ONT 10x Feature Barcoding Pipeline

This pipeline processes single-cell TSB libraries generated using Oxford Nanopore Technologies (ONT) sequencing with 10x Genomics GEM chips.

Overview

The pipeline assumes that the sequencing library was prepared using 10x Genomics technology and is designed to handle the specific requirements of ONT single-cell transcriptomics data.

Cloning the Pipeline Repository

To get started, first clone the pipeline repository from GitHub:

git clone https://github.com/CooperStansbury/ont_10x_feature_barcodes.git
cd ont_10x_feature_barcodes

This ensures you have the latest version of the pipeline.

Running the Pipeline

To run the pipeline, follow these steps:

1. Set Up the Environment
   Ensure Conda is installed and set up correctly. The pipeline requires the following Conda environment:

   • Top-level environment: workflow-env.yaml

   Install the environments by running:

   conda env create -f envs/workflow-env.yaml

   After installation, activate the top-level environment:

   conda activate workflow-env

2. Prepare Configuration Files
   Update the config.yaml file with the correct paths to your input data, reference genome, and output directories.

3. Execute the Pipeline
   Always run Snakemake using the --use-conda flag to ensure proper dependency management:

   snakemake --use-conda --cores <num_cores>

   Replace <num_cores> with the number of threads available for computation.

4. Dry Run (Optional)
   To verify the workflow without executing commands:

   snakemake --use-conda --configfile config.yaml -n

5. Cluster Execution (Optional)
   If running on an HPC system, submit jobs using:

   snakemake --use-conda --cluster "sbatch --mem={resources.mem_mb}" --jobs 10

Input Requirements

• Raw FASTQ Files: Path specified in config.yaml and organized in a fastq_paths.txt file as described in the config file's readme.
• Configuration File: config.yaml contains parameters for alignment, filtering, and output directories.

Output

This pipeline produces the following output files and directories:

• config: Stores the configuration files used for the pipeline run, ensuring reproducibility.
• fastq: Contains the initial raw FASTQ files used as input.
• feature_barcodes: Holds the disambiguated feature barcodes, likely in a structured format (e.g., CSV, TSV) with associated metadata.
• flexiplex: Contains intermediate files and results related to the Flexiplex barcode correction process. This might include:
  • Corrected barcode sequences.
  • Logs and reports from the Flexiplex tool.
  • Statistics on error correction.
• hashed_reads: Stores reads that have been hashed based on their assigned feature barcodes. This might be organized by:
  • test: A subdirectory potentially containing a subset of hashed reads for testing or validation purposes.
• reference: Contains reference files used in the pipeline, such as:
  • Genome sequences.
  • Annotation files (e.g., GTF).
  • Barcode whitelists.
• reports: Houses various reports generated during the pipeline execution:
  • nanoplexer: Reports related to the Nanoplexer demultiplexing step, including:
    • Demultiplexing statistics.
    • Barcode assignment summaries.
    • Potential error rates.
• whitelist: Contains the initial whitelist of expected feature barcodes, potentially used for filtering or validation.

Troubleshooting

• Ensure Conda is installed and environments are set up correctly.
• Verify input file paths: Ensure all paths in config.yaml and fastq_paths.txt are correct.
• Check for missing dependencies: Run snakemake --use-conda --conda-create-envs-only.
• Review logs: Check the output logs in the logs directory for error messages and troubleshooting hints.

---

For further details and support, refer to the official Snakemake documentation:
https://snakemake.readthedocs.io