FetaGenome Generator

Scripts in this repository allow for the creation of 'Fake Metagenomes' (aka FetaGenomes) with known community compositions.

In order to have these scripts work, you'll need to following installed and available on your $PATH:

• ART
• Python3
• Some sort of Unix-based system with basic utilties (gzip, cat, rm) installed.
• If using the plasmid-aware script, you'll need BBMap as well.

Installing FetaGenome Generator

Ideally, create a virtualenv (some instructions can be found here), and then install FetaGenome via pip: pip install fetagenome

Making FetaGenomes

To make FetaGenomes, run FetaGenome - this will create a mock fetagenome with known proportions as defined in a config file that is given as one of the arguments to the script.

The config file should have two columns, comma-separated - the first should be Strain, and the second should be Proportion. Put the absolute path to FASTA files you want to use in the first column and the relative proportion in the second. If you want to have this process simplified, put all the FASTA files you're interested in having as part of your FetaGenome into a folder and run generate_config_file -i /path/to/folder. This will create a file called FetaGenomeConfig.csv in your current working directory with all FASTA-formatted files in the folder specified as strains in equal proportions.

usage: FetaGenome [-h] -c CONFIG_FILE -o OUTPUT_DIR [-n NUMBER_READS]
                  [-f FETAGENOME_NAME] [-q QUALITY_SHIFT] [-t THREADS]
                  [-l READ_LENGTH] [-p {MSv1,HS25}]

Given a configuration file, will create a FetaGenome from FASTA files by
simulating reads with ART and pasting reads together into a FetaGenome.

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG_FILE, --config_file CONFIG_FILE
                        Path to your configuration file for FetaGenome
                        creation.
  -o OUTPUT_DIR, --output_dir OUTPUT_DIR
                        Output directory for your FetaGenome files. Will be
                        created if it does not already exist.
  -n NUMBER_READS, --number_reads NUMBER_READS
                        Number of reads to include in FetaGenome. Defaults to
                        1000000.
  -f FETAGENOME_NAME, --fetagenome_name FETAGENOME_NAME
                        Name of your FetaGenome file. Defaults to FetaGenome
                        (so reads will be called FetaGenome_R1.fastq.gz and
                        FetaGenome_R2.fastq.gz)
  -q QUALITY_SHIFT, --quality_shift QUALITY_SHIFT
                        By default, ART will simulate Illumina reads with
                        fairly high quality. If you want this changed, you can
                        make them even higher quality with a positive integer
                        (to shift up by 2 on average, enter 2) or make them
                        lower quality with a negative number.
  -t THREADS, --threads THREADS
                        Number of threads to run, allows for much faster
                        simulation of reads. Defaults to number of cores on
                        your machine.
  -l READ_LENGTH, --read_length READ_LENGTH
                        Read length. Defaults to 250.
  -p {MSv1,HS25}, --platform {MSv1,HS25}
                        Sequencing platform to simulate from. Choices are MSv1
                        (MiSeq) or HS25 (HiSeq 2500). Defaults to MiSeq

Making Fetagenomes (Plasmid Aware Edition)

The above Fetagenome creation assumes that coverage is even across the entirety of the genomes that are being simulated, which probably isn't a biological reality, particularly when plasmids get involved. With this in mind, FetaGenomePlasmidAware was created. This program is very similar to FetaGenome, but will calculate the depth of each contig in the assemblies passed into it, and use that information to create more reads for higher-depth locations, and fewer reads for low-depth locations.

Because FetaGenomePlasmidAware needs to calculate depth, it needs FASTQ files as well as FASTA, so you'll need to add a ForwardReads and ReverseReads column that contain full paths to forward and reverse reads, respectively, for each of your strains. Other than that, usage is very similar to FetaGenome. See below usage for more info.

usage: FetaGenomePlasmidAware [-h] -c CONFIG_FILE [-f FETAGENOME_NAME]
                              [-n NUMBER_READS] [-l READ_LENGTH]
                              [-i INSERT_SIZE] [-t THREADS] [--log LOG]
                              [--coverage_report COVERAGE_REPORT] [-v]
                              [-p {MSv1,HS25}]

Given a configuration file, will create a FetaGenome from FASTA files by
simulating reads with ART and pasting reads together into a FetaGenome.

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG_FILE, --config_file CONFIG_FILE
                        Path to your configuration file for FetaGenome
                        creation.
  -f FETAGENOME_NAME, --fetagenome_name FETAGENOME_NAME
                        Name of your FetaGenome file. (so reads will be called
                        FetaGenome_R1.fastq.gz and FetaGenome_R2.fastq.gz)
  -n NUMBER_READS, --number_reads NUMBER_READS
                        Number of reads to include in FetaGenome. Defaults to
                        1000000.
  -l READ_LENGTH, --read_length READ_LENGTH
                        Read length. Defaults to 150.
  -i INSERT_SIZE, --insert_size INSERT_SIZE
                        Insert size. Defaults to 250.
  -t THREADS, --threads THREADS
                        Number of threads to run. Defaults to all cores on
                        your machine.
  --log LOG             Name for log file. Defaults to FetaGenome_log_YYYY-MM-
                        DD_HH:MM:SS.txt
  --coverage_report COVERAGE_REPORT
                        If you want a report on coverages for all of your
                        contigs, specify a file name here for them to be
                        written to. This file will be in CSV format.
  -v, --version         show program's version number and exit
  -p {MSv1,HS25}, --platform {MSv1,HS25}
                        Sequencing platform to simulate from. Choices are MSv1
                        (MiSeq) or HS25 (HiSeq 2500). Defaults to HiSeq.