ArticleFrontiers in genetics2015
riboFrame: An Improved Method for Microbial Taxonomy Profiling from Non-Targeted Metagenomics.
Article in Frontiers in genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Testing the limits of short-reads metagenomic classifications programs in wastewater treating microbial communities.Scientific reports · 2025Article
- Convolutional neural networks improve fungal classification.Scientific reports · 2020Article
- DAIRYdb: a manually curated reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products.BMC genomics · 2019Article
- Metagenomic Composition Analysis of an Ancient Sequenced Polar Bear Jawbone from Svalbard.Genes · 2018Article
- Deep learning models for bacteria taxonomic classification of metagenomic data.BMC bioinformatics · 2018Article
- Metagenomic Approaches to Investigate the Contribution of the Vineyard Environment to the Quality of Wine Fermentation: Potentials and Difficulties.Frontiers in microbiology · 2018Review
- Environmental drivers of a microbial genomic transition zone in the ocean's interior.Nature microbiology · 2017Article
- Single-virus genomics reveals hidden cosmopolitan and abundant viruses.Nature communications · 2017Article
- RiboTagger: fast and unbiased 16S/18S profiling using whole community shotgun metagenomic or metatranscriptome surveys.BMC bioinformatics · 2016Article
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Authors and funding
4 authors.
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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.
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