ArticleNucleic acids research2012
Rapid identification of high-confidence taxonomic assignments for metagenomic data.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 77 citations in OpenAlex.
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- Distinctive roles between rumen epimural and content bacterial communities on beef cattle feed efficiency: A combined analysis.Current research in microbial sciences · 2021Article
- phyloFlash: Rapid Small-Subunit rRNA Profiling and Targeted Assembly from Metagenomes.mSystems · 2020Article
- An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome.Metabolites · 2020Review
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- Cluster oligonucleotide signatures for rapid identification by sequencing.BMC bioinformatics · 2018Article
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- CoreProbe: A Novel Algorithm for Estimating Relative Abundance Based on Metagenomic Reads.Genes · 2018Article
- GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes.Nucleic acids research · 2018Article
- A clinician's guide to microbiome analysis.Nature reviews. Gastroenterology & hepatology · 2017Review
- k-SLAM: accurate and ultra-fast taxonomic classification and gene identification for large metagenomic data sets.Nucleic acids research · 2017Article
- Article
- A Metagenomic Approach to Cyanobacterial Genomics.Frontiers in microbiology · 2017Review
- High-resolution characterization of the human microbiome.Translational research : the journal of laboratory and clinical medicine · 2017Review
- Metabolic network-guided binning of metagenomic sequence fragments.Bioinformatics (Oxford, England) · 2016Article
- Evaluation of shotgun metagenomics sequence classification methods using in silico and in vitro simulated communities.BMC bioinformatics · 2015Article
- Bayesian mixture analysis for metagenomic community profiling.Bioinformatics (Oxford, England) · 2015Article
- Transfer of energy pathway genes in microbial enhanced biological phosphorus removal communities.BMC genomics · 2015Article
- Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies.Bioinformatics and biology insights · 2015Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the taxonomic lineage of DNA sequences is an important step in metagenomic analysis. Short DNA fragments from next-generation sequencing projects and microbes that lack close relatives in reference sequenced genome databases pose significant problems to taxonomic attribution methods. Our new classification algorithm, RITA (Rapid Identification of Taxonomic Assignments), uses the agreement between composition and homology to accurately classify sequences as short as 50 nt in length by assigning them to different classification groups with varying degrees of confidence. RITA is much faster than the hybrid PhymmBL approach when comparable homology search algorithms are used, and achieves slightly better accuracy than PhymmBL on an artificial metagenome. RITA can also incorporate prior knowledge about taxonomic distributions to increase the accuracy of assignments in data sets with varying degrees of taxonomic novelty, and classified sequences with higher precision than the current best rank-flexible classifier. The accuracy on short reads can be increased by exploiting paired-end information, if available, which we demonstrate on a recently published bovine rumen data set. Finally, we develop a variant of RITA that incorporates accelerated homology search techniques, and generate predictions on a set of human gut metagenomes that were previously assigned to different 'enterotypes'. RITA is freely available in Web server and standalone versions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.