[](http://www.repostatus.org/#active)
# MAGinator
Combining the strengths of contig and gene based methods to provide:
* Accurate abundances of species using de novo signature genes
* MAGinator uses a statistical model to find the best genes for calculating accurate abundances
* SNV-level resolution phylogenetic trees based on signature genes
* MAGinator creates a phylogenetic tree for each species so you can associate your metadata with subspecies/strain level differences
* Connect accessory genome to the species annotation by getting a taxonomic scope for gene clusters
* MAGinator clusters all ORFs into gene clusters and for each gene cluster you will know which taxonomic level it is specific to
* Improve your functional annotation by grouping your genes in synteny clusters based on genomic adjacency
* MAGinator clusters gene clusters into synteny clusters - Syntenic genes are usually part of the same pathway or have similar functions
## Installation
All you need for running MAGinator is snakemake and mamba. Other dependencies will be installed by snakemake automatically.
```sh
conda create -n maginator -c bioconda -c conda-forge snakemake mamba
conda activate maginator
pip install maginator
```
Furthermore, MAGinator also needs the GTDB-tk database downloaded. Here we download release 214. If you don't already have it, you can run the following:
```sh
wget https://data.ace.uq.edu.au/public/gtdb/data/releases/release214/214.1/auxillary_files/gtdbtk_r214_data.tar.gz
tar xvzf *.tar.gz
```
## Usage
MAGinator needs 3 input files:
* The clusters.tsv files from [VAMB](https://github.com/RasmussenLab/vamb)
* A fasta file with sequences of all contigs, with unique names
* A comma-separated file giving the position of the fastq files with your sequencing reads formatted as: SampleName,PathToForwardReads,PathToReverseReads
Run MAGinator:
```sh
maginator -v vamb_clusters.tsv -r reads.csv -c contigs.fasta -o my_output -g "/path/to/GTDB-Tk/database/release214/"
```
A testset can be found in the test_data directory.
1. Download the 3 samples used for the test at SRA: https://www.ncbi.nlm.nih.gov/sra?LinkName=bioproject_sra_all&from_uid=715601 with the ID's dfc99c_A, f9d84e_A and 221641_A
2. Change the paths to the read-files in reads.csv
3. Unzip the contigs.fasta.gz
4. Run MAGinator
### Run on a compute cluster
MAGinator can run on compute clusters using qsub (torque), sbatch (slurm), or drmaa structures. The --cluster argument toggles the type of compute cluster infrastructure. The --cluster_info argument toggles the information given to the submission command, and it has to contain the following keywords {cores}, {memory}, {runtime}, which are used to forward resource information to the cluster.
A qsub MAGinator can for example be run with the following command (... indicates required arguments, see above):
```sh
maginator ... --cluster qsub --cluster_info "-l nodes=1:ppn={cores}:thinnode,mem={memory}gb,walltime={runtime}"
```
## Test data
A test set can be found in the maginator/test_data directory.
1. Download the 3 samples used for the test at SRA: https://www.ncbi.nlm.nih.gov/sra?LinkName=bioproject_sra_all&from_uid=715601 with the ID's dfc99c_A, f9d84e_A and 221641_A
2. Clone repo: git clone https://github.com/Russel88/MAGinator.git
3. Change the paths to the read-files in reads.csv
4. Unzip the contigs.fasta.gz
5. Run MAGinator
MAGinator can been run on the test data on a slurm server with the following command:
```sh
maginator --vamb_clusters clusters.tsv --reads reads.csv --contigs contigs.fasta --gtdb_db data/release214/ --output test_out --cluster slurm --cluster_info "-n {cores} --mem {mem_gb}gb -t {runtime}" --max_mem 180
```
The expected output can be found as a zipped file on Zenodo: https://doi.org/10.5281/zenodo.8279036. MAGinator has been run on the test data (using GTDB-tk db release207_v2) on a slurm server.
On the compute cluster each job have had access to 180gb RAM, with the following time consumption:
real 72m27.379s
user 0m18.830s
sys 1m0.454s
If you run on a smaller server you can set the parameters --max_cores and --max_mem.
## Recommended workflow
To generate the input files to run MAGinator we have created a recommended workflow, with preprocessing, assembly and binning* of your metagenomics reads (the rules for binning have been copied from VAMB (https://github.com/RasmussenLab/vamb/blob/master/workflow/)).
It has been setup as a snakefile in recommended_workflow/reads_to_bins.Snakefile.
The input to the workflow is the reads.csv file. The workflow can be run using snakemake:
```
snakemake --use-conda -s reads_to_bins.Snakefile --resources mem_gb=180 --config reads=reads.csv --cores 10 --printshellcmds
```
Once the binning is done, we recommend using a tool like dRep (https://github.com/MrOlm/drep) to create the species-level clusters. The advantage of dRep is that the clustering parameters can be modified to create clusters that belong to different taxonomic levels. An R script is located in recommended workflow/MAGinator_setup.R, that will create the different input files for MAGinator adapted to the output of dRep.
Preparing data for MAGinator run
```
sed 's/@/_/g' assembly/all_assemblies.fasta > all_assemblies.fasta
sed 's/@/_/g' vamb/clusters.tsv > clusters.tsv
```
Now you are ready to run MAGinator.
## Functional Annotation
To generate the functional annotation of the genes we recommend using EggNOG mapper (https://github.com/eggnogdb/eggnog-mapper).
You can download it and try to run it on the test data
```sh
mkdir test_out/functional_annotation
emapper.py -i test/genes/all_genes_rep_seq.fasta --output test_out/functional_annotation -m diamond --cpu 38
```
The eggNOG output can be merged with clusters.tsv and further processed to obtain functional annotations of the MAG, cluster or sample levels with the following command:
```sh
(echo -e '#sample\tMAG_cluster\tMAG\tfunction'; join -1 1 -2 1 <(awk '{print $2 "\t" $1}' clusters.tsv | sort) <(tail -n +6 annotations.tsv | head -n -3 | cut -f1,15 | grep -v '\-$' | sed 's/_[[:digit:]]\+\t/\t/' | sed 's/,/\n/g' | perl -lane '{$q = $F[0] if $#F > 0; unshift(@F, $q) if $#F == 0}; print "$F[0]\t$F[1]"' | sed 's/\tko:/\t/' | sort) | awk '{print $2 "\t" $2 "\t" $3}' | sed 's/_/\t/' | sort -k1,1 -k2,2n) > MAGfunctions.tsv
```
In this case the KEGG ortholog column 15 was picked from the eggNOG-mapper output. But by cutting e.g. column number 13, one would obtain GO terms instead. Refer to the header of the eggNOG-mapper output for other available functional annotations e.g. KEGG pathways, Pfam, CAZy, COGs, etc.
## MAGinator workflow
This is what MAGinator does with your input (if you want to see all parameters run maginator --help):
* Filter bins by size
* Use --binsize to control the cutoff
* Run GTDB-tk to taxonomically annotate bins and call open reading frames (ORFs)
* Group your VAMB clusters into metagenomic species (MGS) based on the taxonomic annotation. (Unannotated VAMB clusters are kept in the pipeline, but left unchanged)
* Use --no_mgs to disable this
* Use --annotation_prevalence to change how prevalent an annotation has to be in a VAMB cluster to call taxonomic consensus
* Cluster your ORFs into gene clusters to get a non-redundant gene catalogue
* Use --clustering_min_seq_id to toggle the clustering identity
* Use --clustering_coverage to toggle the clustering coverage
* Use --clustering_type to toggle whether to cluster on amino acid or nucleotide level
* Map reads to the non-redundant gene catalogue
* Use --min_length to filter for the minimum number of basepairs that must be aligned to keep a read
* Use --min_identity to filter for the minimum percentage of identity of mapped read to be kept
* Use --min_map to filter for the minimum percentage of a read that has to be mapped to be kept
* Create a gene count matrix based on a signature reads approach
* By default, MAGinator will redistribute ambiguous mapping reads based on the profile of uniquely mapping reads
* This can be changed with the --multi option.
* Pick non-redundant genes that are only found in one MAG cluster each
* Fit signature gene model and use the resulting signature genes to get the abundance of each MAG cluster
* Use --min_mapped_signature_genes to change minimum number of signature genes to be detected in the sample to be included in the analysis
* Use --min_samples to alter the number of samples with the MAG cluster present in order to perform signature gene refinement
* Prepare for generation of phylogenies for each MAG cluster by finding outgroups and marker genes which will be used for rooting the phylogenies
* Use the read mappings to collect SNV information for each signature gene and marker gene for each sample
* Align signature and marker genes, concatenate alignments and infer phylogenetic trees for each MAG cluster
* Use --phylo to toggle whether use fasttree (fast, approximate) or iqtree (slow, precise) to infer phylogenies
* Infer the taxonomic scope of each gene cluster. That is, at what taxonomic level are genes from a given gene cluster found in
* Use --tax_scope_threshold to toggle the threshold for how to find the taxonomic scope consensus
* Cluster gene clusters into synteny clusters based on how often they are found adjacent on contigs
## Output
* abundance/
* abundance_phyloseq.RData - Phyloseq object for R, with absolute abundance and taxonomic data
* clusters/
* <cluster>/<bin>.fa - Fasta files with nucleotide sequence of bins
* genes/
* all_genes.faa - Amino acid sequences of all ORFs
* all_genes.fna - Nucletotide sequences of all ORFs
* all_genes_nonredundant.fasta - Nucleotide sequences of gene cluster representatives
* all_genes_cluster.tsv - Gene clusters
* matrix/
* gene_count_matrix.tsv - Read count for each gene cluster for each sample
* small_gene_count_matrix.tsv - Read count matrix only containing the genes, that does not cluster across MAG cluster
* synteny/ - Intermediate files for synteny clustering of gene clusters
* gtdbtk/
* <cluster>/ - GTDB-tk taxonomic annotation for each VAMB cluster
* logs/ - Log files
* mapped_reads/
* bams/ - Bam files for mapping reads to gene clusters
* phylo/
* alignments/ - Alignments for each signature gene
* cluster_alignments/ - Concatenated alignments for each MAG cluster
* pileup/ - SNV information for each MAG cluster and each sample
* trees/ - Phylogenetic trees for each MAG cluster
* stats.tab - Mapping information such as non-N fraction, number of signature genes and marker genes, read depth, and number of bases not reaching allele frequency cutoff
* stats_genes.tab - Same as above but the information is split per gene
* signature_genes/
* \- R data files with signature gene optimization
* read-count_detected-genes.pdf - Figure for each MAG cluster displaying number of identified SG's in each sample along with the number of reads mapped.
* signature_reads/
* profiles - Read count profiles with ambiguous reads redistributed based on the uniquely mapped reads profile
* tabs/
* gene_cluster_bins.tab - Table listing which bins each gene cluster was found in
* gene_cluster_tax_scope.tab - Table listing the taxonomic scope of each gene cluster
* metagenomicspecies.tab - Table listing which, if any, clusters where merged in MAG cluster and the taxonomy of those
* signature_genes_cluster.tsv - Table with the signature genes for each MAG cluster
* synteny_clusters.tab - Table listing the synteny cluster association for the gene clusters. Gene clusters from the same synteny cluster are genomically adjacent.
* tax_matrix.tsv - Table with taxonomy information for MAG cluster
Raw data
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"author_email": "russel2620@gmail.com,trine_zachariasen@hotmail.com",
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"description": "[](http://www.repostatus.org/#active)\n\n# MAGinator\n\nCombining the strengths of contig and gene based methods to provide:\n\n* Accurate abundances of species using de novo signature genes\n * MAGinator uses a statistical model to find the best genes for calculating accurate abundances\n* SNV-level resolution phylogenetic trees based on signature genes\n * MAGinator creates a phylogenetic tree for each species so you can associate your metadata with subspecies/strain level differences\n* Connect accessory genome to the species annotation by getting a taxonomic scope for gene clusters\n * MAGinator clusters all ORFs into gene clusters and for each gene cluster you will know which taxonomic level it is specific to\n* Improve your functional annotation by grouping your genes in synteny clusters based on genomic adjacency\n * MAGinator clusters gene clusters into synteny clusters - Syntenic genes are usually part of the same pathway or have similar functions \n\n## Installation\n\nAll you need for running MAGinator is snakemake and mamba. Other dependencies will be installed by snakemake automatically.\n\n```sh\nconda create -n maginator -c bioconda -c conda-forge snakemake mamba\nconda activate maginator\npip install maginator\n```\n\nFurthermore, MAGinator also needs the GTDB-tk database downloaded. Here we download release 214. If you don't already have it, you can run the following:\n```sh\nwget https://data.ace.uq.edu.au/public/gtdb/data/releases/release214/214.1/auxillary_files/gtdbtk_r214_data.tar.gz\ntar xvzf *.tar.gz\n```\n\n## Usage\n\nMAGinator needs 3 input files:\n\n* The clusters.tsv files from [VAMB](https://github.com/RasmussenLab/vamb)\n* A fasta file with sequences of all contigs, with unique names\n* A comma-separated file giving the position of the fastq files with your sequencing reads formatted as: SampleName,PathToForwardReads,PathToReverseReads\n\nRun MAGinator:\n```sh\nmaginator -v vamb_clusters.tsv -r reads.csv -c contigs.fasta -o my_output -g \"/path/to/GTDB-Tk/database/release214/\"\n```\n\nA testset can be found in the test_data directory. \n1. Download the 3 samples used for the test at SRA: https://www.ncbi.nlm.nih.gov/sra?LinkName=bioproject_sra_all&from_uid=715601 with the ID's dfc99c_A, f9d84e_A and 221641_A\n2. Change the paths to the read-files in reads.csv\n3. Unzip the contigs.fasta.gz \n4. Run MAGinator\n\n### Run on a compute cluster\nMAGinator can run on compute clusters using qsub (torque), sbatch (slurm), or drmaa structures. The --cluster argument toggles the type of compute cluster infrastructure. The --cluster_info argument toggles the information given to the submission command, and it has to contain the following keywords {cores}, {memory}, {runtime}, which are used to forward resource information to the cluster.\n\nA qsub MAGinator can for example be run with the following command (... indicates required arguments, see above):\n```sh\nmaginator ... --cluster qsub --cluster_info \"-l nodes=1:ppn={cores}:thinnode,mem={memory}gb,walltime={runtime}\"\n```\n\n## Test data\n\nA test set can be found in the maginator/test_data directory. \n1. Download the 3 samples used for the test at SRA: https://www.ncbi.nlm.nih.gov/sra?LinkName=bioproject_sra_all&from_uid=715601 with the ID's dfc99c_A, f9d84e_A and 221641_A\n2. Clone repo: git clone https://github.com/Russel88/MAGinator.git\n3. Change the paths to the read-files in reads.csv\n4. Unzip the contigs.fasta.gz \n5. Run MAGinator\n\nMAGinator can been run on the test data on a slurm server with the following command:\n```sh\nmaginator --vamb_clusters clusters.tsv --reads reads.csv --contigs contigs.fasta --gtdb_db data/release214/ --output test_out --cluster slurm --cluster_info \"-n {cores} --mem {mem_gb}gb -t {runtime}\" --max_mem 180\n```\nThe expected output can be found as a zipped file on Zenodo: https://doi.org/10.5281/zenodo.8279036. MAGinator has been run on the test data (using GTDB-tk db release207_v2) on a slurm server.\n\nOn the compute cluster each job have had access to 180gb RAM, with the following time consumption: \nreal\t72m27.379s\nuser\t0m18.830s\nsys\t1m0.454s\n\nIf you run on a smaller server you can set the parameters --max_cores and --max_mem.\n\n## Recommended workflow \n\nTo generate the input files to run MAGinator we have created a recommended workflow, with preprocessing, assembly and binning* of your metagenomics reads (the rules for binning have been copied from VAMB (https://github.com/RasmussenLab/vamb/blob/master/workflow/)). \nIt has been setup as a snakefile in recommended_workflow/reads_to_bins.Snakefile.\n\nThe input to the workflow is the reads.csv file. The workflow can be run using snakemake:\n```\nsnakemake --use-conda -s reads_to_bins.Snakefile --resources mem_gb=180 --config reads=reads.csv --cores 10 --printshellcmds \n```\nOnce the binning is done, we recommend using a tool like dRep (https://github.com/MrOlm/drep) to create the species-level clusters. The advantage of dRep is that the clustering parameters can be modified to create clusters that belong to different taxonomic levels. An R script is located in recommended workflow/MAGinator_setup.R, that will create the different input files for MAGinator adapted to the output of dRep.\n\nPreparing data for MAGinator run\n```\nsed 's/@/_/g' assembly/all_assemblies.fasta > all_assemblies.fasta\nsed 's/@/_/g' vamb/clusters.tsv > clusters.tsv\n```\n\nNow you are ready to run MAGinator.\n\n## Functional Annotation\n\nTo generate the functional annotation of the genes we recommend using EggNOG mapper (https://github.com/eggnogdb/eggnog-mapper).\n\nYou can download it and try to run it on the test data\n```sh\nmkdir test_out/functional_annotation\nemapper.py -i test/genes/all_genes_rep_seq.fasta --output test_out/functional_annotation -m diamond --cpu 38\n```\n\nThe eggNOG output can be merged with clusters.tsv and further processed to obtain functional annotations of the MAG, cluster or sample levels with the following command:\n```sh\n(echo -e '#sample\\tMAG_cluster\\tMAG\\tfunction'; join -1 1 -2 1 <(awk '{print $2 \"\\t\" $1}' clusters.tsv | sort) <(tail -n +6 annotations.tsv | head -n -3 | cut -f1,15 | grep -v '\\-$' | sed 's/_[[:digit:]]\\+\\t/\\t/' | sed 's/,/\\n/g' | perl -lane '{$q = $F[0] if $#F > 0; unshift(@F, $q) if $#F == 0}; print \"$F[0]\\t$F[1]\"' | sed 's/\\tko:/\\t/' | sort) | awk '{print $2 \"\\t\" $2 \"\\t\" $3}' | sed 's/_/\\t/' | sort -k1,1 -k2,2n) > MAGfunctions.tsv\n```\nIn this case the KEGG ortholog column 15 was picked from the eggNOG-mapper output. But by cutting e.g. column number 13, one would obtain GO terms instead. Refer to the header of the eggNOG-mapper output for other available functional annotations e.g. KEGG pathways, Pfam, CAZy, COGs, etc.\n\n\n## MAGinator workflow\n\nThis is what MAGinator does with your input (if you want to see all parameters run maginator --help):\n* Filter bins by size\n * Use --binsize to control the cutoff\n* Run GTDB-tk to taxonomically annotate bins and call open reading frames (ORFs)\n* Group your VAMB clusters into metagenomic species (MGS) based on the taxonomic annotation. (Unannotated VAMB clusters are kept in the pipeline, but left unchanged)\n * Use --no_mgs to disable this\n * Use --annotation_prevalence to change how prevalent an annotation has to be in a VAMB cluster to call taxonomic consensus\n* Cluster your ORFs into gene clusters to get a non-redundant gene catalogue\n * Use --clustering_min_seq_id to toggle the clustering identity\n * Use --clustering_coverage to toggle the clustering coverage\n * Use --clustering_type to toggle whether to cluster on amino acid or nucleotide level\n* Map reads to the non-redundant gene catalogue \n * Use --min_length to filter for the minimum number of basepairs that must be aligned to keep a read\n * Use --min_identity to filter for the minimum percentage of identity of mapped read to be kept\n * Use --min_map to filter for the minimum percentage of a read that has to be mapped to be kept\n* Create a gene count matrix based on a signature reads approach\n * By default, MAGinator will redistribute ambiguous mapping reads based on the profile of uniquely mapping reads\n * This can be changed with the --multi option.\n* Pick non-redundant genes that are only found in one MAG cluster each\n* Fit signature gene model and use the resulting signature genes to get the abundance of each MAG cluster\n * Use --min_mapped_signature_genes to change minimum number of signature genes to be detected in the sample to be included in the analysis\n * Use --min_samples to alter the number of samples with the MAG cluster present in order to perform signature gene refinement\n* Prepare for generation of phylogenies for each MAG cluster by finding outgroups and marker genes which will be used for rooting the phylogenies\n* Use the read mappings to collect SNV information for each signature gene and marker gene for each sample\n* Align signature and marker genes, concatenate alignments and infer phylogenetic trees for each MAG cluster\n * Use --phylo to toggle whether use fasttree (fast, approximate) or iqtree (slow, precise) to infer phylogenies\n* Infer the taxonomic scope of each gene cluster. That is, at what taxonomic level are genes from a given gene cluster found in\n * Use --tax_scope_threshold to toggle the threshold for how to find the taxonomic scope consensus\n* Cluster gene clusters into synteny clusters based on how often they are found adjacent on contigs\n\n\n## Output\n\n* abundance/\n * abundance_phyloseq.RData - Phyloseq object for R, with absolute abundance and taxonomic data\n* clusters/\n * <cluster>/<bin>.fa - Fasta files with nucleotide sequence of bins\n* genes/\n * all_genes.faa - Amino acid sequences of all ORFs\n * all_genes.fna - Nucletotide sequences of all ORFs\n * all_genes_nonredundant.fasta - Nucleotide sequences of gene cluster representatives\n * all_genes_cluster.tsv - Gene clusters\n * matrix/\n * gene_count_matrix.tsv - Read count for each gene cluster for each sample\n * small_gene_count_matrix.tsv - Read count matrix only containing the genes, that does not cluster across MAG cluster\n * synteny/ - Intermediate files for synteny clustering of gene clusters\n* gtdbtk/\n * <cluster>/ - GTDB-tk taxonomic annotation for each VAMB cluster\n* logs/ - Log files\n* mapped_reads/\n * bams/ - Bam files for mapping reads to gene clusters\n* phylo/\n * alignments/ - Alignments for each signature gene\n * cluster_alignments/ - Concatenated alignments for each MAG cluster\n * pileup/ - SNV information for each MAG cluster and each sample\n * trees/ - Phylogenetic trees for each MAG cluster\n * stats.tab - Mapping information such as non-N fraction, number of signature genes and marker genes, read depth, and number of bases not reaching allele frequency cutoff \n * stats_genes.tab - Same as above but the information is split per gene\n* signature_genes/ \n * \\- R data files with signature gene optimization\n * read-count_detected-genes.pdf - Figure for each MAG cluster displaying number of identified SG's in each sample along with the number of reads mapped.\n* signature_reads/\n * profiles - Read count profiles with ambiguous reads redistributed based on the uniquely mapped reads profile\n* tabs/\n * gene_cluster_bins.tab - Table listing which bins each gene cluster was found in\n * gene_cluster_tax_scope.tab - Table listing the taxonomic scope of each gene cluster\n * metagenomicspecies.tab - Table listing which, if any, clusters where merged in MAG cluster and the taxonomy of those\n * signature_genes_cluster.tsv - Table with the signature genes for each MAG cluster\n * synteny_clusters.tab - Table listing the synteny cluster association for the gene clusters. Gene clusters from the same synteny cluster are genomically adjacent.\n * tax_matrix.tsv - Table with taxonomy information for MAG cluster\n \n\n\n",
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