Metadata-Version: 2.4 Name: MirMachine Version: 0.3.0.5 Summary: MirMachine Home-page: https://github.com/sinanugur/MirMachine Author: Sinan U. Umu Author-email: sinanugur@gmail.com Keywords: RNA miRNA detection prediction Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3 Classifier: Topic :: Scientific/Engineering :: Bio-Informatics Description-Content-Type: text/markdown License-File: LICENSE.txt Requires-Dist: appdirs==1.4.4 Requires-Dist: attrs==25.3.0 Requires-Dist: biopython==1.85 Requires-Dist: configargparse==1.7 Requires-Dist: datrie==0.8.2 Requires-Dist: docopt==0.6.2 Requires-Dist: docutils==0.21.2 Requires-Dist: gitdb==4.0.12 Requires-Dist: gitpython==3.1.44 Requires-Dist: jsonschema==4.23.0 Requires-Dist: nbformat==5.10.4 Requires-Dist: numpy==2.2.5 Requires-Dist: psutil==7.0.0 Requires-Dist: pyyaml==6.0.2 Requires-Dist: smmap==5.0.2 Requires-Dist: snakemake==9.3.3 Requires-Dist: toposort==1.10 Requires-Dist: traitlets==5.14.3 Requires-Dist: wrapt==1.17.2 Requires-Dist: zipp==3.21.0 Requires-Dist: rich==14.0.0 Provides-Extra: dev Requires-Dist: pytest>=3.7; extra == "dev" Dynamic: author Dynamic: author-email Dynamic: classifier Dynamic: description Dynamic: description-content-type Dynamic: home-page Dynamic: keywords Dynamic: license-file Dynamic: provides-extra Dynamic: requires-dist Dynamic: summary # MirMachine Logo [![Build Status](https://app.travis-ci.com/sinanugur/MirMachine.svg?branch=master)](https://app.travis-ci.com/sinanugur/MirMachine) [![Documentation Status](https://readthedocs.org/projects/mirmachine/badge/?version=latest)](https://mirmachine.readthedocs.io/en/latest/?badge=latest) [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) [![PyPI version](https://badge.fury.io/py/MirMachine.svg?style=flat&cache-control=no-cache)](https://badge.fury.io/py/MirMachine) [![Anaconda-Server Badge](https://anaconda.org/bioconda/mirmachine/badges/version.svg?style=flat&cache-control=no-cache)](https://anaconda.org/bioconda/mirmachine) [![Anaconda-Server Badge](https://anaconda.org/bioconda/mirmachine/badges/downloads.svg?style=flat&cache-control=no-cache)](https://anaconda.org/bioconda/mirmachine) [![Anaconda-Server Badge](https://anaconda.org/bioconda/mirmachine/badges/latest_release_relative_date.svg?style=flat&cache-control=no-cache)](https://anaconda.org/bioconda/mirmachine) [![Docker Pulls](https://img.shields.io/docker/pulls/sinanugur/mirmachine)](https://hub.docker.com/r/sinanugur/mirmachine) A command line tool to detect miRNA homologs in genome sequences. Installation ------------ To install this package with conda run: ``` conda install mirmachine -c bioconda -c conda-forge ``` Please add conda-forge as a channel. Installing via [mamba](https://github.com/mamba-org/mamba) is also strongly recommended for a faster installation. You can install __mamba__ and later MirMachine like this: ``` conda install mamba -c conda-forge mamba install mirmachine -c bioconda -c conda-forge ``` Check if the installation works by calling the main script. ``` MirMachine.py --help ``` Note: You have to install dependencies if you prefer GitHub or PyPi installation. Quick start example ------------------- Create a new directory and run MirMachine there after the installation. MirMachine will create the required directories while running. ``` MirMachine.py -n Caenorhabditis -s Caenorhabditis_elegans --genome sample/genomes/ce11.fa --cpu 20 --model proto ``` See our documentation for detailed explanations: https://mirmachine.readthedocs.io/ Options and Arguments --------------------- ``` Usage: MirMachine.py --node --species --genome [--model ] [--evalue ] [--cpu ] [--add-all-nodes|--single-node-only] [--unlock|--remove] [--touch] [--dry] [--long] MirMachine.py --species --genome --family [--model ] [--evalue ] [--cpu ] [--unlock|--remove] [--touch] [--dry] [--long] MirMachine.py --node [--add-all-nodes] MirMachine.py --print-all-nodes MirMachine.py --print-all-families MirMachine.py --print-ascii-tree MirMachine.py (-h | --help) MirMachine.py --version Arguments: -n , --node Node name. (e.g. Caenorhabditis) -s , --species Species name. (e.g. Caenorhabditis_elegans) -g , --genome Genome fasta file location (e.g. data/genome/example.fasta) -m , --model Model type: deutero, proto, combined [default: combined] -f , --family Run only a single microRNA family (e.g. Let-7). -e , --evalue Inclusion E-value. May inflate low quality hits. [default: 0.2] Default 5 if --long is used. -c , --cpu CPUs. [default: 2] Options: -a, --add-all-nodes Move on the tree both ways. NOT required most of the time. -o, --single-node-only Run only on the given node for microRNA families. --long Use long microRNA covariance models rather than standard models (Experimental). -p, --print-all-nodes Print all available node options and exit. -l, --print-all-families Print all available families in this version and exit. -t, --print-ascii-tree Print ascii tree of the tree file. -u, --unlock Rescue stalled jobs (Try this if the previous job ended prematurely). -r, --remove Clear all output files (this won't remove input files). -d, --dry Dry run. -h, --help Show this screen. --touch Touch output files (mark them up to date without really changing them). --version Show version. ``` Output ------ The `MirMachine` main executable will generate GFF annotations (filtered and unfiltered) and some other files. You will see `results/predictions/` directory which contains: `gff/` __All predicted microRNA families.__ `filtered_gff/` __High confidence microRNA family predictions after bitscore filtering. (This file is what you need in most cases)__ `fasta/` __Both high and low confidence predictions in FASTA format.__ MirMachine's other repos ------ Supplementary files repo: https://github.com/sinanugur/MirMachine-supplementary Citation ------ Our Cell Genomics paper is here: [https://doi.org/10.1016/j.xgen.2023.100348](https://doi.org/10.1016/j.xgen.2023.100348) Please cite if you find our tool useful. MirMachine website: https://mirmachine.org