UPARSE: highly accurate OTU sequences from microbial amplicon reads

By Robert C. Edgar. Nature Methods, 2013

14,916 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a7bc5af47d55bb1bc618574597946cf0691c9d2d

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Citations in the corpus

Soil microbial communities in cucumber monoculture and rotation systems and their feedback effects on cucumber seedling growth (2017, methodology): “Sequences were trimmed to 220 bp using fastq_filter in UPARSE (Edgar 2013).”
Wild Mouse Gut Microbiota Promotes Host Fitness and Improves Disease Resistance (2017, neutral): “0.1090 Edgar, 2013 http://www.drive5.com/usearch/ UPARSE Edgar, 2013 http://drive5.com/usearch/manual/ uparse_pipeline.html ATIMA (Agile Toolkit for Incisive Microbial Analyses) Alkek Center for Metagenomics and Microbiome Research, Baylor College http://atima.jplab.net ImageJ v2.”
A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing (2016, methodology): “The UPARSE pipeline (Edgar 2013) was used to merge the demultiplexed 165 sequences, conduct quality filtering, create a de novo database, and map the sequences to this 166 database.”
Next Generation Sequencing-Based Investigation of Potential Patient-to-Patient Hepatitis C Virus Transmission during Hemodialytic Treatment (2016, methodology): “A data cleaning strategy was performed according to published methods [16].”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, methodology): “Sequences that surpass a dissimilarity threshold (bit-score) are progressively placed on higher taxonomy levels.”
LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, methodology): “After obtaining the raw sequencing reads, the reads can be clustered into operational taxonomic units (OTUs), denoised into amplicon sequence variants (ASVs), or mapped to a microbial reference database (taxa) using existing bioinformatics pipelines such as UPARSE, DADA2, and MetaPhlAn [3–5].”
Combined amendment of immobilizers and the plant growth-promoting strain Burkholderia phytofirmans PsJN favours plant growth and reduces heavy metal uptake (2015, methodology): “Denoised sequences were then de-replicated and sorted in USEARCH v7 (Edgar, 2013).”
Changing soil characteristics alter the arbuscular mycorrhizal fungi communities of Arabica coffee (Coffea arabica) in Ethiopia across a management intensity gradient (2015, methodology): “Sequences obtained from the 454 pyrosequencing run were clustered into operational taxonomix units (OTUs), determined at a 3% sequence dissimilarity cutoff, using the UPARSE algorithm, following the recommended pipeline described by Edgar (2013).”
Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming (2018, methodology): “Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads.”
Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots (2018, positive): “We clustered the quality sequences into operational taxonomic units (OTUs) at ≥ 98% sequence similarity with the UPARSE series of scripts [73].”
Soil microbial community and network changes after long-term use of plastic mulch and nitrogen fertilization on semiarid farmland (2021, neutral): “0.1001 software (Edgar, 2013).”