Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Genomic variants, including Copy Number Variations (CNVs), are crucial for understanding complex disease susceptibility and resistance.
• The advent of next-generation sequencing has spurred the development of numerous computational tools for accurate CNV identification.

Purpose of the Study:

• To critically review and characterize the technical details influencing CNV detection, breakpoint estimation, and copy number quantification.
• To highlight key insights regarding GC-content and mappability biases in CNV analysis.
• To discuss critical caveats in the evaluation process of CNV detection tools.

Main Methods:

• Review of established structural variant detection approaches: Split-Read, Paired-End Mapping, Read-Depth, and Assembly-based methods.
• Characterization of technical factors impacting CNV detection accuracy.
• Analysis of biases such as GC-content and mappability.

Main Results:

• Detailed technical insights affecting CNV breakpoint and copy number estimation.
• Identification of significant GC-content and mappability biases impacting CNV detection.
• Discussion of common assumptions, limitations, and evaluation challenges in CNV tools.

Conclusions:

• Emphasizes the need for careful consideration of technical details, biases, and evaluation methodologies for robust CNV detection.
• Provides valuable insights and identifies areas for future contributions to advance the state-of-the-art in CNV detection tools.