CNPI: Rapid Analyses of Human Copy Number Data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Existing tools for copy number genotyping from whole-genome sequencing (WGS) data often lack efficiency for large-scale analysis.
• Cloud-based storage of WGS data necessitates tools with rapid compute times, low memory usage, and accessible outputs.

Purpose of the Study:

• To introduce Copy Number Private Investigator (CNPI), a computational toolkit designed for efficient copy number genotyping from WGS data.
• To provide a tool that addresses the limitations of existing methods in terms of speed, memory, and output usability for large-scale genomic analyses.

Main Methods:

• CNPI processes genome-wide copy number estimates from WGS data and a reference annotation file.
• The toolkit performs genotyping of regions of interest, digital karyotype determination, chromosomal sex assessment, and generates an Individual-level Copy Number Score (ICNS).
• Benchmarking was conducted using gold-standard NA12878 data to assess performance and accuracy.

Main Results:

• CNPI can genotype all genes in the human genome in under 2 seconds using 1 CPU and less than 1 GB of memory.
• Benchmarking against NA12878 data showed good results, with user-adjustable thresholds for optimizing sensitivity and specificity.
• The ICNS metric proved useful in distinguishing between unaffected individuals and those with autism or 9p deletion syndrome.

Conclusions:

• CNPI offers a highly efficient and rapid solution for copy number genotyping from WGS data, suitable for cloud environments.
• The toolkit's design facilitates deployment on cloud platforms like AWS Lambda due to its low cost per sample and resource efficiency.
• CNPI provides reliable and precise copy number assessments, enhancing genomic data analysis for various applications, including disease association studies.