Marker-MAGu

Trans-Kingdom Marker Gene Pipeline for Taxonomic Profiling of Human Metagenomes

If you want to detect and quantify phages, bacteria, and archaea in whole genome shotgun reads from human-derived samples, Marker-MAGu is the tool for you.

Basically, this tool uses the strategy (marker gene detection) and database (marker gene sequences) from Metaphlan4, then:

1. adds marker genes from 10s of thousands of phages derived from human metagenomes
2. tweaks the thresholds so that phages and bacteria are detected with similar specificity and sensitivity

The relative abundance of bacteria/archaea with be highly similar to Metaphlan4(default settings) if Marker-MAGu is run with --detection relaxed (33% of marker genes required for detection). With Marker-MAGu --detection default output will be a bit less sensitive and a bit more specific, using a stricter threshold (75% of marker genes required for detection). Changes in settings and databases will likely change the output

Also, as in Metaphlan4 SGBs, or Species-level Genome Bins are genomically-distinct species.

Logo by Adrien Assie

Schematic

Installation

Conda steps

This will only work in Linux. If you only have access to another OS, you must use the Container steps (e.g. Docker, Singularity, Podman). See below.

1. Create a new environment and install with conda. Tested with conda version 23.5.0

conda create -n marker-magu -c bioconda marker-magu

2. Download the database (~9.9 GB when decompressed).

cd Marker-MAGu or cd to where you want the database to reside

mkdir DBs && cd DBs

wget https://zenodo.org/records/8342581/files/Marker-MAGu_markerDB_v1.1.tar.gz

md5sum Marker-MAGu_markerDB_v1.1.tar.gz

should return e0947cb1d4a3df09829e98627021e0dd

tar -xvf Marker-MAGu_markerDB_v1.1.tar.gz

rm Marker-MAGu_markerDB_v1.1.tar.gz

3. Set the Marker-MAGu database directory variable MARKERMAGU_DB, e.g.:

conda env config vars set MARKERMAGU_DB=/path/to/DBs/v1.1

Container (Docker, Singularity, Podman)

* be sure to mount your volumes/directories with the `Marker-MAGu` database as well as those with input read files 

* I believe you can save environmental variables like MARKERMAGU_DB in `Docker` containers

(OPTIONAL) Database for filtering out host reads and spike-ins

You could filter unwanted sequences out upstream of this tool, but this will allow you to do it within Marker-MAGu using minimap2. The pipeline script will look for a file at filter_seqs/filter_seqs.fna which could be any fasta-formatted sequence file you want to use to remove matching reads (e.g. from host or spike-in).

Here are instructions for downloading and formatting the human genome and phiX spike-in (3 GB decompressed).

cd Marker-MAGu ### or `cd` to where you want the filter_seqs to reside

mkdir filter_seqs && cd filter_seqs

## download phiX genome and gunzip
wget https://ftp.ncbi.nlm.nih.gov/genomes/refseq/viral/Sinsheimervirus_phiX174/latest_assembly_versions/GCF_000819615.1_ViralProj14015/GCF_000819615.1_ViralProj14015_genomic.fna.gz

gunzip GCF_000819615.1_ViralProj14015_genomic.fna.gz

## download human genome and gunzip
wget https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/latest_assembly_versions/GCF_009914755.1_T2T-CHM13v2.0/GCF_009914755.1_T2T-CHM13v2.0_genomic.fna.gz

gunzip GCF_009914755.1_T2T-CHM13v2.0_genomic.fna.gz

## concatenate files
cat GCF_000819615.1_ViralProj14015_genomic.fna GCF_009914755.1_T2T-CHM13v2.0_genomic.fna > filter_seqs.fna

## set the MARKERMAGU_FILTER variable in your conda environment

conda env config vars set MARKERMAGU_FILTER=/path/to/filter_seqs

## optionally delete separate files
rm GCF_000819615.1_ViralProj14015_genomic.fna GCF_009914755.1_T2T-CHM13v2.0_genomic.fna

Remember to set -f True to run the filtering step.

Running the tool

Linux-only for the conda environment

You might run this as part of a bash script, do your own upstream read processing, etc, but these are the basic instructions.

Required inputs:

-r reads file(s) (.fastq)

-s sample name (no space characters)

-o output directory (may be shared with other samples)

Activate the conda environment:

conda activate marker-magu

Individual samples can be run with the python script. E.g.:

Basic run with 1 .fastq file:

markermagu -r /path/to/reads/myreads.fastq -s sample_ABC -o myproject_MM1

Using multiple input .fastq files (Marker-MAGu doesn’t used paired-end info)

markermagu -r /path/to/reads/myreads1.fastq /path/to/reads/myreads2.fastq -s sample_ABC -o myproject_MM1

Using multiple input .fastq files with wildcard

markermagu -r /path/to/reads/*.fastq -s sample_ABC -o myproject_MM1

With pre-filtering steps:

markermagu -r /path/to/reads/myreads.fastq -s sample_ABC -o myproject_MM1 -q True -f True

With relaxed detection

markermagu -r /path/to/reads/myreads.fastq -s sample_ABC -o myproject_MM1 --detection relaxed

Help menu

markermagu -h

Full help menu

usage: markermagu [-h] -r READS [READS ...] -s SAMPLE -o OUTPUT_DIR [--version] [-t CPU] [-q QUAL]
                  [-f FILTER_SEQS] [--filter_dir FILTER_DIR] [--temp TEMP_DIR] [--keep KEEP] [--db DB]
                  [--detection {default,relaxed}]

Marker-MAGu is a read mapping pipeline which uses marker genes to detect and measure bacteria, phages,
archaea, and microeukaryotes. Version 0.4.0

options:
  -h, --help            show this help message and exit

 REQUIRED ARGUMENTS for Marker-MAGu :
  -r READS [READS ...], --reads READS [READS ...]
                        read file(s) in .fastq format. You can specify more than one separated by a
                        space
  -s SAMPLE, --sample SAMPLE
                        Sample name. No space characters, please.
  -o OUTPUT_DIR, --output_dir OUTPUT_DIR
                        Output directory name. Will be created if it does not exist. Can be shared
                        with other samples. No space characters, please.

 OPTIONAL ARGUMENTS for Marker-MAGu.:
  --version             show program's version number and exit
  -t CPU, --cpu CPU     Default: 48 -- Example: 32 -- Number of CPUs available for Marker-MAGu.
  -q QUAL, --qual QUAL  True or False. Remove low-quality reads with fastp?
  -f FILTER_SEQS, --filter_seqs FILTER_SEQS
                        True or False. Remove reads aligning to sequences at
                        filter_seqs/filter_seqs.fna ?
  --filter_dir FILTER_DIR
                        path to directory of sequences to filter. If not set, Marker-MAGu looks for
                        environmental variable MARKERMAGU_FILTER. Then, if this variable is unset, it
                        this is unset, DB path is assumed to be
                        /cmmr/apps/conda_users/u241374/envs/marker-magu/lib/python3.11/site-
                        packages/markermagu
  --temp TEMP_DIR       path of temporary directory. Default is {OUTPUT_DIR}/{SAMPLE}_temp/
  --keep KEEP           True of False. Keep the intermediate files, located in the temporary
                        directory? These can add up, so it is not recommended if space is a concern.
  --db DB               DB path. If not set, Marker-MAGu looks for environmental variable
                        MARKERMAGU_DB. Then, if this variable is unset, it this is unset, DB path is
                        assumed to be /cmmr/apps/conda_users/u241374/envs/marker-
                        magu/lib/python3.11/site-packages/markermagu
  --detection {default,relaxed}
                        Stringency of SGB detection. "default" setting requires >=75 percent of marker
                        genes with at least 1 read mapped. "relaxed" setting requires >= 33.3 percent
                        of marker genes with at least 1 read mapped AND at least 3 marker genes
                        detected.

Combine Output tables in a Project Directory

The project directory should have multiple *.detected_species.tsv tables.

Activate conda environment: conda activate marker-magu

Then, run Rscript with the project directory as the first and only argument:

Rscript /path/to/Marker-MAGu/src/markermagu/combine_sample_tables1.R myproject_MM1

This command will generate the table myproject_MM1.combined_profile.tsv.

Notes for Users

Resource Usage

In my experience, the step which uses minimap2 to align reads to the marker gene database uses about 66GB of memory.

Output table

Marker-MAGu outputs a long form table, and the combined outputs (from combine_sample_tables1.R) are long form. This allows for a smaller file size and nimbler downstream analysis, in my opinion. Major bioinformatics coding languages can convert long form tables to wide form.

An example in R using Tidyverse packages:

library(data.table)
library(stringr)
library(dplyr)
library(tidyr)

long_dt <- fread("myproject_MM1.combined_profile.tsv", 
                  sep = "\t", header = T) %>%
  mutate(SGB = gsub(".*s__","s__", lineage ), 
  genus = gsub(".*g__","g__", lineage ), 
  genus = gsub("\|s__.*", "", genus),
  kingdom = gsub("\|.*", "", lineage))

wide_dt <- long_dt %>%
  subset( select = c("SGB", "rel_abundance", "sampleID")) %>%
  pivot_wider(names_from = sampleID, 
              values_from = rel_abundance, values_fill = 0)

Trove of Gut Virus Genomes

See details and download full genome database on Zenodo:

Link

Host predictions, virulence, etc. for phages

Please see the file Marker-MAGu_virus_DB_v1.1_metadata.tsv which is downloaded with the Marker-MAGu database. You can merge this table and any Marker-MAGu output table on the “lineage” column.

Source for virus taxonomy

Other notes

Relative abundance values from each sample will add up to 1, so there is no “unknown fraction estimate”

Currently, Marker-MAGu only profiles phages with 4 or more unique marker genes, so small, ssDNA phages such as microviruses and inoviruses may be undetectable. I’m working on it.

Should any issues arise, please leave an issue in this GitHub Repo.

Adding your own virus hallmark genes to Marker-MAGu

A command line walkthrough for users wishing to add their own viruses to the Marker-MAGu DB. While Marker-MAGu itself is pretty simple, it can be a bit involved to make and format marker genes. But, hey, let’s do it.

Input

Multi-fasta file with genome sequences of viruses. If you have a very large file (thousands of genomes), the hmmscan and BLASTN steps may take a while.

In this walkthrough, we will use my_viruses1.fna, so we will set a variable with the stem of this file name:

MYSEQS="my_viruses1"

Package/tool versions used

It might be easiest to install these in a new conda environment. Tool versions might not matter too much.

seqkit v2.2.0
bioawk v1.0
prodigal v2.6.3
hmmer v3.3
blast v2.9.0

HMMER database for virus hallmark gene identification

This is the HMMER database used by Cenote-Taker 2 for virus identification

1) navigate to a new directory for processing data

2) download DB

wget https://zenodo.org/records/4966268/files/hmmscan_DBs.tgz

3) unpack

tar -xvf hmmscan_DBs.tgz

Call ORFs, Identify hallmark genes

1) call genes

make sure variable is set for your file MYSEQS="my_viruses1"

prodigal -a ${MYSEQS}.prot.faa -d ${MYSEQS}.genes.fna -i ${MYSEQS}.fna -p meta -q

2) hmmscan virus virion and replication HMMER databases

hmmscan --tblout ${MYSEQS}.hmmscan_virion.out --cpu 16 -E 1e-8 --noali hmmscan_DBs/virus_specific_baits_plus_missed6a ${MYSEQS}.prot.faa

then

hmmscan --tblout ${MYSEQS}.hmmscan_replicate.out --cpu 16 -E 1e-15 --noali hmmscan_DBs/virus_replication_clusters3 ${MYSEQS}.prot.faa

3) combine tables

cat ${MYSEQS}.hmmscan_virion.out ${MYSEQS}.hmmscan_replicate.out | grep -v "^#" | sed 's/ /```

cut -f3 $MYSEQS\mathrm{.}al{l}_{h}mmsca{n}_{f}mt\mathrm{.}out>\{MYSEQS\}.all\_hmmscan\_fmt.out\; >${MYSEQS}.hallmark_gene_list.txt

**4) Retreive hallmark genes**

seqkit grep -j 16 -f $MYSEQS\mathrm{.}hallmar{k}_{g}en{e}_{l}ist\mathrm{.}txt\{MYSEQS\}.hallmark\_gene\_list.txt${MYSEQS}.genes.fna > ${MYSEQS}.hallmark_genes.nucl.fna

seqkit grep -j 16 -f $MYSEQS\mathrm{.}hallmar{k}_{g}en{e}_{l}ist\mathrm{.}txt\{MYSEQS\}.hallmark\_gene\_list.txt${MYSEQS}.prot.faa > ${MYSEQS}.hallmark_genes.prot.faa

### Parse genomes

**1) Get list of genomes with minimum number of hallmark genes**

This command will only keep genomes with 4 or more hallmark genes as is recommended for `Marker-MAGu`. See awk expression `if ($1>=4)`.

sed ‘s/[^_]*$\mathrm{/}{\mathrm{/}}^{\mathrm{\prime }}//\text{'}${MYSEQS}.hallmark_gene_list.txt | sort | uniq -c | awk ‘{if (1>=4) {print2}}’ > ${MYSEQS}.marker_genomes_list.txt

**2) Make fastas of genome-specific marker genes**

mkdir indiv_genomes

cat $MYSEQS\mathrm{.}marke{r}_{g}enome{s}_{l}ist\mathrm{.}txt\mathrm{\mid }xargs-n1-I-P16seqkitgrep-j1--quiet-r-p\mathrm{"}\mathrm{"}\{MYSEQS\}.marker\_genomes\_list.txt\; |\; xargs\; -n\; 1\; -I\; \{\}\; -P\; 16\; seqkit\; grep\; -j\; 1\; --quiet\; -r\; -p\; "\{\}"${MYSEQS}.hallmark_genes.nucl.fna -o indiv_genomes/{}_hmg.fna

**3) Concatenate hallmark genes for each genome**

HMGS=( find indiv_genomes/ -type f -name "*__hmg.fna" | sed 's/\.fna//g' ) if [ -n "HMGS” ] ; then for HM in $HMGS;dobioawk-vgenq=\mathrm{"}HMGS\; ;\; do\; bioawk\; -v\; genq="${HM#indiv_genomes/}” -c fastx ‘{if (NR == 1) {print “>”genq ; print seq} else {printseq }}’ $HM\mathrm{.}fna>\{HM\}.fna\; >${HM}.concat.fna ; done else echo “hallmark gene files for each genome NOT found” fi

cat indiv_genomes/*_hmg.concat.fna > ${MYSEQS}.hallmark_genes.nucl.concat.fna

### Compare concatenated hallmark gene sequences against each other to dereplicate redundant sequences

NOTE: If all-vs-all BLASTN only returns self-alignments, the `anicalc.py` script will return an error. In this case, just use the `${MYSEQS}.marker_genomes_list.txt` file instead of the `${MYSEQS}.hallmark_genes.nucl.concat.exemplars1.txt` for step 3).

**1) BLASTN**

mkdir blast_DBs

makeblastdb -in $MYSEQS\mathrm{.}hallmar{k}_{g}enes\mathrm{.}nucl\mathrm{.}concat\mathrm{.}fna-dbtypenucl-outblas{t}_{D}Bs\mathrm{/}\{MYSEQS\}.hallmark\_genes.nucl.concat.fna\; -dbtype\; nucl\; -out\; blast\_DBs/${MYSEQS}.hallmark_genes.nucl.concat

blastn -query $MYSEQS\mathrm{.}hallmar{k}_{g}enes\mathrm{.}nucl\mathrm{.}concat\mathrm{.}fna-dbblas{t}_{D}Bs\mathrm{/}\{MYSEQS\}.hallmark\_genes.nucl.concat.fna\; -db\; blast\_DBs/${MYSEQS}.hallmark_genes.nucl.concat -outfmt ‘6 std qlen slen’ -max_target_seqs 10000 -perc_identity 90 -out ${MYSEQS}.hallmark_genes.nucl.concat.blastn.tsv -num_threads 16

**2) anicalc/aniclust to find redundant sequences**

python /path/to/Marker-MAGu/src/markermagu/utils/anicalc.py -i $MYSEQS\mathrm{.}hallmar{k}_{g}enes\mathrm{.}nucl\mathrm{.}concat\mathrm{.}blastn\mathrm{.}tsv-o\{MYSEQS\}.hallmark\_genes.nucl.concat.blastn.tsv\; -o${MYSEQS}.hallmark_genes.nucl.concat.anicalc.tsv

python /path/to/Marker-MAGu/src/markermagu/utils/aniclust.py –fna $MYSEQS\mathrm{.}hallmar{k}_{g}enes\mathrm{.}nucl\mathrm{.}concat\mathrm{.}fna--ani\{MYSEQS\}.hallmark\_genes.nucl.concat.fna\; --ani${MYSEQS}.hallmark_genes.nucl.concat.anicalc.tsv –out ${MYSEQS}.hallmark_genes.nucl.concat.aniclust.ANI95_TCOV85.tsv –min_ani 95 –min_tcov 85 –min_qcov 0

cut -f1 $MYSEQS\mathrm{.}hallmar{k}_{g}enes\mathrm{.}nucl\mathrm{.}concat\mathrm{.}aniclust\mathrm{.}ANI95_{T}COV85.tsv>\{MYSEQS\}.hallmark\_genes.nucl.concat.aniclust.ANI95\_TCOV85.tsv\; >${MYSEQS}.hallmark_genes.nucl.concat.exemplars1.txt

**3) Retreive all the INDIVIDUAL hallmark genes for exemplars**

bioawk -c fastx ‘{print $name,name,$seq}’ $MYSEQS\mathrm{.}hallmar{k}_{g}enes\mathrm{.}nucl\mathrm{.}fna\mathrm{\mid }grep-F-f\{MYSEQS\}.hallmark\_genes.nucl.fna\; |\; grep\; -F\; -f${MYSEQS}.hallmark_genes.nucl.concat.exemplars1.txt | awk ‘{OFS=FS=”\t”}{print “>” $1;print1\; ;\; print$2}’ > ${MYSEQS}.hallmark_genes.nucl.exemplars1.fna

### Add marker genes to Marker-MAGu

**1) Format marker genes for `Marker-MAGu`**

This is kind of an unrefined piece of code as it does not assign meaningful taxonomical assignments at any level except species ("s\_\_"), which it merely assigns the sequence name. However, it does fulfill the requirements for `Marker-MAGu`, which is to format the header as:

*gene_name;hierarchical_taxonomy;genome_of_origin_name*

sed ‘s/[^_]*/@_&/ ; s/^@_//g'{MYSEQS}.hallmark_genes.nucl.exemplars1.fna | bioawk -c fastx ‘{ split(name, a, "_@") ; print ">"a[1]a[2]";k__Viruses|p__Unclassified_viruses|c__Unclassified_viruses|o__Unclassified_viruses|f__Unclassified_viruses|g__Unclassified_viruses|s__vOTU_"a[1]";"a[1] ; printseq}’ > ${MYSEQS}.hallmark_genes.nucl.exemplars1.fmt.fna

If you want to assign more complex taxonomy to your sequences based on some other tool, please make an Issue and I can upload some additional scripts to the repo to facilitate this.

**2) Concatenate your formatted marker genes to the existing `Marker-MAGu` database**

cd Marker-MAGu/DBs

mkdir v_${MYSEQS}

cat v1.1/Marker-MAGu_markerDB.fna /path/to/{MYSEQS}.hallmark_genes.nucl.exemplars1.fmt.fna > v_{MYSEQS}/Marker-MAGu_markerDB.fna

That's it. Remember to specify the updated database when running `Marker-MAGu`. E.g. `--db v_my_viruses1`

## Citation

(insert)
\t/g' | sort -u -k3,3 > ${MYSEQS}.all_hmmscan_fmt.out

cut -f3 ${MYSEQS}.all_hmmscan_fmt.out > ${MYSEQS}.hallmark_gene_list.txt

4) Retreive hallmark genes

seqkit grep -j 16 -f ${MYSEQS}.hallmark_gene_list.txt ${MYSEQS}.genes.fna > ${MYSEQS}.hallmark_genes.nucl.fna

seqkit grep -j 16 -f ${MYSEQS}.hallmark_gene_list.txt ${MYSEQS}.prot.faa > ${MYSEQS}.hallmark_genes.prot.faa

Parse genomes

1) Get list of genomes with minimum number of hallmark genes

This command will only keep genomes with 4 or more hallmark genes as is recommended for Marker-MAGu. See awk expression if ($1>=4).

sed 's/[^_]*$//' ${MYSEQS}.hallmark_gene_list.txt | sort | uniq -c | awk '{if ($1>=4) {print $2}}' > ${MYSEQS}.marker_genomes_list.txt

2) Make fastas of genome-specific marker genes

mkdir indiv_genomes

cat ${MYSEQS}.marker_genomes_list.txt | xargs -n 1 -I {} -P 16 seqkit grep -j 1 --quiet -r -p "{}" ${MYSEQS}.hallmark_genes.nucl.fna -o indiv_genomes/{}_hmg.fna

3) Concatenate hallmark genes for each genome

HMGS=$( find indiv_genomes/ -type f -name "*__hmg.fna" | sed 's/\.fna//g' )
if [ -n "$HMGS" ] ; then
    for HM in $HMGS ; do 
        bioawk -v genq="${HM#indiv_genomes/}" -c fastx '{if (NR == 1) {print ">"genq ; print $seq} else {print $seq }}' ${HM}.fna > ${HM}.concat.fna ; 
    done
else
    echo "hallmark gene files for each genome NOT found"
fi

cat indiv_genomes/*_hmg.concat.fna > ${MYSEQS}.hallmark_genes.nucl.concat.fna

Compare concatenated hallmark gene sequences against each other to dereplicate redundant sequences

NOTE: If all-vs-all BLASTN only returns self-alignments, the anicalc.py script will return an error. In this case, just use the ${MYSEQS}.marker_genomes_list.txt file instead of the ${MYSEQS}.hallmark_genes.nucl.concat.exemplars1.txt for step 3).

1) BLASTN

mkdir blast_DBs

makeblastdb -in ${MYSEQS}.hallmark_genes.nucl.concat.fna -dbtype nucl -out blast_DBs/${MYSEQS}.hallmark_genes.nucl.concat

blastn -query ${MYSEQS}.hallmark_genes.nucl.concat.fna -db blast_DBs/${MYSEQS}.hallmark_genes.nucl.concat -outfmt '6 std qlen slen' -max_target_seqs 10000 -perc_identity 90 -out ${MYSEQS}.hallmark_genes.nucl.concat.blastn.tsv -num_threads 16

2) anicalc/aniclust to find redundant sequences

python /path/to/Marker-MAGu/src/markermagu/utils/anicalc.py -i ${MYSEQS}.hallmark_genes.nucl.concat.blastn.tsv -o ${MYSEQS}.hallmark_genes.nucl.concat.anicalc.tsv

python /path/to/Marker-MAGu/src/markermagu/utils/aniclust.py --fna ${MYSEQS}.hallmark_genes.nucl.concat.fna --ani ${MYSEQS}.hallmark_genes.nucl.concat.anicalc.tsv --out ${MYSEQS}.hallmark_genes.nucl.concat.aniclust.ANI95_TCOV85.tsv --min_ani 95 --min_tcov 85 --min_qcov 0

cut -f1 ${MYSEQS}.hallmark_genes.nucl.concat.aniclust.ANI95_TCOV85.tsv > ${MYSEQS}.hallmark_genes.nucl.concat.exemplars1.txt

3) Retreive all the INDIVIDUAL hallmark genes for exemplars

bioawk -c fastx '{print $name, $seq}' ${MYSEQS}.hallmark_genes.nucl.fna | grep -F -f ${MYSEQS}.hallmark_genes.nucl.concat.exemplars1.txt | awk '{OFS=FS="\t"}{print ">" $1 ; print $2}' > ${MYSEQS}.hallmark_genes.nucl.exemplars1.fna

Add marker genes to Marker-MAGu

1) Format marker genes for Marker-MAGu

This is kind of an unrefined piece of code as it does not assign meaningful taxonomical assignments at any level except species (“s__“), which it merely assigns the sequence name. However, it does fulfill the requirements for Marker-MAGu, which is to format the header as:

gene_name;hierarchical_taxonomy;genome_of_origin_name

sed 's/[^_]*$/@_&/ ; s/^@_//g' ${MYSEQS}.hallmark_genes.nucl.exemplars1.fna | bioawk -c fastx '{ split($name, a, "_@") ; print ">"a[1]a[2]";k__Viruses|p__Unclassified_viruses|c__Unclassified_viruses|o__Unclassified_viruses|f__Unclassified_viruses|g__Unclassified_viruses|s__vOTU_"a[1]";"a[1] ; print $seq}' > ${MYSEQS}.hallmark_genes.nucl.exemplars1.fmt.fna

If you want to assign more complex taxonomy to your sequences based on some other tool, please make an Issue and I can upload some additional scripts to the repo to facilitate this.

2) Concatenate your formatted marker genes to the existing Marker-MAGu database

cd Marker-MAGu/DBs

mkdir v_${MYSEQS}

cat v1.1/Marker-MAGu_markerDB.fna /path/to/${MYSEQS}.hallmark_genes.nucl.exemplars1.fmt.fna > v_${MYSEQS}/Marker-MAGu_markerDB.fna

That’s it. Remember to specify the updated database when running Marker-MAGu. E.g. --db v_my_viruses1

Citation

(insert)