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Qiime
Qiime is a bioinformatics software package widely used for analysis of microbial communities and environmental sequencing projects. It is associated with academic institutions and projects that involve high-throughput sequencing platforms and ecological studies, and is cited in publications involving molecular ecology, metagenomics, and microbiome research. The software has been utilized in collaborations among researchers affiliated with institutions such as University of California, San Diego, Harvard University, Broad Institute, Lawrence Berkeley National Laboratory, and agencies including National Institutes of Health and National Science Foundation.
Qiime grew from efforts to analyze marker gene surveys generated on platforms like Illumina, Roche 454, and Ion Torrent. It addresses experimental workflows common to studies connected to projects like the Human Microbiome Project and the Earth Microbiome Project. Qiime has been mentioned in papers published in journals such as Nature, Science, PLoS ONE, The ISME Journal, and Nature Methods. The project intersects with datasets originating from consortia linked to MetaSUB Consortium, Parkinson's Disease microbiome investigations, and environmental surveys in regions studied by groups at Smithsonian Institution and Scripps Institution of Oceanography.
Qiime integrates modules for quality control, taxonomic assignment, diversity analysis, and visualization used in studies by researchers at Massachusetts Institute of Technology, Stanford University, Yale University, University of Oxford, and University of Cambridge. Components include pipelines compatible with tools developed at European Bioinformatics Institute and algorithms originating from teams at Johns Hopkins University and University of Michigan. The software produces outputs that can be interpreted alongside resources from GenBank, SILVA database, Greengenes, and UNITE. Qiime’s visualization capabilities have been compared to plots and interfaces used by projects at Google AI teams, labs at Microsoft Research, and groups contributing to Galaxy Project.
Typical workflows in Qiime reflect experimental designs employed by investigators associated with National Center for Biotechnology Information, Wellcome Trust Sanger Institute, and environmental groups like NOAA. Users process sequence reads, perform chimera checking using methods developed in labs such as University of Colorado Boulder and Max Planck Institute for Marine Microbiology, and cluster sequences referencing taxonomies curated by organizations including European Molecular Biology Laboratory and California Academy of Sciences. Qiime workflows are taught in workshops by instructors from Cold Spring Harbor Laboratory, EMBL-EBI Training, and training programs at Carnegie Institution for Science.
Qiime incorporates algorithms and external tools that originated in research labs such as those at University of California, Berkeley, University of Washington, Princeton University, and Georgia Institute of Technology. Examples include sequence denoising algorithms comparable to methods from DADA2 developers, clustering approaches related to work at University of Illinois Urbana-Champaign, and alignment tools paralleling those from University of Texas Austin and National Institute of Standards and Technology. Taxonomic classifiers used in Qiime are aligned with reference sets produced by curators at European Nucleotide Archive, Barcode of Life Data System, and database efforts led by J. Craig Venter Institute.
Qiime reads and writes formats common to sequencing projects coordinated with EMBL-EBI, NCBI Sequence Read Archive, and standards promoted by Global Biodiversity Information Facility. It interoperates with metadata standards developed in collaborations among Renaissance Computing Institute, RStudio, and research networks funded by Wellcome Trust. Output tables and phylogenies produced by Qiime are compatible with file types used in analyses at Australian National University, ETH Zurich, and institutions contributing to Figshare and Dryad repositories.
Performance characteristics of Qiime have been benchmarked in comparisons involving compute environments at Oak Ridge National Laboratory, Argonne National Laboratory, and university clusters at University of Wisconsin–Madison and Purdue University. Scalability considerations reference practices from distributed computing projects like Hadoop, high-performance computing centers at XSEDE, and containerized deployments associated with Docker and orchestration systems inspired by work at Kubernetes contributors. Large-scale studies using Qiime-style pipelines have been conducted by consortia similar to MetaSUB Consortium and multinational efforts supported by European Commission research programs.
Development of Qiime involves contributors from academic groups at Imperial College London, University of Copenhagen, McGill University, and collaborative networks sponsored by Bill & Melinda Gates Foundation and Gordon and Betty Moore Foundation. Community support and reproducible practices draw on platforms and governance models resembling GitHub, Conda, Bioconda, and training initiatives organized by Carnegie Mellon University and Data Carpentry. Licensing and distribution have followed models used by projects hosted by Open Source Initiative and package ecosystems akin to those overseen by Python Software Foundation and Apache Software Foundation.
Category:Bioinformatics software