Overview - CNV Germline

The CGAP Pipelines module for germline Copy Number Variants (CNVs) (https://github.com/dbmi-bgm/cgap-pipeline-SV-germline) identifies, annotates, and filters CNVs starting from analysis-ready bam files to produce final sets of calls in vcf format.

CNVs are a class of large genomic variants that include deletion and duplication events resulting in a copy number change. CNVs are a type of Structural Variants (SVs), which also includes inversions, translocations, and other complex genomic changes. However, it’s useful to maintain a separate classification to account for the differences between the algorithms used for their detection. SV calling algorithms rely on local information from anomalously mapping reads (e.g., read pairs with unexpected fragment sizes, mapping orientations, and clipping events). CNV calling algorithms search instead for unexpected variation in sequencing coverage. As a result, algorithms for CNV detection perform better and are more robust in the identification of larger events, as they don’t rely on local context information, but are less powerful and accurate than SV algorithms in the detection of smaller events. Given these substantial differences, CGAP implements both SV and a CNV calling algorithms, with the goal to combine the strength of both algorithmic approaches for an integrated analysis of the structural variation in the germline genome.

The pipeline is mostly based on the CNV calling algorithm BICseq2, alongside software for variants annotation and filtering (ensembl-vep, Sansa and granite).

The pipeline is designed for proband-only analysis, with the proband diagnosed with a likely monogenic disease. It can receive the initial analysis-ready bam file from either of the CGAP Upstream modules.

Note: The pipeline is not optimized for Whole Exome Sequencing (WES) data. Currently, the bam files used for input must be generated by mapping 150 bp paired-end reads to hg38/GRCh38 genome assembly. The mappability file used by BICseq2 was generated on hg38/GRCh38, as well as the other reference files used in the annotation steps. The mappability file was calculated considering 150 bp reads.

Docker Images

The Dockerfiles provided in this GitHub repository can be used to build public docker images. If built through portal-pipeline-utils pipeline_deploy command (https://github.com/dbmi-bgm/portal-pipeline-utils), private ECR images will be created for the target AWS account.

The cnv_germline image is primarily for CNV identification. This image contains (but is not limited to) the following software packages:

• BICseq2 normalization (0.2.6)
• BICseq2 segmentation (0.7.3)
• R (4.1.0)
• granite (0.2.0)
• picard (2.26.11)
• samtools (1.9)

Pipeline Flow

The overall flow of the pipeline is shown below:

Pipeline Parts

Largely, the pipeline consists of three parts:

Part 1. Starting from an analysis-ready bam file, BICseq2 algorithm identifies differences in coverage that are eventually converted to genotyped CNVs in vcf format using bic_seq2_vcf_formatter.py script

Part 2. CNVs are annotated using VEP (Ensembl Variant Effect Predictor) to add transcripts information and Sansa to add gnomAD SV allele frequencies

Part 3. CNVs are filtered to remove non-functional variants, artifacts, and variants that are common in the population

vcf files are checked for integrity using VCFtools vcf-validator at the end of each step during which they are created or modified.

Pipeline Steps

• Part 1. BICseq2 CNVs Identification
  • BICseq2
    • Input
    • Generating BICseq2 seq Files
    • BICseq2-norm
    • BICseq2-seg
  • Reformatting BICseq2 Output to vcf
    • Calling DEL or DUP
    • Genotyping DEL or DUP
  • References
• Part 2. CNVs Annotation
  • Sansa and VEP
    • Sansa
    • VEP
    • Combine Sansa and VEP
  • Confidence Classes
    • High Confidence Calls
    • Low Confidence Calls
  • References
• Part 3. CNVs Filtering and Secondary Annotations
  • Annotation Filtering and SVTYPE Selection
    • Requirements
    • Gene list
    • Inclusion List
    • Exclusion List
    • CNV Type Selection
    • Output
  • 20 Unrelated Filtering
    • Requirements
    • Matching and Filtering
    • Output
  • Secondary Annotation
    • Requirements
    • Annotation and Possible Filtering
    • Output
  • Length Filtering
    • Requirements
    • Filtering
    • Output
  • VCF Annotation Cleaning
    • Requirements
    • Cleaning
    • Output
  • References

References

BICseq2. ensembl-vep. Sansa. granite. VCFtools.