Macrel: (Meta)genomic AMP Classification and Retrieval

Pipeline to mine antimicrobial peptides (AMPs) from (meta)genomes.

If you use this software in a publication please cite

Santos-Júnior CD, Pan S, Zhao X, Coelho LP. 2020. Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ 8:e10555. DOI: 10.7717/peerj.10555

License

MIT.

Macrel as a whole is under the MIT license.

Install

The recommended method of installation is through bioconda:

conda install mamba -n base -c conda-forge 
mamba install macrel -c bioconda

To install from source, read the docs

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, or by running:

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:

macrel peptides \
    --fasta example_seqs/expep.faa.gz \
    --output out_peptides

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:

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:

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:

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!

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 to know us more.