WGBS Snakemake Workflow

This workflow is designed to run the basic steps for a whole-genome bisulfite sequencing experiment. It's intended to automate the workflow for future-use and reproducibility. Its design is explicitly simple to make it easy for users to not only understand the order and purpose of each step, but to be able to look at the code, figure out how it works and get it running extremely easily.

One advantage of running this through Snakemake is that it intelligently handles threading and replaces completed processes up to the number of cores specified at run-time. However, options for the thread count for each step are configurable in the .yaml file.

Dependencies

Most recent tested versions indicated. Though, more recent versions and slightly older ones are likely a-okay!

1. Trim Galore! v0.6.4
2. FastQC v0.11.8
3. bwa-meth v0.2.2
4. samtools v1.9
5. Picard Tools v2.22.3
6. MethylDackel v0.4
7. Mosdepth v0.2.9
8. Snakemake v5.14.0
9. Python3 v3.5

Quick-Start Guide

Firstly, download all the dependencies and make sure they're in your $PATH (that you can run them from a BASH prompt). Then clone the github repo:

git clone https://github.com/groverj3/wgbs_snakemake.git

Edit the config.yaml file to include your sample IDs (fastq filenames, excluding extensions, pair numbers, lane info, etc.) and a reference genome (which may be pre-indexed). You'll definitely want to make sure that the adapter sequence in there matches what's in your samples.

Currently, the workflow expects an R1 and R2 file for each sample. Place the individual .fastq.gz files for R1 and R2 into the input_data directory. Once you have all the required dependencies installed run the workflow with:

snakemake --cores {cores_here}

However, it is recommended to use the supplied Docker image.

Running With the Supplied Container

Supplied with this workflow is a Docker image built against Ubuntu 16.04. It was vital to use this rather old version because HPCs frequently have older kernels (especially ours at UofA), usually due to running an old version of CentOS or similar.

You can find the image on Dockerhub here: https://hub.docker.com/r/groverj3/mosher_lab_wgbs

Docker is a great way to enable you to get started running the workflow without needing to worry about dependency hell. Because, of course, the listed dependencies have dependencies themselves. Depending on the system you're running it may be impossible to install things globally, and it can be a pain to point a bunch of software at conflicting versions of libraries, etc. This is especially true as software ages.

Docker is, however, not commonly run in HPC environments due to requiring root access. Singularity solves this issue. Though, because Docker is more ubiquitous the image is supplied on Dockerhub. Singularity is fully compatible with the Docker image.

For the sake of documentation, the Dockerfile used to create the Docker image is also included in this GitHub repo.

Running with Docker

First, clone the github repo if you haven't and enter the directory:

git clone https://github.com/groverj3/wgbs_snakemake.git
cd wgbs_snakemake

Next, pull the image from Dockerhub:

docker pull groverj3/mosher_lab_wgbs:ubuntu_16

Then, locate your samples, put them in the input_data folder, note the location of your reference genome, and edit the config.yaml file as described in the quick-start section and its embedded comments.

Finally, run the workflow with the following command from within the Snakemake workflow's directory (where you cloned it from github):

docker run mosher_lab_wgbs -v ${system directory}:${container directory} \
    bash -c 'cd ${container directory} && snakemake --cores ${num cores}'

The -v option mounts a directory from your system to the container so it can operate on files outside the container. For simplicity I would recommend just connecting to a folder named the same inside the container. The snakemake executation command must then be run inside that same directory within the container. It sounds confusing, but just think of it as letting the container see your analysis directory. the bash -c '' line then changes to that same directory and runs snakemake with the number of cores you specify.

On HPCs you probably can't run Docker. So, you can instead do something very similar with Singularity.

Running with Singularity

First, clone the github repo if you haven't and enter the directory:

git clone https://github.com/groverj3/wgbs_snakemake.git
cd wgbs_snakemake

Next, pull the image from Dockerhub:

singularity pull docker://groverj3/mosher_lab_wgbs:ubuntu_16

This will create a file mosher_lab_wgbs_ubuntu_16.sif in your current directory. There are alternative ways to manage your images from centralized storage as well.

Then, locate your samples, put them in the input_data folder, note the location of your reference genome, and edit the config.yaml file as described in the quick-start section and its embedded comments.

Finally, run the workflow with the following command from within the Snakemake workflow's directory (where you cloned it from github):

singularity exec mosher_lab_wgbs_ubuntu_16.sif snakemake --cores ${num cores}

It's actually a little more straightforward than Docker because it's assumed that Singularity is running at the user level.

Workflow

1. Index the reference genome with bwameth and samtools faidx
2. Quality checking, and output of sample information with FastQC
3. Adapter and quality trimming with Trim Galore!
4. Alignment to a reference genome with bwa-meth
5. Marking PCR duplicates with Picard Tools MarkDuplicates
6. Detecting methylation bias per read position with MethylDackel
7. Extracting methylation calls per position into bedGraph and methylKit formats with MethylDackel
8. Calculating depth and coverage with Mosdepth

The output from the workflow is suitable for DMR-calling or aggregation of calls to determine % methylation per feature.

All About the Workflow

Whole-genome bisulfite sequencing is a modification of whole-genome shotgun sequencing designed to convert unmethylated cytosines into uracil. These uracils are then sequenced as thymine. By tallying up the number of cytosines and thymines for each cytosine in the reference genome you can then calculate a percentage methylation for each annotated cytosine in your species' reference assembly.

While it is possible to determine this by calling C -> T SNPs against a reference there is purpose-built software for this task. The most common aligner for WGBS is currently Bismark, and in our testing it performed well. However, we decided to use a slightly different pipeline for the purposes of our work in the Mosher Lab. The pipeline we settled on is a combination of open source tools built around bwameth for alignment and MethylDackel for methylation calling. In our experience this pipeline was many times faster and resulted in a marginally higher mapping rate. Additionally, it uses Picard Tools to mark potential PCR duplicates, and its method for doing so is not as conservative as Bismark's internal version of the same process. Bismark's speed has improved in more recent versions, and is under more active development but bwameth still produces comparable results in less time.

Use of MethylDackel allows us to determine per-position biases in terms of methylation calls on the reads, and different ones based on read orientation. Using some shell script hacking we can extract its recommendations for inclusion bounds for methylation calling based on these biases and use in the methylation calling step. This should reduce false positive or negatives based on effects of cytosines being too close to adapters, interference from end-repair, or simply incomplete trimming.

At the conclusion of the pipeline overall fold-coverage is calculated using the very fast Mosdepth tool from Brent Pedersen and some python scripts.

Citing the Workflow

Please do cite us! The included Zenodo DOI is the easiest way. Additionally, you should consider citing the paper in which we first used this workflow:

Grover JW et al. Abundant expression of maternal siRNAs is a conserved feature of seed development. 2020. PNAS. https://doi.org/10.1073/pnas.2001332117