Metadata-Version: 1.2
Name: vica
Version: 0.1.5
Summary: find highly divergent DNA and RNA viruses in microbiomes
Home-page: http://github.com/usda-ars-gbru/vica
Author: Adam R. Rivers, Qingpeng Zhang
Author-email: adam.rivers@ars.usda.gov
License: License :: OSI Approved :: BSD License
Description-Content-Type: UNKNOWN
Description: Vica: Software to identify highly divergent DNA and RNA viruses and phages in microbiomes
        =========================================================================================
        .. image:: https://travis-ci.org/USDA-ARS-GBRU/vica.svg?branch=master
            :target: https://travis-ci.org/USDA-ARS-GBRU/vica
        
        .. image:: https://codecov.io/gh/USDA-ARS-GBRU/vica/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/USDA-ARS-GBRU/vica
        
        .. image:: https://readthedocs.org/projects/vica/badge/?version=latest
            :target: http://vica.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        
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        Authors
        -------
        * Adam R. Rivers, US Department of Agriculture, Agricultural Research Service
        * Qingpeng Zhang, US Department of Energy, Joint Genome Institute
        * Susannah G. Tringe, US Department of Energy, Joint Genome Institute
        
        Introduction
        ------------
        
        Vica is designed to identify highly divergent viruses and phage representing new
        families or orders in assembled metagenomic and metatranscriptomic data. Vica
        does this by combining information from across the spectrum of composition
        to homology. The current version of Vica uses three feature sets (5-mers,
        codon usage in all three frames, and minhash sketches from long kmers (k=24,31).
        The classifier uses a jointly trained deep neural network and logistic model
        implemented in Tensorflow. The software is designed to identify  both DNA
        and RNA viruses and phage in metagenomes and metatranscriptomes.
        
        Models
        ------
        
        The current leases does not include trained models but we will be adding them
        in the future to allow for the rapid identification of viruses without model training.
        
        Usage
        -----
        
        This package can classify assembled data and train new classification models.
        Most users will only use the classification functionality in Vica. We will provide
        trained models for classifying contigs in future releases. classification can be
        easily invoked with the command::
        
           vica classify -infile contigs.fasta -out classifications.txt -modeldir modeldir
        
        The package also has a suite of tools to prepare data, train and evaluate new
        classification models. Many of the workflows for doing this can be evoked with
        the same sub-command interface::
        
           vica split
           vica get_features
           vica train
           vica evaluate
        
        For details see the Tutorial.
        
        Requirements
        ------------
        
        The package relies on a number of python dependencies that are resolved when
        the package is installed with PIP.
        
        The non-python dependencies are:
        
        - Bbtools > v37.75- https://jgi.doe.gov/data-and-tools/bbtools/
        - Prodigal > v2.6.3 - https://github.com/hyattpd/Prodigal
        - GNU Coreutils - http://www.gnu.org/software/coreutils/coreutils.html
        
        Documentation
        -------------
        Documentation for the package is at http://vica.readthedocs.io/en/latest/
        
        Package availability
        --------------------
        - PyPi: https://pypi.python.org/pypi/vica
        - Github: https://github.com/USDA-ARS-GBRU/vica
        
        
        Copyright information
        ---------------------
        
        ViCA Copyright (c) 2018, The Regents of the University of California, through
        Lawrence Berkeley National Laboratory (subject to receipt of any required
        approvals from the U.S. Dept. of Energy).  All rights reserved.
        
        If you have questions about your rights to use or distribute this software,
        please contact Berkeley Lab's Innovation & Partnerships Office at  IPO@lbl.gov
        referring to " Viral Classification Algorithm Using Supervised Learning (LBNL
        Ref 2017-125)."
        
        NOTICE.  This software was developed under funding from the U.S. Department of
        Energy.  As such, the U.S. Government has been granted for itself and others
        acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in
        the Software to reproduce, prepare derivative works, and perform publicly and
        display publicly.  The U.S. Government is granted for itself and others acting
        on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
        Software to reproduce, prepare derivative works, distribute copies to the
        public, perform publicly and display publicly, and to permit others to do so.
        
Keywords: virus classifier metagenome RNA DNA microbiome tensorflow
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.5
Classifier: Development Status :: 3 - Alpha
Requires-Python: >3.5
