Python库 | medaka-1.0.2.tar.gz

  Medaka ====== [](https://travis-ci.org/nanoporetech/medaka) [](https://pypi.org/project/medaka/) [](https://pypi.org/project/medaka/) [](https://anaconda.org/bioconda/medaka) [](https://anaconda.org/bioconda/medaka) `medaka` is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from 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). * Methylation aggregation from Guppy `.fast5` files. * Benchmarks are provided [here](https://nanoporetech.github.io/medaka/benchmarks.html). * Includes extras for implementing and training bespoke correction networks. * Works on Linux and MacOS. * 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 ------------ Medaka can be installed in one of several ways. **Installation with conda** Perhaps the simplest way to start using medaka on both Linux and MacOS is through conda; medaka is available via the [bioconda](https://anaconda.org/bioconda/medaka) channel: conda create -n medaka -c conda-forge -c bioconda medaka **Installation with pip** For those who prefer python's native pacakage manager, medaka is also available on pypi and can be installed using 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 several binaries: * [samtools](https://github.com/samtools/samtools), * [minimap2](https://github.com/lh3/minimap2), * [tabix](https://github.com/samtools/htslib), and * [bgzip](https://github.com/samtools/htslib) and place these within the `PATH`. `samtools/bgzip/tabix` version 1.9 and `minimap2` version 2.17 are recommended as these are those used in development of medaka. **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: > bzip2 g++ zlib1g-dev libbz2-dev liblzma-dev libffi-dev libncurses5-dev > libcurl4-gnutls-dev libssl-dev curl make cmake wget python3-all-dev > python-virtualenv In addition it is required to install and set up git LFS before cloning the repository. A Makefile is provided to fetch, compile and install all direct dependencies into a python virtual environment. To set-up the environment run: # Note: certain files are stored in git-lfs, https://git-lfs.github.com/, # which must therefore be installed first. 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. Unfortunately depending on your python version it may be necessary to modify the requirements of the `medaka` package for it to run without complaining. Using the source code from github a working GPU-powered `medaka` can be configured with: # Note: certain files are stored in git-lfs, https://git-lfs.github.com/, # which must therefore be installed first. git clone https://github.com/nanoporetech/medaka.git cd medaka sed -i 's/tensorflow/tensorflow-gpu/' requirements.txt make install However, note that The `tensorflow-gpu` GPU package is compiled against specific versions of the NVIDIA CUDA and cuDNN libraries; users are directed to the [tensorflow installation](https://www.tensorflow.org/install/gpu) pages for further information. cuDNN can be obtained from the [cuDNN Archive](https://developer.nvidia.com/rdp/cudnn-archive), whilst CUDA from the [CUDA Toolkit Archive](https://developer.nvidia.com/cuda-toolkit-archive). Depending on your GPU, `medaka` may show out of memory errors when running. To avoid these the inference batch size can be reduced from the default value by setting the `-b` option when running `medaka_consensus`. A value `-b 100` is suitable for 11Gb GPUs. For users with RTX series GPUs it may be required to additionally set an environment variable to have `medaka` run without failure: export TF_FORCE_GPU_ALLOW_GROWTH=true In this situation a further reduction in batch size may be required. 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` formats 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, otherwise 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 ${NPROC} -m r941_min_high_g303 The variables `BASECALLS`, `DRAFT`, and `OUTDIR` in the above should be set appropriately. For the selection of the model (`-m r941_min_high_g303` in the example above) see the Model section following. When `medaka_consensus` has finished running, the consensus will be saved to `${OUTDIR}/consensus.fasta`. Models ------ For best results it is important to specify the correct model, `-m` in the above, according to the basecaller used. Allowed values can be found by running `medaka tools list\_models`. Medaka models are named to indicate i) the pore type, ii) the sequencing device (MinION or PromethION), iii) the basecaller variant, and iv) the basecaller version, with the format: {pore}_{device}_{caller variant}_{caller version} For example the model named `r941_min_fast_g303` should be used with data from MinION (or GridION) R9.4.1 flowcells using the fast Guppy basecaller version 3.0.3. By contrast the model `r941_prom_hac_g303` should be used with PromethION data and the high accuracy basecaller (termed "hac" in Guppy configuration files). Where a version of Guppy has been used without an exactly corresponding medaka model, the medaka model with the highest version equal to or less than the guppy version should be selected. Methylation Calling ------------------- `medaka` includes a basic workflow for aggregating Guppy basecalling results for Dcm, Dam, and CpG methylation. The workflow is currently very preliminary and subject to change and improvement. Aggregating the information from Guppy outputs is a two stage process, first the basecalling results are extracted `.