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Qiita: report of progress towards an open access microbiome data analysis and visualization~platform

The Qiita Development Team
University of California, San Diego


Advances in sequencing, proteomics, transcriptomics and metabolomics are giving us new insights into the microbial world and dramatically improving our ability to understand microbial community composition and function at high resolution. These new technologies are generating vast amounts of data, even from a single study or sample, leading to challenges in storage, representation, analysis, and integration of the disparate data types.

Qiita~( aims to be the leading platform to store, analyze, and share multi-omics data.~Qiita is BSD-licensed, unit-tested, and adherent to PEP8 style guidelines. New code additions are reviewed by multiple developers and tested using Travis CI. This approach opens development to the largest possible number of experts in \textquotedbl{}-omics\textquotedbl{} fields. The heterogeneous data generated by these disciplines led us to use a combination of Redis, PostgreSQL, BIOM (Atr10), and HDF5 for relational and hierarchical storage. The compute backend is provided by IPython’s parallel framework ( In addition, the project depends on mature Python packages such as Tornado (, click (, scipy (, numpy (, and scikit-bio ( others. Most notably, the analysis pipeline is provided by QIIME (, with EMPeror ( as the visualization platform for high-dimensional ordination plots, which can be recolored interactively and manipulated using the sample metadata.

By providing the database and compute resources at to the global community of microbiome researchers, Qiita alleviates the technical burdens, such as familiarity with the command line or access to compute power, that are typically limiting for researchers studying microbial ecology, while at the same time promoting an open access culture. Because Qiita is entirely open source and highly scalable, developers can inspect, customize, and extend it to suit their needs regardless of whether it is deployed as a desktop application or as a shared resource.


Microbiome, multi-omics, open science, metagenomics, metatranscriptomics, metaproteomics, metabolomics

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