Analyse methylation profiles

Identify genomic regions with and without methylation. The pipeline expects paired-end Illumina reads with the bisulfide conversion.

Purpose

  • Epigenetic marker for several diseases (e.g. oncology)
  • Compare between samples with different phenotype (e.g. tissues)

Required inputs

  • Sequenced paired-end reads from Illumina sequencer in gzipped fastq format.
    • each sample is represented by two gzipped fastq files
    • standard output files of paired-end sequencing
  • Reference genome in fasta format
|-- reads/original
        |-- <sample_1>_R1.fastq.gz
        |-- <sample_1>_R2.fastq.gz
        |-- <sample_2>_R1.fastq.gz
        |-- <sample_2>_R2.fastq.gz
|-- reference/<reference>
        |-- <reference>.fa

Generated outputs

  • Summary report of methylation profiles in sequenced samples

Example

How to run example:

cd /usr/local/snakelines/example/genomic

snakemake \
   --snakefile ../../snakelines.snake \
   --configfile config_methylseq.yaml \
   --use-conda

Example configuration:

sequencing: paired_end
samples:                            # List of sample categories to be analysed
    - name: example.*               # Regex expression of sample names to be analysed (reads/original/example.*_R1.fastq.gz)
      reference: mhv                # Reference genome for reads in the category (reference/mhv/mhv.fa)

report_dir: report/public/01-methyl-seq  # Generated reports and essential output files would be stored there
threads: 16                         # Number of threads to use in analysis

reads:                              # Prepare reads and quality reports for downstream analysis
    preprocess:                     # Pre-process of reads, eliminate sequencing artifacts, contamination ...

        trimmed:                    # Remove low quality parts of reads
            method: trimmomatic     # Supported values: trimmomatic
            temporary: False        # If True, generated files would be removed after successful analysis
            crop: 500               # Maximal number of bases in read to keep. Longer reads would be truncated.
            quality: 20             # Minimal average quality of read bases to keep (inside sliding window of length 5)
            headcrop: 20            # Number of bases to remove from the start of read
            minlen: 35              # Minimal length of trimmed read. Shorter reads would be removed.

        deduplicated:               # Remove fragments with the same sequence (PCR duplicated)
            method: fastuniq        # Supported values: fastuniq
            temporary: False        # If True, generated files would be removed after successful analysis

    report:                         # Summary reports of read characteristics to assess their quality
        quality_report:             # HTML summary report of read quality
            method: fastqc          # Supported values: fastqc
            read_types:             # List of preprocess steps for quality reports
                - original
                - trimmed
                - deduplicated

mapping:                            # Find the most similar genomic region to reads in reference (mapping process)
    mapper:                         # Method for mapping
        method: bismark             # Supported values: bowtie2, bwa, bismark
        temporary: True

    index:                          # Generate .bai index for mapped reads in .bam files
        method: samtools            # Supported values: samtools

    postprocess:                    # Successive steps to refine mapped reads
        sorted:
            method: samtools
            temporary: False

    report:                         # Summary reports of mapping process and results
        quality_report:             # HTML summary with quality of mappings
            method: qualimap        # Supported values: qualimap
            map_types:              # List of post-process steps for quality reports
                - sorted
        methylation:
            method: bismark

Planned improvements

  • Aggregate quality statistics of preprocess and mapping with the MultiQC
  • Include coverage tracks (Bismark can produce them as well)