seqnado 0.2.0

Pipelines for genomics analysis

SeqNado Pipeline

Pipeline based on snakemake to process ChIP-seq, ATAC-seq, RNA-seq and short read WGS data for SNP calling.

Installation

1. Create a basic conda environment (with pip to install python packages) and activate it.

       conda create -n seqnado pip
       conda activate seqnado
   

2. Install the pipeline. Three options:

   a) Install the package from pip (recommended)

       pip install seqnado
   

   b) Clone the repositry and install directly.

       git clone https://github.com/alsmith151/SeqNado.git
       cd SeqNado
       pip install .
   

   c) Install from GitHub directly

       pip install git+https://github.com/alsmith151/SeqNado.git
   

3. If you intend to use a cluster e.g. SLURM add the path to the DRMAA interface to your .bashrc:

       # Access to the DRMAA library: https://en.wikipedia.org/wiki/DRMAA
       echo "export DRMAA_LIBRARY_PATH=/<full-path>/libdrmaa.so" >> ~/.bashrc
   
       # For CBRG users the command to use is:
       echo "export DRMAA_LIBRARY_PATH=/usr/lib64/libdrmaa.so" >> ~/.bashrc
   

Running the pipeline

1. Setup project directory

   In the parent directory of desired the working directory run the following command:

       seqnado-config atac # ATAC-seq samples
       seqnado-config chip # ChIP-seq/ChIPMentation
       seqnado-config rna # RNA-seq - Not fully tested
       seqnado-config snp # snp calling - Not fully tested
   
   

   This will lead you through a series of questions which will create a new project directory, config file and a sample sheet for you to edit.

   cd into the newly made directory and inspect the config file.

2. Copy or link fastq files into the fastq directory

   Copy:
   cp PATH_TO_FASTQ/example_R1.fastq.gz

   Symlink: Be sure to use the absolute path for symlinks i.e.
   ln -s /ABSOLUTE_PATH_TO_FASTQ/example_R1.fastq.gz

3. Set-up sample sheet

   There are two options for preparing a sample sheet:

   a) Using seqnado-design

       seqnado-design atac fastq/* # ATAC-seq samples
       seqnado-design chip fastq/* # ChIP-seq/ChIPMentation
       seqnado-design rna fastq/* # RNA-seq - Not fully tested
       seqnado-design snp fastq/* # snp calling - Not fully tested
   
   

   If samples names match the following conventions then a sample sheet will be generated for your samples:

    ChIP-seq
   
    * samplename1_Antibody_R1.fastq.gz
    * samplename1_Antibody_R2.fastq.gz
    * samplename1_Input_1.fastq
    * samplename1_Input_2.fastq
   
    For ATAC-seq:
   
    * sample-name-1_R1.fastq.gz
    * sample-name-1_R2.fastq.gz
    * sample-name-1_1.fastq
    * sample-name-1_2.fastq
   
    For RNA-seq:
   
    * sample-name-1_R1.fastq.gz
    * sample-name-1_R2.fastq.gz
    * sample-name-1_1.fastq
    * sample-name-1_2.fastq  
   

   b) Using a custom sample sheet.

   This is useful for situations in which it can be difficult to appropriately compare IP and Input control samples.

   • For ChIP-seq samples you will need to create a csv or tsv file with the following columns:

     sample	antibody	fq1	fq2	control
     SAMPLE-NAME	ANTIBODY	SAMPLE-NAME_ANTIBODY_R1.fastq.gz	SAMPLE-NAME_ANTIBODY_R2.fastq.gz	CONTROL_SAMPLE_Input

   • For ATAC-seq, RNA-seq or SNP calling samples you will need to create a csv or tsv file with the following columns:

     sample	fq1	fq2
     SAMPLE-NAME	SAMPLE-NAME_R1.fastq.gz	SAMPLE-NAME_R2.fastq.gz

4. Running the pipeline

   All FASTQ files present in the directory will be processed by the pipeline in parallel and original FASTQ files will not be modified. If new FASTQ files are added to a pre-run pipeline, only the new files will be processed.

   After copying/linking FASTQ files into the working directory and configuring the copy of config_[assay].yml in the working directory for the current experiment, the pipeline can be run with:

   seqnado atac # ATAC-seq samples
   seqnado chip # ChIP-seq/ChIPMentation
   seqnado rna # RNA-seq - Not fully tested
   seqnado snp # snp calling - Not fully tested
   

   • To visualise which tasks will be performed by the pipeline before running.
     seqnado atac -c 1 --preset ss --dag | dot -Tpng > dag.png

   • If using all default settings (this will run on just the login node)
     seqnado atac -c NUMBER_OF_CORES

   • If you want to use the cluster (recommended)
     seqnado atac -c NUMBER_OF_CORES --preset ss

   • Avoiding network disconnections
     nohup seqnado atac make &

   Your processed data can be found in ./seqnado_output