Getting started

The main prerequisite for lcdb-wf is conda <https://docs.conda.io/en/latest/>_, with the bioconda. channel set up and the mamba drop-in replacement for conda installed.

If this is new to you, please see conda and conda envs in lcdb-wf.

Note

lcdb-wf is tested and heavily used on Linux. It is only supported on Linux.

Setting up a project

The general steps to use lcdb-wf in a new project are:

  1. Deploy: download and run deploy.py to copy files into a project directory

  2. Configure: set up samples table for experiments and edit configuration file

  3. Run: activate environment and run the Snakemake file either locally or on a cluster

1. Deploying lcdb-wf

Using lcdb-wf starts with copying files to a project directory, or “deploying”.

Unlike other tools you may have used, lcdb-wf is not actually installed per se. Rather, it is “deployed” by copying over relevant files from the lcdb-wf repository to your project directory. This includes Snakefiles, config files, and other infrastructure required to run, and excludes files like these docs and testing files that are not necessary for an actual project. The reason is to use this script is so you end up with a cleaner project directory, compared to cloning the repo directly.

This script also writes a file to the destination called .lcdb-wf-deployment.json. It stores the timestamp and details about what commit was used to deploy it. This tracks provenance of the code, so you can always figure out what lcdb-wf commit your deployment originally started from.

There are a few ways of doing this.

Option 1: Download and run the deployment script

This is the most convenient method, although it does not allow running tests locally.

BRANCH=master  # optionally change branch
wget https://raw.githubusercontent.com/lcdb/lcdb-wf/$BRANCH/deploy.py

Run python deploy.py -h to see help. Be sure to use the --staging and --branch=$BRANCH arguments when using this method, which will clone the repository to a location of your choosing. Once you deploy you can remove the script. For example:

python deploy.py \
  --dest analysis/project \
  --staging /tmp/lcdb-wf-tmp \
  --branch $BRANCH \
  --flavor rnaseq \
  --clone \
  --build-envs

# You can clean up the cloned copy if you want:
# rm -rf /tmp/lcdb-wf-tmp

This will clone the full git repo to /tmp/lcdb-wf-tmp, check out the master branch (or whatever branch $BRANCH is set to), copy the files required for an RNA-seq project over to analysis/project, build the main conda environment and the R environment, save the .lcdb-wf-deployment.json file there, and then delete the temporary repo.

Option 2: Clone repo manually

Clone a repo using git and check out the branch. Use this method for running tests):

BRANCH=master  # optionally change branch
git clone https://github.com:lcdb/lcdb-wf /tmp/lcdb-wf
cd /tmp/lcdb-wf
git checkout $BRANCH

Then run the deploy script, python deploy.py -h to see usage info. Here is an example for RNA-seq:

python deploy.py \
  --dest analysis/project \
  --flavor rnaseq \
  --build-envs

Note

If you want to run the tests then don’t deploy just yet – see Testing the installation for details, and then come back here to deploy for an actual project.

Note

See conda and conda envs in lcdb-wf for more details on the conda environment building.

2. Configure

This step takes the most effort. The first time you set up a project it will take some time to understand the configuration system.

3. Run

Activate the main environment and go to the workflow you want to run. For example if you have deployed and configured an RNA-seq run, then do:

conda activate ./env
cd workflows/rnaseq

and run the following:

snakemake --dryrun

If all goes well, this should print a list of jobs to be run.

You can run locally, but this is NOT recommended for a typicaly RNA-seq project. To run locally, choose the number of CPUs you want to use with the -j argument as is standard for Snakemake.

Warning

If you haven’t made any changes to the Snakefiles, be aware that the default configuration needs a lot of RAM. For example, the MarkDuplicates runs set 20 GB RAM for Java, and that’s for each job. Adjust the Snakefiles accordingly if you don’t have enough RAM available (search for “Xmx” to find the Java args that set memory).

# run locally (not recommended)
snakemake --use-conda -j 8

The recommended way is to run on a cluster.

To run on a cluster, you will need a Snakemake profile for your cluster that translates generic resource requirements into arguments for your cluster’s batch system.

On NIH’s Biowulf cluster, the profile can be found at https://github.com/NIH-HPC/snakemake_profile. If you are not already using this for other Snakemake workflows, you can set it up the first time like this:

  1. Clone the profile to a location of your choosing, maybe ~/snakemake_profile

  2. Set the environment variable LCDBWF_SNAKEMAKE_PROFILE, perhaps in your ~/.bashrc file.

Then back in your deployed and configured project, submit the wrapper script as a batch job:

sbatch ../../include/WRAPPER_SLURM

This will submit Snakemake as a batch job, use the profile to translate resources to cluster arguments and set default command-line arguments, and submit the various jobs created by Snakemake to the cluster on your behalf. See Running on a cluster for more details on this.

Other clusters will need different configuration, but everything in lcdb-wf is standard Snakemake. The Snakemake documentation on cluster execution and cloud execution can be consulted for running on your particular system.

You can typically run simultaneous workflows when they are in different directories; see Overview of workflows for details.

Next steps

Next, we give a brief overview of the file hierarchy of lcdb-wf in the Guide to file hierarchy page.