QC

See the qc hydra-genetics module documentation for more details on the softwares for the quality control. Default hydra-genetics settings/resources are used if no configuration is specified.


dag plot

Pipeline output files:

  • results/dna/qc/multiqc_DNA.html
  • results/dna/{sample}_{type}/qc/{sample}_{type}.coverage_and_mutations.tsv
  • gvcf_dna/{sample}_{type}.mosdepth.g.vcf.gz

MultiQC

A MultiQC html report is generated using MultiQC v1.11. The report starts with a general statistics table showing the most important QC-values followed by additional QC data and diagrams. The qc data is generated using FastQC, samtools, picard, and GATK.

Configuration

Software settings

  • multiqc: reports: DNA: config: Config of the general statistics table
  • qc_files: Configuration of input files to MultiQC in the config file:
# config.yaml
multiqc:
  container: "docker://hydragenetics/multiqc:1.11"
  reports:
    DNA:
      config: "config/multiqc_config_dna.yaml"
      included_unit_types: ["N", "T"]
      qc_files:
        - "qc/fastqc/{sample}_{type}_{flowcell}_{lane}_{barcode}_fastq1_fastqc.zip"
        - "qc/fastqc/{sample}_{type}_{flowcell}_{lane}_{barcode}_fastq2_fastqc.zip"
        - "qc/picard_collect_alignment_summary_metrics/{sample}_{type}.alignment_summary_metrics.txt"
        - "qc/picard_collect_duplication_metrics/{sample}_{type}.duplication_metrics.txt"
        - "qc/picard_collect_hs_metrics/{sample}_{type}.HsMetrics.txt"
        - "qc/picard_collect_insert_size_metrics/{sample}_{type}.insert_size_metrics.txt"
        - "qc/samtools_stats/{sample}_{type}.samtools-stats.txt"
        - "qc/gatk_calculate_contamination/{sample}_{type}.contamination.table"

FastQC

FastQC v0.11.9 is run on the raw fastq-files.

Configuration

Cluster resources

Options Value
mem_mb 12288
mem_per_cpu 6144
threads 2

Samtools

Samtools stats v1.15 is run on BWA-mem aligned and merged bam files.

Picard

Picard v2.25.0 is run on BWA-mem aligned and merged bam files collecting a number of metrics. The metrics calculated are listed below:

GATK

Cross-sample contamination is estimated using GATK v4.1.9.0. The contamination is calculated by gatk GetPileupSummaries making pileups for input SNP positions on BWA-mem aligned and merged bam files followed by evaluating these pileups with gatk CalculateContamination. Contamination levels should very low so that already at 1% there is reason to be concerned.

Genome vcf

Using GATK Mutect2 v4.1.9.0 it is possible to make a vcf with alternative allele information for all positions in the design bed. As in the ordinary variant calling the individual chromosome bamfiles are used and then the vcf-files are merged. Additionally, the coverage of all positions are calculated using mosdepth v0.3.2 and then added to the final vcf file.

Configuration

References


Cluster resources for Mutect2

Options Value
mem_mb 12288
mem_per_cpu 6144
time "48:00:00"

Result file

  • gvcf_dna/{sample}_{type}.mosdepth.g.vcf.gz

Coverage and mutations

This excel-friendly report produced by the in-house script hotspot_report.py (rule and configuration) contains coverage in all clinical relevant positions defined in a "hotspot"-file and flags positions with low coverage. It also collects information on the filtered variants from the vcf file. The coverage flag is configured by the levels option, included columns are configured in hotspot_report.yaml and chromosome id translation is done by hotspot_report.chr.translation.hg19.

Configuration

References

  • File with clinical relevant positions


Software settings

report_config: "config/hotspot_report.yaml"
chr_translation_file: "config/hotspot_report.chr.translation.hg19"
levels:
  - [200, "ok", "yes"]
  - [30, "low", "yes"]
  - [0, "low", "not analyzable"]

Result file

  • results/dna/{sample}_{type}/qc/{sample}_{type}.coverage_and_mutations.tsv

Sample mixup check

The sample mixup check compares the ID-SNPs in the DNA sample to the RNA sample in the same analysis and reports sample similarities to be able to discern sample mixups. The check is performed by the in-house script sample_mixup_check.py (rule and config).

Result file

  • results/sample_mixup_check.tsv

Somalier relatedness check

The somalier relatedness check uses Somalier to estimate relatedness between all samples (DNA and RNA) in the analysis. This is used to verify that samples from the same individual cluster together. The results are summarized in a best match report produced by the in-house script somalier_best_match.py (rule and config).

Result file

  • results/qc/somalier/somalier_best_match.tsv

Somalier DNA-DNA relatedness check

A specific check for DNA-DNA sample pairs using Somalier relatedness data. It helps identify potential mixups between DNA samples from the same analysis. The report is generated by the somalier_dna_dna_match.py script (rule and config).

Result file

  • results/qc/somalier_dna_dna_match.tsv