Reference : Alignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction
Scientific journals : Article
Life sciences : Microbiology
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/16311
Alignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction
English
Laczny, Cedric Christian mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Pinel, Nicolás mailto [Institute for Systems Biology, Seattle, Washington, USA]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Wilmes, Paul mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
31-Mar-2014
Scientific Reports
Nature Publishing Group
Yes (verified by ORBilu)
International
2045-2322
London
United Kingdom
[en] metagenomics ; machine learning ; visualization
[en] The visualization of metagenomic data, especially without prior taxonomic identification of reconstructed genomic fragments, is a challenging problem in computational biology. An ideal visualization method should, among others, enable clear distinction of congruent groups of sequences of closely related taxa, be applicable to fragments of lengths typically achievable following assembly, and allow the efficient analysis of the growing amounts of community genomic sequence data. Here, we report a scalable approach for the visualization of metagenomic data that is based on nonlinear dimension reduction via Barnes-Hut Stochastic Neighbor Embedding of centered log-ratio transformed oligonucleotide signatures extracted from assembled genomic sequence fragments. The approach allows for alignment-free assessment of the data-inherent taxonomic structure, and it can potentially facilitate the downstream binning of genomic fragments into uniform clusters reflecting organismal origin. We demonstrate the performance of our approach by visualizing community genomic sequence data from simulated as well as groundwater, human-derived and marine microbial communities.
Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/16311
10.1038/srep04516
http://www.nature.com/srep/2014/140331/srep04516/full/srep04516.html

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