Reference : VizBin - an application for reference-independent visualization and human-augmented b...
Scientific journals : Article
Life sciences : Microbiology
http://hdl.handle.net/10993/20140
VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data
English
Laczny, Cédric mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Sternal, Tomasz mailto [Poznan University of Technology > Institute of Computing Science]
Plugaru, Valentin mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Gawron, Piotr mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Atashpendar, Arash mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Margossian, Houry Hera mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Coronado, Sergio mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
van der Maaten, Laurens mailto [Delft University of Technology > Pattern Recognition and Bioinformatics Group]
Vlassis, Nikos [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Wilmes, Paul mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
20-Jan-2015
Microbiome
BioMed Central
Yes (verified by ORBilu)
International
2049-2618
London
United Kingdom
[en] Metagenomics ; Machine learning ; Binning ; Visualization
[en] Background
Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge.
Results
We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.
Conclusions
VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.
Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/20140
10.1186/s40168-014-0066-1.
http://www.microbiomejournal.com/content/3/1/1/abstract

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