Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

By Patrick D. Schloss, Sarah L. Westcott, Thomas Ryabin, J. Hall, Martin Hartmann, E. Hollister, Ryan A. Lesniewski, B. Oakley, Donovan H. Parks, Courtney J. Robinson and 5 more. Applied and Environmental Microbiology, 2009

19,675 citations recorded by the source corpus.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7fae7eb289138204e8a60d1c18b831627df233f1

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Citations in the corpus

Rhizosphere microbiome assemblage is affected by plant development (2013, methodology): “Sequences were also assigned to phylotypes using the phylotype command in Mothur.”
Assembling metagenomes, one community at a time (2017, background): “The emergence of two key platforms for analysing amplicon sequencing data, mothur [46] and QIIME [47], has allowed for methodological standards to be set, which enables robust comparisons between studies [48].”
Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi (2015, positive): “3 (Schloss et al. 2009) with the following options: average quality over 15 nucleotide window >30, no ambiguities allowed, and homopolymer length unrestricted.”
Identifying personal microbiomes using metagenomic codes (2015, methodology): “OTU abundances were calculated from HMP 16S sequencing data using Mothur (32).”
Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research (2017, background): “…bioinformatics front, there have been tremendous efforts to develop systems to process and analyze microbiome data, including QIIME (13) and mothur (14), primarily for 16S rRNA data, while metagenomic data analysis is still often pieced together using various individual bioinformatic methods (15).”
Commensal microbiota affects ischemic stroke outcome by regulating intestinal γδT cells (2016, positive): “Sequences were analyzed using the mothur pipeline, version 1.33.3 58 as described previously 59 .”
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible (2013, methodology): “Some heuristics for filtering low-abundance OTUs are already described in the documentation of various microbiome analysis workflows [29,30], and in many cases these can be classified as forms of Figure 5.”
Fungal diversity in deep-sea sediments revealed by culture-dependent and culture-independent approaches (2012, methodology): “Clones were grouped into operational taxonomic units (OTUs) by using sequence similarity cut-off value of 98% (O’Brien et al. 2005) by using MOTHUR software version 1.4.1 (Schloss et al. 2009).”