Alignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction

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 rel...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2014-03, Vol.4 (1), p.4516-4516, Article 4516
Main Authors: Laczny, Cedric C., Pinel, Nicolás, Vlassis, Nikos, Wilmes, Paul
Format: Article
Language:English
Subjects:
45/23
631/114/1305
631/326/2565/2142
Algorithms
BASIC BIOLOGICAL SCIENCES
Computational Biology - methods
Databases, Genetic
Genomics - methods
Humanities and Social Sciences
Humans
Metagenome - genetics
Metagenomics - methods
multidisciplinary
Science
Science & Technology - Other Topics
Sequence Analysis, DNA - methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep04516