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Source: q2-sample-classifier
Section: science
Priority: optional
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Liubov Chuprikova <chuprikovalv@gmail.com>,
Steffen Moeller <moeller@debian.org>
Build-Depends: debhelper-compat (= 13),
dh-python,
qiime (>= 2020.11.0),
python3-all,
python3-setuptools,
python3-pytest <!nocheck>
Standards-Version: 4.5.1
Vcs-Browser: https://salsa.debian.org/med-team/q2-sample-classifier
Vcs-Git: https://salsa.debian.org/med-team/q2-sample-classifier.git
Homepage: https://qiime2.org
Rules-Requires-Root: no
Package: q2-sample-classifier
Architecture: all
Depends: ${shlibs:Depends},
${misc:Depends},
${python3:Depends},
qiime (>= 2020.11.0),
python3-distutils,
q2-types,
q2-feature-table
Description: QIIME 2 plugin for machine learning prediction of sample data
QIIME 2 is a powerful, extensible, and decentralized microbiome analysis
package with a focus on data and analysis transparency. QIIME 2 enables
researchers to start an analysis with raw DNA sequence data and finish with
publication-quality figures and statistical results.
Key features:
* Integrated and automatic tracking of data provenance
* Semantic type system
* Plugin system for extending microbiome analysis functionality
* Support for multiple types of user interfaces (e.g. API, command line,
graphical)
.
QIIME 2 is a complete redesign and rewrite of the QIIME 1 microbiome analysis
pipeline. QIIME 2 will address many of the limitations of QIIME 1, while
retaining the features that makes QIIME 1 a powerful and widely-used analysis
pipeline.
.
QIIME 2 currently supports an initial end-to-end microbiome analysis pipeline.
New functionality will regularly become available through QIIME 2 plugins. You
can view a list of plugins that are currently available on the QIIME 2 plugin
availability page. The future plugins page lists plugins that are being
developed.
.
Microbiome studies often aim to predict outcomes or differentiate samples
based on their microbial compositions, tasks that can be efficiently
performed by supervised learning methods. The q2-sample-classifier plugin
makes these methods more accessible, reproducible, and interpretable to
a broad audience of microbiologists, clinicians, and others who wish to
utilize supervised learning methods for predicting sample characteristics
based on microbiome composition or other "omics" data
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