Modified by Josh L. Espinoza for Pyrodigal and PyHMMSearch support.
MetaCoAG is a metagenomic contig binning tool that makes use of the connectivity information found in assembly graphs, apart from the composition and coverage information. MetaCoAG makes use of single-copy marker genes along with a graph matching technique and a label propagation technique to bin contigs. MetaCoAG is tested on contigs obtained from next-generation sequencing (NGS) data. Currently, MetaCoAG supports contigs assembled using metaSPAdes and MEGAHIT, and recently we have added support for Flye assemblies (has not been tested extensively).
For detailed instructions on installation, usage and visualisation, please refer to the documentation hosted at Read the Docs.
For original implementation, please refer to https://github/metagentools/MetaCoAG
MetaCoAG installation requires Python 3.7 or above. You will need the following python dependencies to run MetaCoAG and related support scripts. The latest tested versions of the dependencies are listed as well.
MetaCoAG NAL mod allows for precomputed gene predictions, cleans up intermediate files, and uses the following tools to scan for single-copy marker genes. These tools have been tested on the following versions.
pip install https://github.com/jolespin/metacoag-nal/releases/download/1.2.3rc2/metacoag-1.2.3rc2.tar.gz
After setting up, run the following command to ensure that metacoag is working.
metacoag --help
Usage: metacoag [OPTIONS]
MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly
Graphs
Options:
--assembler [spades|megahit|megahitc|flye|custom]
name of the assembler used. (Supports
SPAdes, MEGAHIT and Flye) [required]
--graph PATH path to the assembly graph file [required]
--contigs PATH path to the contigs file [required]
--abundance PATH path to the abundance file [required]
--paths PATH path to the contigs.paths (metaSPAdes) or
assembly.info (metaFlye) file
--output PATH path to the output folder [required]
--proteins TEXT path to proteins fasta in Prodigal format
--proteins_to_contigs TEXT Tab-delimited file mapping proteins to
contigs [id_protein]<tab>[id_contig]. If
--proteins and provided without
--proteins_to_contigs then id_protein
formatting is assumed to be
[id_contig]_[gene_number]
--hmm TEXT path to marker.hmm[.gz] file. [default:
auxiliary/marker.hmm.gz]
--hmm_marker_field [accession|name]
HMM reference type [default: accession]
--score_type [full|domain] Score reflects full sequence or domain only
[default: full]
--threshold_method [gathering|noise|trusted|e]
Cutoff threshold method [default: trusted]
--evalue FLOAT E-value threshold . [default: 10]
--min_length INTEGER minimum length of contigs to consider for
binning. [default: 1000]
--p_intra FLOAT RANGE minimum probability of an edge matching to
assign to the same bin. [default: 0.1;
0<=x<=1]
--p_inter FLOAT RANGE maximum probability of an edge matching to
create a new bin. [default: 0.01; 0<=x<=1]
--d_limit INTEGER distance limit for contig matching.
[default: 20]
--depth INTEGER depth to consider for label propagation.
[default: 10]
--n_mg INTEGER total number of marker genes. [default:
108]
--bin_mg_threshold FLOAT RANGE minimum fraction of marker genes that should
be present in a bin. [default: 0.33333;
0<=x<=1]
--min_bin_size INTEGER minimum size of a bin to output in base
pairs (bp). [default: 200000]
--delimiter [,|;|$'\t'|" "] delimiter for output results. Supports a
comma (,), a semicolon (;), a tab ($'\t'), a
space (" ") and a pipe (|) . [default: ,]
--nthreads INTEGER number of threads to use. [default: 8]
-v, --version Show the version and exit.
--help Show this message and exit.
metacoag --assembler spades --graph /path/to/graph_file.gfa --contigs /path/to/contigs.fasta --paths /path/to/paths_file.paths --abundance /path/to/abundance.tsv --output /path/to/output_folder
metacoag --assembler megahit --graph /path/to/graph_file.gfa --contigs /path/to/contigs.fasta --abundance /path/to/abundance.tsv --output /path/to/output_folder
metacoag --assembler flye --graph /path/to/assembly_graph.gfa --contigs /path/to/assembly.fasta --paths /path/to/assembly_info.txt --abundance /path/to/abundance.tsv --output /path/to/output_folder
metacoag --assembler megahit --graph /path/to/graph_file.gfa --contigs /path/to/contigs.fasta --abundance /path/to/abundance.tsv --output /path/to/output_folder --proteins /path/to/proteins.faa
metacoag --assembler megahit --graph /path/to/graph_file.gfa --contigs /path/to/contigs.fasta --abundance /path/to/abundance.tsv --output /path/to/output_folder --proteins /path/to/proteins.faa --proteins_to_contigs /path/to/proteins_to_contigs.tsv
If you use MetaCoAG in your work, please cite the following publications.
Mallawaarachchi, V., Lin, Y. (2022). MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs. In: Pe'er, I. (eds) Research in Computational Molecular Biology. RECOMB 2022. Lecture Notes in Computer Science(), vol 13278. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-04749-7_5
Vijini Mallawaarachchi and Yu Lin. Accurate Binning of Metagenomic Contigs Using Composition, Coverage, and Assembly Graphs. Journal of Computational Biology 2022 29:12, 1357-1376. DOI: https://doi.org/10.1089/cmb.2022.0262
Initial implementation of MetaCoAG is funded by an Essential Open Source Software for Science Grant from the Chan Zuckerberg Initiative.
