cgMSI (Core Genome Metagenome Strain Identify), a tool to detect pathogen from nanopore metagenomic data within species at low abundance. cgMSI consists of two core modules:
- The cgMSI LIB module will create or update the library accroding to the file provided by the user.
- The cgMSI MAP module will identify the strain and estimate the abundance.
For any issues or concerns, please contact us at [email protected]
| Species name | Number of loci |
|---|---|
| Klebsiella pneumoniae | 2358 |
| Escherichia coli | 2531 |
| Enterococcus faecalis | 1972 |
| Listeria monocytogenes | 1701 |
| Pseudomonas aeruginosa | 3867 |
| Staphylococcus aureus | 1861 |
| Salmonella enterica | 3002 |
It is recommended to create a new conda environment:
conda create -n python37 python=3.7
# Activate this environment:
conda activate python37
• numpy (v1.15.0)
conda install -c conda-forge numpy
• pandas (v0.24.2)
conda install -c conda-forge pandas
• minimap2 (v2.22)
conda install -c bioconda minimap2
• pysam (v0.15.3)
conda install -c bioconda pysam
• seqkit (v2.0.0)
conda install -c bioconda seqkit
• scipy (v22.11.1)
conda install -c conda-forge scipy
First of all, we should:
- change directory (cd) to cgMSI folder
- cd into cgMSI directory
cd ../cgMSI python cgMSI.py -h
We have downloaded Klebsiella pneumoniae core gene allele pool and some reference genomes in dir ./test and added an example to show how to use cgMSI. Detailed parameter information follows this section. Firstly, generate related library by cgMSI LIB module.
cd ../cgMSI/
tar -zxvf ./test/Kp_alleles.tar.gz
python cgMSI.py LIB -species Kp -genomesDir ./test/testRef/ -allelePath ./test/Kp_alleles.fasta -alleleTable ./library/Klebsiella_pneumoniae_cgMLST_count.tsv -t 12 -outPutDir ./test/library/
Next, detection pathogen strain by cgMSI MAP module.
python cgMSI.py MAP -species Kp -t 12 -genomesDir ./test/testRef/ -allelePath ./test/Kp_alleles.fasta -sampleFile ./test/test_01X.fna -alleleTablePath ./library/Klebsiella_pneumoniae_cgMLST_count.tsv -genomeAlleleMatrix ./test/library/Kp.tsv -outPutDir ./test
The result can be found at dir ./test.
We need the database of strains, which can be downloaded from NCBI. Also you can add your own genomes to the folder. First you need to make sure that genomes belonging to the same species are in one folder, different species are in different folders. The allele table and specise alleles file were download from https://www.cgmlst.org/ncs . We download 7 specises' allele tables in ./library that can use directly.The target specise allele file you can download from the website and merge all loci into a fasta file. create a new library for a species:
python cgMSI.py LIB -genomesDir genomeDIR -allelePath species_alleles.fasta -alleleTable speciesAlleleTable -species speciesName -t threadNumber
Required arguments:
-genomesDir, string Target species Reference Genome Directory Full Path
-allelePath, string alleles fasta file,can be download
-alleleTable, string path of the target specise allele table
-species, string species name with No whitespace(if Escherichia coli ,like Ec) for distinguish different species
-outPutDir string the dir of the library (default at ./cgMSI/library/)
Optional arguments:
-t, int Number of threads to use by aligner (bowtie2) if different from default (12)
add a genome to a existed species library:
python cgMSI.py LIB -addGenome -genomesDir genomeDIR -allelePath species_alleles.fasta -alleleTable speciesAlleleTable -species speciesName -genomeName addGenomeName -genomeFile addGenomeFastaFile -t threadNumber
Required arguments:
-genomesDir, string directory Full Path of target species Reference Genome
-allelePath, string alleles fasta file,can be download
-alleleTable, string path of the target specise allele table
-species, string species name with No whitespace(if Escherichia coli ,like Ec) for distinguish different species
-genomeName string the name of the genome added into the library
-genomeFile string full path of the added genome fasta file
-outPutDir string the dir of the library (default at ./cgMSI/library/)
Optional arguments:
-t, int Number of threads to use by aligner (bowtie2) if different from default (8)
First you need to make sure that the LIB module is finished. MAP module will use library generated previously with LIB module.
call MAP module help for details
python cgMSI.py MAP -h
python cgMSI.py MAP -genomesDir genomeDIR -allelePath species_alleles.fasta -alleleTable speciesAlleleTable -species speciesName -sampleFile sampleFile -outPutDir outPutDir -t threadNumber
Required arguments:
-genomesDir, string directory Full Path of target species Reference Genome
-allelePath, string alleles fasta file,can be download
-alleleTable, string full path of the target specise allele table
-species, string species name with No whitespace(if Escherichia coli ,like Ec) for distinguish different species
-genomeName string the name of the genome added into the library
-genomeFile string full path of the added genome fasta file
-sampleFile string full path of sample file(fasta or fastq)
-outPutDir string the dir of the predict result
Optional arguments:
-t, int Number of threads to use by aligner (bowtie2) if different from default (8)