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| MetaPhlAn | |
|---|---|
| Name | MetaPhlAn |
| Developer | Nicola Segata; Christopher Quince; Peer Bork; Owen White; Curtis Huttenhower |
| Initial release | 2012 |
| Latest release | 2018 |
| Programming language | Python |
| Operating system | Multiplatform |
| License | Open-source |
| Genre | Metagenomics, Bioinformatics |
MetaPhlAn MetaPhlAn is a computational tool for profiling microbial community composition from shotgun metagenomic sequencing data. It provides species-level and strain-level relative abundance estimates by mapping reads to clade-specific marker sequences derived from reference genomes. Developed by a consortium of researchers affiliated with institutions such as Harvard University, European Molecular Biology Laboratory, and University of Trento, MetaPhlAn has been widely adopted in microbiome studies across clinical, environmental, and agricultural research.
MetaPhlAn revolutionized taxonomic profiling by replacing broad marker genes with unique clade-specific markers curated from whole genomes; early adopters included teams from Broad Institute, Wellcome Sanger Institute, Max Planck Society, Institut Pasteur, and Dana-Farber Cancer Institute. Its pipeline reduces false positives common to alignment-based classifiers used by groups at National Institutes of Health and European Bioinformatics Institute. MetaPhlAn outputs relative abundance tables compatible with downstream tools developed at EMBL-EBI, UCSC, NCBI, JGI, and projects like the Human Microbiome Project and Earth Microbiome Project.
MetaPhlAn identifies microbial clades by mapping metagenomic reads to a database of unique genomic markers compiled from reference genomes deposited at GenBank, RefSeq, and ENA. The tool uses alignment algorithms similar to those implemented in software from BLAST, Bowtie2, BWA, and integrates concepts from taxonomic classifiers used at European Nucleotide Archive. MetaPhlAn applies probabilistic weighting and normalization strategies that echo statistical approaches used in analyses from Stanford University, MIT, and Harvard School of Public Health to convert marker counts into relative abundances.
The core of MetaPhlAn is its marker catalog derived from thousands of genomes from repositories such as GenBank, RefSeq, ENA, and curated datasets from Joint Genome Institute and PATRIC. Marker selection leverages comparative genomics methods developed by teams at University of Cambridge, ETH Zurich, and University of Oxford to find sequences unique to species, genera, and higher taxa. The database has been expanded using contributions from research groups at Kyoto University, National University of Singapore, University of California, San Diego, and University College London and cross-referenced with taxonomies maintained by Linnaean Society-linked resources and standards from International Nucleotide Sequence Database Collaboration.
MetaPhlAn's accuracy and speed have been benchmarked against tools from EMBL-EBI, University of Maryland, Argonne National Laboratory, and developers of classifiers like Kraken, Centrifuge, and QIIME extensions. Validation studies published by teams at Massachusetts General Hospital, Johns Hopkins University, and University of Washington demonstrated strong species-level precision and robustness to host contamination compared to marker-free approaches used by groups at Lawrence Berkeley National Laboratory and Oak Ridge National Laboratory. Performance metrics often reference standardized datasets from consortia such as Human Microbiome Project and mock communities produced by laboratories at National Institute of Standards and Technology.
MetaPhlAn has been employed in clinical microbiome investigations conducted at Mayo Clinic, Cleveland Clinic, Karolinska Institutet, and University of Toronto to associate microbial signatures with diseases studied at World Health Organization-affiliated research programs. Environmental applications include surveys coordinated with teams at Smithsonian Institution, Scripps Institution of Oceanography, and Woods Hole Oceanographic Institution. Agricultural and industrial studies have used MetaPhlAn alongside work from USDA, INRAE, and Rothamsted Research to profile soil and plant-associated microbiomes. MetaPhlAn outputs integrate with statistical and visualization packages developed at R Foundation for Statistical Computing, Bioconductor, Galaxy Project, and visualization work from Tableau Software-related academic pipelines.
MetaPhlAn depends on a reference marker database and therefore can miss novel taxa not represented in GenBank or RefSeq, a limitation noted in comparisons with de novo assembly approaches used by groups at Broad Institute and JGI. It produces relative rather than absolute abundances unless combined with cell-count or spike-in protocols developed by laboratories at EMBL-EBI and NIST. Performance can be affected by highly uneven genomic representation similar to issues discussed in studies from NCBI and European Bioinformatics Institute. Like other computational tools, MetaPhlAn's accuracy is contingent on up-to-date taxonomies curated by institutions such as Linnaean Society-linked databases and nomenclature authorities at International Committee on Systematics of Prokaryotes.
MetaPhlAn was first released in 2012 and has undergone iterative improvements in marker selection, performance, and database curation in later releases circa 2015–2018, with contributions from researchers at University of Trento, Harvard T.H. Chan School of Public Health, European Molecular Biology Laboratory, and collaborators at Broad Institute and Wellcome Sanger Institute. Subsequent development has paralleled advances in sequencing technologies from Illumina, PacBio, and Oxford Nanopore Technologies and analytical frameworks from NCBI and EMBL-EBI. Ongoing community-driven enhancements are coordinated with open science initiatives supported by organizations such as Gates Foundation and consortia including Human Microbiome Project.