qiime 1.8.0+dfsg-4ubuntu1 source package in Ubuntu


qiime (1.8.0+dfsg-4ubuntu1) yakkety; urgency=medium

  * Add a build-dep on python-biom-format to only build where it exists.

 -- Adam Conrad <email address hidden>  Sat, 30 Apr 2016 21:48:32 -0600

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Uploaded by:
Adam Conrad on 2016-05-01
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Medium Urgency

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Disco release on 2018-10-30 universe science
Cosmic release on 2018-05-01 universe science
Bionic release on 2017-10-24 universe science
Artful release on 2017-04-20 universe science


File Size SHA-256 Checksum
qiime_1.8.0+dfsg.orig.tar.xz 14.0 MiB cb9572d566a793ebaf47ecca4de0df895a991e97986594554cb927bb48d06a79
qiime_1.8.0+dfsg-4ubuntu1.debian.tar.xz 22.7 KiB bf0157c77e53e452abf2465fe919e795b6738b8855c7d06556615e9558034661
qiime_1.8.0+dfsg-4ubuntu1.dsc 2.3 KiB 6c00dc204bd3938e1e45fe630879a20fc33e9084ab1bc57aa591d0fad62763ad

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Binary packages built by this source

qiime: Quantitative Insights Into Microbial Ecology

 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,
 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
 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

qiime-data: Quantitative Insights Into Microbial Ecology (supporting data)

 This package contains the GreenGenes core data set needed by QIIME for PyNAST
 alignment and filtering, but you will still need to download the appropriate
 database for taxonomic assignment.

qiime-doc: Quantitative Insights Into Microbial Ecology (tutorial)

 QIIME (canonically pronounced ‘Chime’) is a pipeline for performing
 microbial community analysis that integrates many third party tools which
 have become standard in the field. A standard QIIME analysis begins with
 sequence data from one or more sequencing platforms, including
  * Sanger,
  * Roche/454, and
  * Illumina GAIIx.
 With all the underlying tools installed,
 of which not all are yet available in Debian (or any other Linux
 distribution), QIIME can perform
  * library de-multiplexing and quality filtering;
  * denoising with PyroNoise;
  * OTU and representative set picking with uclust, cdhit, mothur, BLAST,
    or other tools;
  * taxonomy assignment with BLAST or the RDP classifier;
  * sequence alignment with PyNAST, muscle, infernal, or other tools;
  * phylogeny reconstruction with FastTree, raxml, clearcut, or other tools;
  * alpha diversity and rarefaction, including visualization of results,
    using over 20 metrics including Phylogenetic Diversity, chao1, and
    observed species;
  * beta diversity and rarefaction, including visualization of results,
    using over 25 metrics including weighted and unweighted UniFrac,
    Euclidean distance, and Bray-Curtis;
  * summarization and visualization of taxonomic composition of samples
    using pie charts and histograms
 and many other features.
 QIIME includes parallelization capabilities for many of the
 computationally intensive steps. By default, these are configured to
 utilize a mutli-core environment, and are easily configured to run in
 a cluster environment. QIIME is built in Python using the open-source
 PyCogent toolkit. It makes extensive use of unit tests, and is highly
 modular to facilitate custom analyses.
 This package contains the documentation and a tutorial.