Package: mercat2
MerCat2: python code for versatile k-mer counter and diversity estimator for database independent property analysis (DIPA) for omic analysisInstalling MerCat2Option 1: Bioconda InstallerInstall mamba using condacondaactivatebase
condainstallmambaNOTE: Make sure you install mamba in your base conda environmentWe have found that mamba is faster than conda for installing packages and creating environments. Using conda might fail to resolve dependencies.Install MerCat2Available via Bioconda:mambacreate-nmercat2-cconda-forge-cbiocondamercat2
condaactivatemercat2Option 2: Source InstallerClone mercat2 from githubRun install_mercat2.sh to install all required dependenciesThis script creates a conda environment for yougitclonehttps://github.com/raw-lab/mercat2.gitcdmercat2
bashinstall_mercat2.sh
condaactivatemercat2DependenciesMerCat2 runs on python version 3.9 and higher.external dependenciesMerCat2 can run without external dependencies based on the options used.Required dependencies:When a raw read .fastq file is givenFastqcFastpFor bacteria/archaea rich samples (-prod option)ProdigalFor eukaryote rich samples or general applications (-fgs option)FragGeneScanRsThese are available through BioConda, except FragGeneScanRS, which is included in the MerCat2 distribution.condainstall-cbiocondafastqcfastpprodigalUsage-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 contigsMust be one of ['.fa', '.fna', '.ffn', '.fasta', 'fastq']-fgs run FragGeneScanRS on nucleotide assembled contigsMust 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 messageMercat assumes the input file format based on the extension providedraw fastq file: ['.fastq', '.fq']nucleotide fasta: ['.fa', '.fna', '.ffn', '.fasta']amino acid fasta: ['.faa']Usage examplesRun mercat2 on a protein file (protein fasta - '.faa')mercat2.py-ifile-name.faa-k3-c10Run mercat2 on a nucleotide file (nucleotide fasta - '.fa', '.fna', '.ffn', '.fasta')mercat2.py-ifile-name.fna-k3-n8-c10Run mercat2 on a nucleotide file raw data (nucleotide fastq - '.fastq')mercat2.py-ifile-name.fastq-k3-n8-c10Run on many samples within a foldermercat2.py-f/path/to/input-folder-k3-n8-c10Run on sample with prodigal/FragGeneScanRS option (raw reads or nucleotide contigs - '.fa', '.fna', '.ffn', '.fasta', '.fastq')mercat2.py-i/path/to/input-file-k3-n8-c10-prodmercat2.py-i/path/to/input-file-k3-n8-c10-fgsthe prodigal and FragGeneScanRS options run the k-mer counter on both contigs and produced amino acidsOutputsResults are stored in the output folder (default 'mercat_results' of the current working directory)the 'report' folder contains an html report with interactive plotly figuresIf at least 4 samples are provided a PCA plot will be included in the html reportthe 'tsv' folder contains counts tables in tab separated formatif 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 metricsif nucleotide files are given a .tsv file is created for each sample containing k-mer count and GC contentif .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 filesif the -fgs option is used, a 'fgs' folder is created with the amino acid .faa fileNotes on memory usage and speedMerCat2 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 MercatIf 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.paperCONTACTPlease send all queries toJose Luis FigueroaDr. Richard Allen White IIIOropen an issue