nanomotif 1.0.2

Identifying methlyation motifs in nanopore data

Nanomotif

Nanomotif is a Python package designed to explore methylation in prokaryotic genomes using Nanopore sequencing. Nanomotif is a fast, scalable, and sensitive tool for identification and utilization of methylation motifs in monocultures and metagenomic samples.

• Overview
• Documentation
• Installation
• Usage
• Citation

Nanomotif offers

• de novo methylated motif identification
• metagenomic bin contamination detection
• bin association of unbinned contigs (eg. plasmids)
• association of MTases and RM-systems to motifs.

Documentation

Please see the documentation for detailed installation and usage instructions, descriptions of required files, and analysis examples.

Skip the Documentation: Your Quickstart Guide to Nanomotif

Installation

Nanomotif can easily be installed using Conda for managing your Python environments. You can create a new environment and install Nanomotif as follows:

conda create -n nanomotif  python=3.12
conda activate nanomotif
conda install -c conda-forge -c bioconda nanomotif

Check installation

Once installed, the installation can be checked by running:

nanomotif check_installation

This runs a test run on a small dataset, ensuring everything works.

For further details, check out the installation guidelines.

Usage

Required files

To identify methylated motifs, the following files are required:

• Assembly (fasta file)
• modkit methylation pileup
• tab-separated file describing contig-bin relationship.

For further details, check out the required files documentation.

Motif discovery

Whether you are interested in finding methylated motifs in monoculture samples or metagenomic samples, we recomment just running motif_discovery

nanomotif motif_discovery ASSEMBLY.fasta PILEUP.bed CONTIG_BIN.tsv -t THREADS --out OUT

This will create three files: motifs.tsv,motif-scored.tsv, and bin-motifs.tsv. Highly methylated motifs are found in bin-motifs.tsv.

See usage and output for detailed usage and output information.

Bin contamination

After motif identification it is possible to identify contamination in bins using the bin-motifs.tsv, assembly, pileup and contig_bin file.

nanomotif detect_contamination --pileup PILEUP --assembly ASSEMBLY --bin_motifs BIN_MOTIFS --contig_bins CONTIG_BIN --out OUT

This will generate a bin_contamination.tsv specifying the contigs, which is flagged as contamination.

If the --write_bins and the --assembly_file flags are specified new de-contaminated bins will be written to a bins folder.

See usage and output for detailed usage and output information.

Include unbinned contigs

The include_contigs command assigns unbinned contigs in the assembly file to bins by training three classifiers, random forest, linear discriminant analysis, and k neighbors classifier, on the methylation pattern of the bins.

nanomotif include_contigs --pileup PILEUP --assembly ASSEMBLY --bin_motifs BIN_MOTIFS --contig_bins CONTIG_BIN --run_detect_contamination --out OUT

Note: this is not a binner and included contigs should be considered putative. Methylation patterns can be shared across MAGs, which is problematic for unrecovered MAGs.

If decontamination should not be performed, the include_contigs can be run without the --run_detect_contamination flag or without the --contamination_file flag.

MTase-linker

This module tries to link methylation motifs to their corresponding MTase and, when present, their entire RM system.

The MTase-Linker module has additional dependencies that are not automatically installed with Nanomotif. Therefore, before using this module, you must manually install these dependencies using the MTase-linker install command. The MTase-linker module requires that conda is available on your system.

nanomotif MTase-linker install

This will create a folder named ML_dependencies in your current working directory, containing the required dependencies for the MTase-linker module. You can use the --dependency_dir flag to change the installation location of the ML_dependencies folder.

The installation requires conda to generate a few environments, and it takes a bit time as it runs the workflow on a small dataset to check the installation.

When the additional dependencies are installed you can run the workflow using MTase-linker run

nanomotif MTase-linker run -t 10 --assembly ASSEMBLY.fasta --contig_bin contig_bin.tsv --bin_motifs nanomotif/bin_motifs.tsv -d ML_dependencies -o mtase_linker

Running the nanomotif MTase-linker run command will generate two primary output files: mtase_assignment_table.tsv and nanomotif_assignment_table.tsv. The first file lists all predicted MTase genes in the genome along with their predicted methylation characteristics and whether the module was able to unambiguously assign any detected motifs to the MTase (linked = (True/False)). The second file includes data from the bin-motifs.tsv of the nanomotif output with two additional columns linked and candidate_genes. The linked variable is a boolean indicator if the motif could be unambiguously linked to a MTase in the bin/genome (TRUE/FALSE). If True the gene_id of the MTase is provided in candidate_gene. If False, the candidate_gene variable lists feasible candidate facilitators of the modification based on motif type and modification type predictions.

Citation

Please cite our preprint if you use Nanomotif for your research:

Nanomotif: Leveraging DNA Methylation Motifs for Genome Recovery and Host Association of Plasmids in Metagenomes from Complex Microbial Communities. Søren Heidelbach, Sebastian Mølvang Dall, Jeppe Støtt Bøjer, Jacob Nissen, Lucas Nicolaas Ludovic van der Maas, Mantas Sereika, Rasmus Kirkegaard, Sabrina Just Kousgaard, Ole Thorlacius-Ussing, Sheila I Jensen, Katja Hose, Thomas Dyhre Nielsen, Mads Albertsen. Preprint at bioRxiv https://doi.org/10.1101/2024.04.29.591623 (2024)

License

Nanomotif is released under the MIT License. Feel free to use, modify, and distribute the package in accordance with the terms of the license.

Acknowledgments

Nanomotif builds upon various open-source libraries and tools that are instrumental in its functionality. We would like to express our gratitude to the developers and contributors of these projects for their valuable work.