wg-blimp (Whole Genome BisuLfIte sequencing Methylation analysis Pipeline) can be utilised to analyse WGBS data. It performs alignment, qc, methylation calling, DMR calling, segmentation and annotation using a multitude of tools. First time using wg-blimp? We recommend having a look at our step-by-step guide.

Requirements

To run wg-blimp you need a UNIX environment that contains a Bioconda setup.

Installation

Bioconda

It is advised to install wg-blimp through Bioconda. It is strongly recommended to install wg-blimp in a fresh environment, as it has many dependencies that may conflict with other packages, for this you can use:

conda create -n wg-blimp wg-blimp r-base==4.1.1

WARNING: You need to install mamba as well if you intend to use wg-blimp‘s cluster mode (e.g. on SLURM clusters) using the following command:

conda create -n wg-blimp wg-blimp r-base==4.1.1 mamba

However, this will install mamba and conda as a dependency, so make sure to not install wg-blimp in an environment that is used for other purposes, and always run conda deactivate after running wg-blimp. Otherwise you might accidentally use the conda version installed along mamba instead of your own.

From source

You can also install wg-blimp from source using

python setup.py install

Using this installation method requires you to make sure all external tools are installed (such as bwa-meth).

Running wg-blimp

WGBS pipeline

wg-blimp is a cli wrapper for the WGBS pipeline implemented using Snakemake. In general, a pipeline config is fed to the Snakemake workflow and the corresponding tools are called. However, wg-blimp also provides some commands to ease creation of config files, or working without config files altogether.

The command wg-blimp run-snakemake will run the pipeline with its default parameters. Make sure to set the --cores and --genome-build options appropriately. This command will also internally create a config.yaml file containing all parameters used for the analysis.

However, in case the default configurations are not sufficient, users can provide their own configurations. The commands wg-blimp create-config and wg-blimp run-snakemake-from-config can be used for this purpose.

wg-blimp will attempt to match .fastq files to sample names by searching for sample names in .fastq file names. By default Illumina naming conventions are expected, e.g. for a samples test1 the .fastq files should be named as follows:

test1_L001_R1_001.fastq.gz
test1_L001_R1_002.fastq.gz
test1_L001_R2_001.fastq.gz
test1_L001_R2_002.fastq.gz
test1_L002_R1_001.fastq.gz
test1_L002_R1_002.fastq.gz
test1_L002_R2_001.fastq.gz
test1_L002_R2_002.fastq.gz

If names derive from this pattern, users can adjust the regular expression to match in the config file’s rawsuffixregex entry.

The folder structure created by wg-blimp run-snakemake will look as follows:

• alignment - contains all bam/bai files
• dmr - contains dmr files by different callers
• logs - each pipeline step deposits its logs here
• methylation - methylation bedgraph files
• qc - multiqc and other qc related files
• raw - text files describing which fastq files have been used for each sample
• segmentation - methylome segments (UMRs/LMRs/PMDs) as computed by MethylSeekR
• config.yaml - configuration file used for the analysis

It is recommended to check the raw folder if all samples contain the correct raw fastq source files. When in doubt, wg-blimp also allows for explicit association of samples and read files by setting sample_fastq_csv in the configuration file. An example csv file could look as follows (column names must be set to sample, forward and reverse):

sample,forward,reverse
sample1,/my/path/sample1_L1_1.fq.gz,/my/path/sample1_L1_2.fq.gz
sample1,/my/path/sample1_L2_1.fq.gz,/my/path/sample1_L2_2.fq.gz
sample2,/my/path/sample2_L1_1.fq.gz,/my/path/sample2_L1_2.fq.gz
sample3,/my/path/sample3_L1_1.fq.gz,/my/path/sample3_L1_2.fq.gz

Cluster mode

You can use wg-blimp on HPC infrastructure using Snakemake’s cluster mode by setting the options --cluster and --nodes. The command specified by --cluster will be used for rule execution by Snakemake Please note that cluster usage strongly depends on local infrastructure and operating systems, thus requiring users to determine adequate parameters for cluster mode. An example of wg-blimp within a SLURM environment could look as follows:

wg-blimp run-snakemake-from-config --cores 32 --nodes 2 --cluster "sbatch --partition normal --nodes=1 --ntasks-per-node 32 --time 01:00:00" config.yaml

Shiny GUI

You can use the command wg-blimp run-shiny to load one or more project config files into a shiny GUI for easier access.

Example

Some example .fastq can be found on Sciebo. You can use the command

wg-blimp run-snakemake fastq/ chr22.fasta blood1,blood2 sperm1,sperm2 results --cores=8 --aligner=gembs

Please note that the pipeline commands also allow a --use-sample-files option so sample groups can be loaded from text files instead of comma separates files.

Config parameters

The following entries are used for running the Snakemake pipeline and may be specified in the config.yaml files:

Key	Value
aligner	Aligner to be used by pipeline. Choose either gemBS or bwa-meth.
annotation_allowed_biotypes	Only genes with this biotype will be annotated in the DMR table (see https://www.gencodegenes.org/pages/biotypes.html ).
annotation_min_mapq	When annotating coverage, only use reads with a minimum mapping quality
bsseq_local_correct	Use local correction for bsseq DMR calling. Usually, setting this to FALSE will increase the number of calls.
cgi_annotation_file	Gzipped csv file used for cg island annotation. Mandatory for MethylSeekR segmentation. Usually downloaded from UCSC Table Browser.
computing_threads	Number of processors a single job is allowed to use. Remember to use --cores parameter for Snakemake.
dmr_tools	Tools to use for DMR calling. Available: bsseq, camel, metilene
group1	Samples in first group for DMR analysis
group2	Samples in second group for DMR analysis
gtf_annotation_file	GTF file used for annotation of genes and promoters.
io_threads	IO intensive tools virtually reserve this many cores (while actually using only one) to reduce file system IO load.
java_memory_gb	Gigabytes of RAM to allocate for Java-based tools. If samples are too large, this must be increased to prevent crashes.
methylation_rate_on_chromosomes	Compute methylation rates for these chromosome during QC
methylseekr_fdr_cutoff	FDR cutoff for MethylSeekR segmentation.
methylseekr_methylation_cutoff	Methylation cutoff for MethylSeekR segmentation.
methylseekr_pmd_chromosome	Chromosome to compute MethylSeekR alpha values for.
min_cov	Minimum average coverage for methylation calling
min_cpg	Minimum number of CpGs in a DMR to be called
min_diff	Minimum average difference between the two groups for DMR calling
output_dir	Directory containing all files created by the pipeline
promoter_tss_distances	Distance interval around TSS’s to be recognized as promoters in DMR annotation.
rawdir	Directory containing .fastq files
rawsuffixregex	The regular expressions to match for paired reads. By default, Illumina naming conventions are accepted.
ref	.fasta reference file. “Bisulfited” references and BWA indices will be created automatically by bwa-meth)
repeat_masker_annotation_file	File containing repetitive regions. Usually generated by RepeatMasker and downloaded from UCSC Table Browser.
sample_fastq_csv	Optional CSV file containing association between samples and read files. The CSV must contain a header with column names sample, forward and reverse. When this option is set, parameters rawdir and rawsuffixregex are ignored.
samples	All samples (usually concatenation of group1 and group2)
target_files	Files to be generated by the Snakemake workflow
temp_dir	Directory for temporary files. This option may be used for instances where computation node disk space is limited.

Reporting errors / Requesting features

If anything goes wrong using wg-blimp or any features are missing, feel free to open an issue or to contact Marius Wöste ( mar.w@wwu.de )

Citing

Please make sure to cite the BMC software article when using wg-blimp for research purposes:

Wöste, M., Leitão, E., Laurentino, S. et al. wg-blimp: an end-to-end analysis pipeline for whole genome bisulfite sequencing data. BMC Bioinformatics 21, 169 (2020). https://doi.org/10.1186/s12859-020-3470-5