree when assigning a gene sequence to an OTU, will help the user during the post-processing task. Moreover it is possible to use this membership degree to automatically evaluate the quality of the obtained OTUs. So the goal of this work is to propose a new clustering approach that takes into account uncertainty when producing OTUs, and improves both the quality and the presentation of the OTUs results.
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Dates and versions

hal-02359826 , version 1 (27-01-2017)
hal-02359826 , version 2 (08-05-2017)
hal-02359826 , version 3 (31-07-2023)

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Alexandre Bazin, Didier Debroas, Engelbert Mephu Nguifo. A De Novo Robust Clustering Approach for Amplicon-Based Sequence Data. Journal of Computational Biology, 2019, 26 (6), pp.618-624. ⟨10.1089/cmb.2018.0170⟩. ⟨hal-02359826v3⟩
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