- This pipeline performs initial QC of the input PacBio sequencing data.
- The required input is a g-zipped fastq file contining PacBio CCS reads.
- Uses the lacI sequence to determine read direction and reverse complements reverse reads.
- Extracts target sequences, e.g. lacI and barcodes from reads.
- Generates QC stats for extracted reads.
- Snakemake is used to compose the bioinformatic pipeline, https://snakemake.readthedocs.io/en/stable/.
- Snakemake and pipeline dependencies can be install using conda (https://www.anaconda.com/distribution/) and bioconda (https://bioconda.github.io/).
- The pipeline is defined in the
Snakefileand adapter sequences are defined in thetargets.csvconfig file.
- Install conda with python 3.7 if not already installed on the system, https://docs.anaconda.com/anaconda/install/, miniconda as well as the full anaconda install will work.
- Generate a conda environment for running the pipeline,
conda env create -n lacI_ccs --file environment.yaml.
- Activate conda environment
conda activate lacI_ccs. - Run pipeline using
snakemake -j [#]. The-jparameter takes the number of threads or parallel jobs snakemake executes simultaneously. (I like to also include-p, which prints the command snakemake executes at each step.) The dry run snakemake argument,-n, can be used to see which pipeline steps will be run.
- Pipeline input files and output directory are defined in the config file, 'config.yaml'.
- The adapter sequences are defined and can be updated in the
targets.csvfile. - Snakemake works similar to make in that it only reruns parts of the analysis pipeline when an input file is changed or the output file is missing. After updating the
targets.csvfile you will want to remove any pipeline output files you want to replace.
- The file, '/data_0/raw/test.fastq.gz', contains a suitable test dataset. To run the pipeline with another dataset, change the output directory (outdir) and input file (infq) in the config.yaml file.