Versatile Identification of Copy Number Variants with Canvas

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Accurate variant calling, especially for copy number alterations (CNAs), is crucial for analyzing large-scale sequencing data.
• Challenges in CNA identification include data complexity, sequencing biases, coverage variations, and sample-specific properties like tumor polyploidy or germline de novo variation.
• A unified software framework for diverse sample types (normal, tumor, pedigrees) streamlines bioinformatics workflows and reduces maintenance overhead.

Purpose of the Study:

• To introduce Canvas, a versatile tool for identifying copy number changes across various sequencing experiments.
• To provide a multipurpose variant calling solution for core sequencing facilities handling diverse sample sources.
• To simplify and standardize the analysis of copy number variations in both germline and somatic sequencing studies.

Main Methods:

• Canvas utilizes a novel approach for copy number variant (CNV) detection applicable to whole-genome and whole-exome sequencing data.
• The tool supports diverse experimental designs: matched tumor-normal, unmatched tumor-normal, small pedigrees, and single-sample normal resequencing.
• Genome-wide parameters such as cancer ploidy, purity, and heterogeneity are inferred alongside variant calls.

Main Results:

• Canvas demonstrates efficient and accurate identification of copy number changes in diverse sequencing datasets.
• The tool successfully analyzes samples from various sources, including cancer genomes and germline pedigrees.
• Canvas provides scalable and easily integrable workflows capable of processing thousands of samples.

Conclusions:

• Canvas offers a versatile and efficient solution for copy number variant calling in large-scale sequencing.
• Its ability to handle diverse sample types within a single framework simplifies bioinformatics pipelines.
• Canvas facilitates robust genomic analysis by inferring critical sample parameters and enabling scalable variant calling.