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Ramazzotti M, Berná L, Donati C, et al. riboFrame: An Improved Method for Microbial Taxonomy Profiling from Non-Targeted Metagenomics. Front Genet. 2015;6:329doi: 10.3389/fgene.2015.00329.
Ramazzotti, M., Berná, L., Donati, C., & Cavalieri, D. (2015). riboFrame: An Improved Method for Microbial Taxonomy Profiling from Non-Targeted Metagenomics. Frontiers in genetics, 6329. https://doi.org/10.3389/fgene.2015.00329
Ramazzotti, Matteo, et al. "riboFrame: An Improved Method for Microbial Taxonomy Profiling from Non-Targeted Metagenomics." Frontiers in genetics vol. 6 (2015): 329. doi: https://doi.org/10.3389/fgene.2015.00329
Ramazzotti M, Berná L, Donati C, Cavalieri D. riboFrame: An Improved Method for Microbial Taxonomy Profiling from Non-Targeted Metagenomics. Front Genet. 2015 Nov 17;6:329. doi: 10.3389/fgene.2015.00329. eCollection 2015. PMID: 26635865; PMCID: PMC4646959.
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Front Genet. 2015 Nov 17;6:329. doi: 10.3389/fgene.2015.00329. eCollection 2015.

riboFrame: An Improved Method for Microbial Taxonomy Profiling from Non-Targeted Metagenomics.

Frontiers in genetics

Matteo Ramazzotti, Luisa Berná, Claudio Donati, Duccio Cavalieri

Affiliations

  1. Dipartimento di Scienze Biomediche Sperimentali e Cliniche, Università degli Studi di Firenze Firenze, Italy.
  2. Unidad de Biología Molecular, Institut Pasteur de Montevideo Montevideo, Uruguay.
  3. Centre for Research and Innovation, Fondazione Edmund Mach San Michele all'Adige, Italy.

PMID: 26635865 PMCID: PMC4646959 DOI: 10.3389/fgene.2015.00329

Abstract

Non-targeted metagenomics offers the unprecedented possibility of simultaneously investigate the microbial profile and the genetic capabilities of a sample by a direct analysis of its entire DNA content. The assessment of the microbial taxonomic composition is frequently obtained by mapping reads to genomic databases that, although growing, are still limited and biased. Here we present riboFrame, a novel procedure for microbial profiling based on the identification and classification of 16S rDNA sequences in non-targeted metagenomics datasets. Reads overlapping the 16S rDNA genes are identified using Hidden Markov Models and a taxonomic assignment is obtained by naïve Bayesian classification. All reads identified as ribosomal are coherently positioned in the 16S rDNA gene, allowing the use of the topology of the gene (i.e., the secondary structure and the location of variable regions) to guide the abundance analysis. We tested and verified the effectiveness of our method on simulated ribosomal data, on simulated metagenomes and on a real dataset. riboFrame exploits the taxonomic potentialities of the 16S rDNA gene in the context of non-targeted metagenomics, giving an accurate perspective on the microbial profile in metagenomic samples.

Keywords: 16S rDNA gene; community profiling; metagenomics; non-targeted approach; short reads; variable region

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