This article was accepted into the corpus but its outbound wikilinks were never NER-processed — typical at the deepest BFS hop or when the run's entity cap was reached. No expansion funnel to show.
| Mothur | |
|---|---|
| Name | Mothur |
| Developer | Schloss Lab |
| Released | 2009 |
| Programming language | C++, Perl, R, Python |
| Operating system | Cross-platform |
| Platform | Command-line, R integration |
| Language | English |
| Genre | Bioinformatics, Microbial ecology |
| License | Academic freeware, open-source components |
Mothur Mothur is a software package for microbial ecology and microbial community analysis that provides tools for processing high-throughput sequence data from marker genes. It integrates routines for sequence curation, clustering, taxonomic classification, diversity estimation, and statistical comparison, serving researchers who work with amplicon datasets generated by platforms such as Illumina and 454. The project is associated with academic groups and is widely cited in studies from environmental microbiology to clinical microbiome research.
Mothur offers an integrated suite of tools for analysis of amplicon sequences derived from marker genes like 16S rRNA, 18S rRNA, ITS, and functional genes. It addresses preprocessing steps including quality filtering, chimera detection, and alignment, through downstream analyses including operational taxonomic unit (OTU) clustering, amplicon sequence variant (ASV) workflows, alpha and beta diversity metrics, and statistical tests. The package is used alongside pipelines and resources from sequencing centers, microbial ecology labs, and bioinformatics cores at institutions such as University of Michigan, Harvard University, National Institutes of Health, Wageningen University, and University of California, Davis. Mothur interoperates with reference databases and tools like SILVA (database), Greengenes, RDP (Ribosomal Database Project), BLAST, UCLUST, and statistical environments like R (programming language).
Development began in the late 2000s in response to the increasing volume of amplicon data produced by next-generation sequencing. The project was led by the Schloss Lab at University of Michigan, with contributions from collaborators at institutions including Argonne National Laboratory, Brookhaven National Laboratory, and University of California, San Diego. Early releases integrated methods from established tools used in projects such as the Human Microbiome Project and the Earth Microbiome Project. Over time, development incorporated advances from methods originating in papers by authors at Broad Institute, J. Craig Venter Institute, and academic groups at University of Wisconsin–Madison and University of Texas at Austin. The software evolved to support diverse sequencing platforms and to adapt to methodological shifts such as the emergence of ASV approaches from groups like Callahan et al. and community standards heralded by consortia including International Human Microbiome Standards.
Mothur includes routines for sequence processing (screen.seqs, align.seqs), chimera detection (chimera.vsearch, chimera.uchime), and taxonomy assignment (classify.seqs). It implements clustering algorithms (cluster, cluster.split) and supports both OTU- and ASV-style outputs. Diversity analyses encompass alpha diversity metrics (Shannon, Simpson), beta diversity measures (Bray-Curtis, Jaccard), ordination methods (PCoA, NMDS), and statistical tests such as AMOVA and ANOSIM. The package integrates with taxonomic references like SILVA (database), Greengenes, and RDP (Ribosomal Database Project), and with alignment tools derived from MUSCLE and MAFFT. Visualization support is provided via scripts and interoperability with R (programming language) packages such as phyloseq and vegan. Workflow automation and reproducibility are supported through batch files and shared community SOPs used by groups at European Molecular Biology Laboratory, Max Planck Institute for Biology, and national sequencing facilities like European Nucleotide Archive depositors.
Mothur operates primarily through a command-line interface where users issue named commands with parameters and input files; common commands follow a verb.object pattern (for example, make.file, trim.seqs). Workflows typically begin with make.contigs for paired-end assembly, proceed through screen.seqs and filter.seqs for quality control, employ cluster or deblur-like methods for grouping, and finalize with classify.seqs and dist.shared for diversity calculations. Batch files allow reproducible pipelines used in studies performed at centers such as Wellcome Sanger Institute, Broad Institute, and university sequencing cores. Output formats are compatible with downstream analyses in environments like QIIME and statistical tools used at institutions such as Columbia University and University of Pennsylvania.
Mothur has been applied across diverse ecosystems: human-associated microbiomes in clinical cohorts at Mayo Clinic and Johns Hopkins University, environmental surveys from Antarctic Peninsula expeditions, agricultural soil microbiomes studied at Agricultural Research Service sites, marine microbiology projects collaborating with Woods Hole Oceanographic Institution, and wastewater treatment analyses at municipal utilities. It underpins studies in microbial biogeography, dysbiosis in disease cohorts investigated at Stanford University School of Medicine, antibiotic impact assessments at Imperial College London, and diet–microbiome interactions researched at Harvard T.H. Chan School of Public Health.
Performance comparisons often reference benchmarking studies from groups at National Center for Biotechnology Information and computational biology labs at University of California, Berkeley and ETH Zurich. Validation typically involves mock communities, spike-in controls, and simulated datasets used by consortia such as the International Human Microbiome Consortium to assess accuracy in diversity estimation, taxonomic assignment, and chimera removal. Computational efficiency depends on dataset size, clustering method, and available hardware; Mothur scales on multicore servers and has been employed on high-performance computing infrastructure at XSEDE and national supercomputing centers like NCAR.
Mothur is distributed with an academic-friendly open-source model developed by academic teams and supported by community contributors from universities and research institutes worldwide. The project maintains documentation, tutorials, and course materials used in workshops at Cold Spring Harbor Laboratory, Gordon Research Conferences, and university short courses. Community support is provided via mailing lists, forums, and collaborative repositories involving contributors affiliated with organizations such as European Molecular Biology Organization, American Society for Microbiology, and national research councils across multiple countries.
Category:Bioinformatics software