Metadata-Version: 2.1
Name: medaka
Version: 0.4.1
Summary: Neural network sequence error correction.
Home-page: https://github.com/nanoporetech/medaka
Author: syoung
Author-email: syoung@nanoporetech.com
License: UNKNOWN
Description: 
        ![Oxford Nanopore Technologies logo](images/ONT_logo_590x106.png)
        
        
        Medaka
        ======
        
        [![Build Status](https://travis-ci.org/nanoporetech/medaka.svg?branch=master)](https://travis-ci.org/nanoporetech/medaka)
        
        `medaka` is a tool to create a consensus sequence of nanopore sequencing data.
        This task is performed using neural networks applied a pileup of individual
        sequencing reads against a draft assembly. It outperforms graph-based methods
        operating on basecalled data, and can be competitive with state-of-the-art
        signal-based methods whilst being much faster.
        
        © 2018 Oxford Nanopore Technologies Ltd.
        
        Features
        --------
        
          * Requires only basecalled data. (`.fasta` or `.fastq`)
          * Improved accurary over graph-based methods (e.g. Racon).
          * 50X faster than Nanopolish (and can run on GPUs).
          * Benchmarks are provided [here](https://nanoporetech.github.io/medaka/benchmarks.html).
          * Includes extras for implementing and training bespoke correction
            networks.
          * Works on Linux (MacOS and Windows support is untested).
          * Open source (Mozilla Public License 2.0).
        
        Tools to enable the creation of draft assemblies can be found in a sister
        project [pomoxis](https://github.com/nanoporetech/pomoxis).
        
        Documentation can be found at https://nanoporetech.github.io/medaka/.
        
        
        Installation
        ------------
        
        There are currently two installation methods for medaka, detailed below.
        
        **Installation with pip**
          
        Medaka can be installed using the python package manager, pip:
        
            pip install medaka
        
        On Linux platforms this will install a precompiled binary, on MacOS (and other)
        platforms this will fetch and compile a source distribution.
        
        We recommend using medaka within a virtual environment, viz.:
        
            virtualenv medaka --python=python3 --prompt "(medaka) "
            . medaka/bin/activate
            pip install medaka
        
        Using this method requires the user to provide a
        [samtools](https://github.com/samtools/samtools) and
        [minimap2](https://github.com/lh3/minimap2) binary and place these
        within the `PATH`.
        
        **Installation from source**
        
        Medaka can be installed from its source quite easily on most systems.
        
         > Before installing medaka it may be required to install some
         > prerequisite libraries, best installed by a package manager. On Ubuntu
         > theses are:
         > * gcc
         > * zlib1g-dev
         > * libbz2-dev
         > * liblzma-dev
         > * libffi-dev
         > * libncurses5-dev
         > * make
         > * wget
         > * python3-all-dev
         > * python-virtualenv
        
        A Makefile is provided to fetch, compile and install all direct dependencies
        into a python virtual environment. To setup the environment run:
        
            git clone https://github.com/nanoporetech/medaka.git
            cd medaka
            make install
            . ./venv/bin/activate
        
        Using this method both `samtools` and `minimap2` are built from source and need
        not be provided by the user.
        
        **Using a GPU**
        
        All installation methods will allow medaka to be used with CPU resource only.
        To enable the use of GPU resource it is necessary to install the
        `tensorflow-gpu` package. In outline this can be achieve with:
        
            pip uninstall tensorflow
            pip install tensorflow-gpu
        
        However, note that The `tensorflow-gpu` GPU package is compiled against a
        specific version of the NVIDIA CUDA library; users are directed to the 
        [tensorflow installation](https://www.tensorflow.org/install/gpu) pages
        for further information.
        
        
        Usage
        -----
        
        `medaka` can be run using its default settings through the `medaka_consensus`
        program. An assembly in `.fasta` format and basecalls in `.fasta` or `.fastq`
        format are required. The program uses both `samtools` and `minimap2`. If
        medaka has been installed using the from-source method these will be present
        within the medaka environment, else they will need to be provided by the user.
        
            source ${MEDAKA}  # i.e. medaka/venv/bin/activate
            NPROC=$(nproc)
            BASECALLS=basecalls.fa
            DRAFT=draft_assm/assm_final.fa
            OUTDIR=medaka_consensus
            medaka_consensus -i ${BASECALLS} -d ${DRAFT} -o ${OUTDIR} -t ${NPROC}
        
        The variables `BASECALLS`, `DRAFT`, and `OUTDIR` in the above should be set
        appropriately. When `medaka_consensus` has finished running, the consensus
        will be saved to `${OUTDIR}/consensus.fasta`.
        
        Acknowledgements
        ----------------
        
        We thank [Joanna Pineda](https://github.com/jopineda) and
        [Jared Simpson](https://github.com/jts) for providing htslib code samples which aided
        greatly development of the optimised feature generation code, and for testing the
        version 0.4 release candidates.
        
        Help
        ----
        
        **Licence and Copyright**
        
        © 2018 Oxford Nanopore Technologies Ltd.
        
        `medaka` is distributed under the terms of the Mozilla Public License 2.0.
        
        **Research Release**
        
        Research releases are provided as technology demonstrators to provide early
        access to features or stimulate Community development of tools. Support for
        this software will be minimal and is only provided directly by the developers.
        Feature requests, improvements, and discussions are welcome and can be
        implemented by forking and pull requests. However much as we would
        like to rectify every issue and piece of feedback users may have, the 
        developers may have limited resource for support of this software. Research
        releases may be unstable and subject to rapid iteration by Oxford Nanopore
        Technologies.
        
Platform: UNKNOWN
Requires-Python: >=3.4.*,<3.7
Description-Content-Type: text/markdown
