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Pipeline DAG

Internal usage of analysis

These instructions are for if you have access to the ebi-cli cluster.



The first dependency is singularity which is a program for building and running containers. This package is installed on all worker nodes on the EBI cluster so you don't need to worry about installing it.

Python dependencies

The pipeline requires the python package snakemake - which require python3. It is the package which runs and coordinates the analysis pipeline. There are multiple ways of installing this package. Three will be presented here using pip, conda and using the virtual environment tool pipenv.

# using pip
pip3 install snakemake
# using pipenv
# if you dont have pipenv
pip3 install --user pipenv
# the following installs requirements and creates virtual env
cd /hps/nobackup2/research/stegle/users2/lurban/puntseq/public/analysis
pipenv install
# activate the virtual environment
pipenv shell

The instructions below will assume you are using the pipenv virtual environment for your snakemake installation.

NOTE: Make sure to activate the virtual environment whenever you start a new session.



Any samples which you would like to run through the pipeline need to be put in a specific directory structure. Within the analysis/ directory there is a directory called data/. Unsurprisingly the data goes in here. You should create a directory under data/ with a name unique to the nanopore run. For example, we have april, june, and august. Under this directory you should create a directory called basecalled. Within that directory you place the fastq file to be analysed. The file must be gziped and be named <run>_all_passed.fastq.gz. So for april we name the file april_all_passed.fastq.gz.


In analysis/ you will find a file called config.yaml. This contains some global variables and also the names of runs and sample IDs. If you add a new run directory (i.e september) then you need to add it to the key runs within the config file. Aside from this you shouldn't need to change anything in this file unless you want to change some fixed parameters for a component of the pipeline.

Cluster config

In analysis/ you will find a file called cluster.yaml. This contains configurations for snakemake to be able to submit jobs on the LSF cluster. The only time you would need to change something in here is if you add a new rule to the snakemake pipeline (in this case just copy the layout of the other rules) or if a job is killed for hitting it's memory limit (increase memory and the memory units in resources).
NOTE: The memory attribute in this file is in Mb units.


To run the pipeline you just need to run a simple script.

cd /hps/nobackup2/research/stegle/users2/lurban/puntseq/public/analysis
# activate virtual environment if needed
pipenv shell
# submit
bash scripts/ 

Simples 🤘


All log files will be deposited in the the analysis/logs directory. Each rule will generate 3 log files. One with the stderr of the program it is running (file wildcard .log), one with the stderr of the cluster job running the rule (file wildcard cluster_*.e), and one containing the stdout of the cluster job (file wildcard cluster_*.o). Check the last entry in the *.o file to find out of the job completed successfully or whether it hit a memory limit. Check the *.e file for issues relating to snakemake job submission. And check the *.log file for any issues relating to the program itself.

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