raw-lab / mercat2

MerCat2: python code for versatile k-mer counting and diversity estimation for database independent property analysis for metaome data

MerCat2: python code for versatile k-mer counter for database independent property analysis (DIPA) for omic analysis

Installing MerCat2

Option 1: Bioconda Installer

Install mamba using conda

conda activate base
conda install mamba

• NOTE: Make sure you install mamba in your base conda environment
• We have found that mamba is faster than conda for installing packages and creating environments. Using conda might fail to resolve dependencies.

Install MerCat2

• Available via Bioconda:

mamba create -n mercat2 -c conda-forge -c bioconda mercat2
conda activate mercat2

Option 2: Source Installer

• Clone mercat2 from github
• Run install_mercat2.sh to install all required dependencies
• This script creates a conda environment for you

git clone https://github.com/raw-lab/mercat2.git
cd mercat2
bash install_mercat2.sh
conda activate mercat2

Dependencies

MerCat2 runs on python version 3.9 and higher.

external dependencies

MerCat2 can run without external dependencies based on the options used.

Required dependencies:

• When a raw read .fastq file is given

  • Fastqc
  • Fastp

• For bacteria/archaea rich samples (-prod option)

  • Prodigal

• For eukaryote rich samples or general applications (-fgs option)

  • FragGeneScanRs

These are available through BioConda, except FragGeneScanRS, which is included in the MerCat2 distribution.

conda install -c bioconda fastqc fastp prodigal

Usage

• -i I path to input file
• -f F path to folder containing input files
• -k K k-mer length
• -n N no of cores [default = all]
• -c C minimum k-mer count [default = 10]
• -prod run prodigal on nucleotide assembled contigs
  • Must be one of ['.fa', '.fna', '.ffn', '.fasta', 'fastq']
• -fgs run FragGeneScanRS on nucleotide assembled contigs
  • Must be one of ['.fa', '.fna', '.ffn', '.fasta', 'fastq']
• -s S split files into chunks of S size, in MB [default is 100MB]
• -skipclean skips the trimming steps [default = False]
• -toupper convert all input sequences to uppercase [default = False]
• -o output folder [default is 'mercat_results' in the current working directory]
• -h, --help show this help message

Mercat assumes the input file format based on the extension provided

• raw fastq file: ['.fastq', '.fq']
• nucleotide fasta: ['.fa', '.fna', '.ffn', '.fasta']
• amino acid fasta: ['.faa']

Usage examples

Run mercat2 on a protein file (protein fasta - '.faa')

mercat2.py -i file-name.faa -k 3 -c 10

Run mercat2 on a nucleotide file (nucleotide fasta - '.fa', '.fna', '.ffn', '.fasta')

mercat2.py -i file-name.fna -k 3 -n 8 -c 10

Run mercat2 on a nucleotide file raw data (nucleotide fastq - '.fastq')

mercat2.py -i file-name.fastq -k 3 -n 8 -c 10

Run on many samples within a folder

mercat2.py -f /path/to/input-folder -k 3 -n 8 -c 10

Run on sample with prodigal/FragGeneScanRS option (raw reads or nucleotide contigs - '.fa', '.fna', '.ffn', '.fasta', '.fastq')

mercat2.py -i /path/to/input-file -k 3 -n 8 -c 10 -prod

mercat2.py -i /path/to/input-file -k 3 -n 8 -c 10 -fgs

• the prodigal and FragGeneScanRS options run the k-mer counter on both contigs and produced amino acids

Outputs

• Results are stored in the output folder (default 'mercat_results' of the current working directory)
  • the 'report' folder contains an html report with interactive plotly figures
    • If at least 4 samples are provided a PCA plot will be included in the html report
  • the 'tsv' folder contains counts tables in tab separated format
    • if protein files are given, or the -prod option, a .tsv file is created for each sample containing k-mer count, pI, Molecular Weight, and Hydrophobicity metrics
    • if nucleotide files are given a .tsv file is created for each sample containing k-mer count and GC content
  • if .fastq raw reads files are used, a 'clean' folder is created with the clean fasta file.
  • if the -prod option is used, a 'prodigal' folder is created with the amino acid .faa and .gff files
  • if the -fgs option is used, a 'fgs' folder is created with the amino acid .faa file

Diversity estimation

Alpha diversity metrics provided by MerCat2 are experimental. We are currently working on the robustness of these measures.

Notes on memory usage and speed

MerCat2 uses a substantial amount of memory when the k-mer is high.
Running MerCat2 on a personal computer using a k-mer length of ~4 should be OK. Total memory usage can be reduced using the Chunker feature (-s option), but keep in mind that in testing when the chunk size is too small (1MB) some of the least significant k-mers will get lost. This does not seem to affect the overall results, but it is something to keep in mind. Using the chunker and reducing the number of CPUs available (-n option) can help reduce memory requirements.

The speed of MerCat2 can be increased when more memory or computer nodes are available on a cluster and using a chunk size of about 100Mb.

Citing Mercat

If you are publishing results obtained using MerCat2, please cite:
Figueroa JL, Panyala A, Colby S, Friesen M, Tiemann L, White III RA. 2022.
MerCat2: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from omics data. bioRxiv.
paper

CONTACT

Please send all queries to Jose Luis Figueroa
Dr. Richard Allen White III
Or open an issue