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GGCPan: A graph-modeled gastric cancer pangenome

Introduction

We introduced a graph-based pangenome called GGCPan as a reference for gastric cancer, and systematically compared the results with those traditional genomics studies using the human reference genome or a linear pangenome as the references. This is our construction method and the analysis pipeline.

GGCPan Construction

  • step1：Align assembled contigs(>500bp) to GRCh38 using minimap2.
  • step2：Detect variants using paftools.js and filter the small variants and variants with quality <= 60.
  • step3：Embed variants to GRCh38 using vg toolkit.
step1,2,3 are included in the following construct.sh file, which can be run to generate the graph-modeled pangenome.
bash construct.sh

Alignment

Reads aligned to linear references using BWA MEM and GATK pipeline.

  • We firstly align the raw reads to references with BWA MEM,then mark duplications and adjust the base quality with GATK BQSR.
  • The codes are stored in Alignment/gatk.slurm.

Reads aligened to graph-modeled pangenome using vg giraffe.

  • The raw reads are aligned to graph-modeled reference with vg giraffe.
  • To detect snps and indels from graph-modeled pangenome,we convert graph alignment format(.gam) to linear alignment format(.bam).
  • The codes are stored in Alignment/graph.alignment.sh.

Variant Calling

SNPs and Indels

  • We used GATK Mutect2 to detect SNPs and Indels.
  • The coded are stored in VariantCalling/mutect2.sh.

Structural vatiants

  • We used Manta, Delly,Svaba and Survivor to detect SVs based on linear references. We used vg call to detect SVs based on graph-modeled references.
  • The codes are stored in VariantCalling/linear.variantDetection.sh and VariantCalling/graph.variantDetection.sh.