GeneToCN: an alignment-free method for gene copy number estimation directly from next-generation sequencing reads

Area of Science:

• Genomics
• Bioinformatics
• Population Genetics

Background:

• Genomes contain large regions of segmental copy number variation, often including entire genes.
• Understanding gene copy number is crucial for genetic studies and disease research.

Purpose of the Study:

• To develop and validate a computational method, GeneToCN, for inferring gene copy number from sequencing data.
• To assess the accuracy and applicability of GeneToCN across different genes, individuals, and sequencing technologies.

Main Methods:

• GeneToCN counts gene-specific k-mer frequencies in FASTQ files to predict copy number.
• Validation involved digital droplet PCR (ddPCR) for amylase genes (AMY1, AMY2A, AMY2B) and comparison with other methods for FCGR3 genes.
• Tested on diverse genomic regions (SMN, NPY4R, LPA Kringle IV-2) and sequencing data (Illumina, PacBio, Oxford Nanopore).

Main Results:

• GeneToCN demonstrated a strong correlation (R=0.99) with ddPCR for amylase gene copy number.
• Showed high concordance for FCGR3A and comparable accuracy to other methods for FCGR3B.
• Predicted copy number distributions for SMN, NPY4R, and LPA Kringle IV-2 aligned with published studies.
• Consistent predictions across Illumina, PacBio, and Oxford Nanopore sequencing data.

Conclusions:

• GeneToCN is a robust and accurate computational tool for inferring gene copy number variation.
• The method is reliable across various genes, large cohorts, and different sequencing platforms.
• GeneToCN facilitates large-scale genomic studies of copy number variation.