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