Germline Genomics

Our OP² germline genome transcriptomics pipeline is a bioinformatics analysis workflow used for whole genome, whole-exome or targeted DNA sequencing data.
It allows you to analyze your genome sequencing data using this gold standard analysis pipeline.
You get insights into the quality of your data, identify small to large nucleotide and structural variation and annotate with biological knowledge.

The workflow processes raw data from FastQ inputs, aligns the reads, calls variants and performs variant annotation.
These results are made available to you via two interactive reports, and a data package with all essential intermediate files to perform more in-depth data analysis.
The pre-processing workflow processes your raw sequence data until QC approved aligned data.
Next, the post-processing workflow enables you to review the biological meaning of your data via data annotation.

Example Pre-processing Report
Example Post-processing Report
  • 1

    Input

    Whole genome, whole-exome and targeted genome data
    Paired-end compressed raw FastQ files
    Reference genome (GRCh37, GRCh38, GRCm38)

  • 2

    Sequence QC

    Reads with low-quality are discarded

  • 3

    Trimming

    Adaptor and quality trimming of reads

  • 4

    Alignment

    BWA aligns reads to reference genome

  • 5

    Alignment QC

    Alignment statistics: read depths, per base, GC content, …

  • 6

    Mark Duplicates

    GATK MarkDuplicates removes potential PCR artefacts
    Construction of expression matrices

  • 7

    Base Quality Score Recalibration

    BQSR is recalibrated
    BQSR model is applied

  • 8

    Merge to final alignment file

    All steps are consolidated in one alignment file per sample

  • 1

    Input

    Trimmed, recalibrated alignment file

  • 2

    Variant Calling

    SNVs, small indels, structural variants are called
    GATK HaplotypeCaller, Strelka2, FreeBayes, Manta, …

  • 3

    Merge multi-variant files

    All variant calling results are consolidated in one variant calling file per sample

  • 4

    Variant Annotation

    Variants get biological knowledge assigned
    snpEff and VEP

  • 5

    Variant QC and reporting

    Quality score of variants are summarized
    Summary statistics on variant categories, etc