Metadata-Version: 2.1
Name: macrel
Version: 0.6.1
Summary: MACREL
Home-page: UNKNOWN
Author: Celio Dias Santos-Junior and Luis Pedro Coelho
Author-email: luispedro@big-data-biology.org
License: MIT
Description: # MACREL: Meta(genomic) AMP Classification and Retrieval
        
        Pipeline to mine antimicrobial peptides (AMPs) from (meta)genomes.
        
        [![Build Status](https://travis-ci.com/BigDataBiology/macrel.svg?branch=master)](https://travis-ci.com/BigDataBiology/macrel)
        [![Documentation Status](https://readthedocs.org/projects/macrel/badge/?version=latest)](https://macrel.readthedocs.io/en/latest/?badge=latest)
        [![license: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
        [![Install with Bioconda](https://anaconda.org/bioconda/macrel/badges/installer/conda.svg)](https://anaconda.org/bioconda/macrel)
        [![Install with Bioconda](https://anaconda.org/bioconda/macrel/badges/downloads.svg)](https://anaconda.org/bioconda/macrel)
        
        If you use this software in a publication please cite
        
        >   MACREL: antimicrobial peptide screening in genomes and metagenomes.
        >   Celio Dias Santos-Junior, Shaojun Pan, Xing-Ming Zhao, Luis Pedro Coelho.
        >   bioRxiv 2019.12.17.880385; doi:
        >   [https://doi.org/10.1101/2019.12.17.880385](https://doi.org/10.1101/2019.12.17.880385)
        
        
        ## License
        
        GPLv3.
        
        While Macrel as a whole is **GPL v3** licensed (to comply with it being used in
        some of its dependencies, namely Peptides), the macrel-specific code is also
        licensed under the **MIT** license.
        
        ## Install
        
        The recommended method of installation is through
        [bioconda](https://anaconda.org/bioconda/macrel):
        
        ```bash
        conda install -c bioconda macrel
        ```
        
        To install from source, [read the
        docs](https://macrel.readthedocs.io/en/latest/install)
        
        ### Examples
        
        > Macrel uses a _subcommand interface_. You run `macrel COMMAND ...` with the
        > COMMAND specifying which components of the pipeline you want to use.
        
        To run these examples, first download the example sequences from
        [github](https://github.com/BigDataBiology/macrel/tree/master/example_seqs), or
        by running:
        
        ```bash
        macrel get-examples
        ```
        
        The main output file generated by Macrel consists of a table with 6 columns containing
        the: sequence access code, peptide sequence, classification of peptide accordingly
        composition and structure, the probability associated with the AMP prediction,
        hemolytic activity prediction and probability associated to hemolytic activity
        prediction. All peptides outputted in this table are considered AMPs (p > 0.5),
        although peptides predicted as AMPs with probabilities closer to 1 are more likely
        to be active. 
        
        To run Macrel on peptides, use the `peptides` subcommand:
        
        ```bash
        macrel peptides \
            --fasta example_seqs/expep.faa.gz \
            --output out_peptides \
            -t 4
        ```
        
        In this case, we use `example_seqs/expep.faa.gz` as the input sequence. This should
        be an amino-acid FASTA file. The outputs will be written into a folder called
        `out_peptides`, and Macrel will 4 threads. An example of output using
        this mode can be found at `test/peptides/expected.prediction`.
        
        To run Macrel on contigs, use the `contigs` subcommand:
        
        ```bash
        macrel contigs \
            --fasta example_seqs/excontigs.fna.gz \
            --output out_contigs
        ```
        
        In this example, we use the example file `excontigs.fna.gz` which is a FASTA
        file with nucleotide sequences, writing the output to `out_contigs`.
        An example of output using this mode can be found at `test/contigs/expected.prediction`.
        Additionally to the prediction table, this mode also produces two files containing
        general gene prediction information in the contigs and a fasta file containing the
        predicted and filtered small genes (<= 100 amino acids).
        
        To run Macrel on paired-end reads, use the `reads` subcommand:
        
        ```bash
        macrel reads \
            -1 example_seqs/R1.fq.gz \
            -2 example_seqs/R2.fq.gz \
            --output out_metag \
            --outtag example_metag
        ```
        
        The paired-end reads are given as paired files (here, `example_seqs/R1.fq.gz`
        and `example_seqs/R2.fq.gz`). If you only have single-end reads, you can omit
        the `-2` argument. An example of outputs using this mode can be found at
        `test/reads/expected.prediction` and `test/reads/expected.smorfs.faa`.
        Additionally to the prediction table, this mode also produces a contigs fasta file, 
        and the two files containing general gene prediction coordinates and a fasta file
        containing the predicted and filtered small genes (<= 100 amino acids).
        
        To run Macrel to get abundance profiles, you only need the short reads file
        and a reference with peptide sequences. Use the `abundance` subcommand:
        
        
        ```bash
        macrel abundance \
            -1 example_seqs/R1.fq.gz \
            --fasta example_seqs/ref.faa.gz \
            --output out_abundance \
            --outtag example_abundance
        ```
        
        This mode returns a table of abundances containing two columns, the first with the
        name of the AMPs and the second with the number of reads mapped back to each peptide
        using the given reference. An example of this output using the example file can be found
        at `test/abundances/expected.abundance.txt`.
        
        ### Community
        
        Macrel is actively developed to fix all issues and assimilate all suggestions we get from 
        users. To make this communication more dynamic and even more colaborative, we created a google
        community. There, Macrel users can discuss issues, ongoing projects, colaborations and
        much more. Please pay us a visit:
        
        [**Go to AMPsphere community now!**](https://groups.google.com/g/ampsphere-users?pli=1)
        
        *Technical issues still are encouraged to be preferentialy sent to the reserved space in the
        Macrel github repository.*
        
        ---
        
        This is a project hosted by the **Big Data Biology Research Group**, follow this [link](big-data-biology.org/) to know us more.
        
Platform: Any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: R
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
