Motivation: Species identification and quantification are common tasks in metagenomics and pathogen detection studies. The most recent techniques are built on mapping the sequenced reads against a reference database (e.g. whole genomes, marker genes, proteins) followed by application-dependent analysis steps. Although these methods have been proven to be useful in many scenarios, there is still room for improvement in species and strain level detection, mainly for low abundant organisms.
Results: We propose a new method: DUDes, a reference-based taxonomic profiler that introduces a novel top-down approach to analyze metagenomic Next-generation sequencing (NGS) samples. Rather than predicting an organism presence in the sample based only on relative abundances, DUDes first identifies possible candidates by comparing the strength of the read mapping in each node of the taxonomic tree in an iterative manner. Instead of using the lowest common ancestor we propose a new approach: the deepest uncommon descendent. We showed in experiments that DUDes works for single and multiple organisms and can identify low abundant taxonomic groups with high precision.
Availability and implementation: DUDes is open source and it is available at http://sf.net/p/dudes
Supplementary information: Supplementary data are available at Bioinformatics online.
Contact: renardB@rki.de.
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