Interactive analysis and assessment of single-cell copy-number variations

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell copy-number variations (CNVs) are crucial for understanding cellular heterogeneity and evolution.
• Accurate analysis of single-cell CNVs requires robust computational tools and validated amplification methods.

Purpose of the Study:

• To introduce Ginkgo, a user-friendly, open-source web platform for single-cell CNV analysis.
• To enable automatic construction of copy-number profiles and phylogenetic trees from mapped sequencing reads.
• To compare and evaluate common single-cell amplification techniques for CNV analysis.

Main Methods:

• Development of the Ginkgo web platform for automated CNV profiling.
• Application of Ginkgo to analyze data from five major genomics studies for validation.
• Comparative analysis of three single-cell amplification techniques: degenerate oligonucleotide-primed PCR, whole-genome amplification, and linker-based PCR.

Main Results:

• Ginkgo successfully reproduced results from five major studies, demonstrating its analytical power.
• The platform automatically generates copy-number profiles and phylogenetic trees for related cells.
• Degenerate oligonucleotide-primed PCR emerged as the most consistent method for single-cell CNV analysis among the tested techniques.

Conclusions:

• Ginkgo provides a valuable, accessible tool for single-cell CNV research.
• The findings highlight the importance of selecting appropriate amplification methods for reliable CNV detection.
• This work advances the field of single-cell genomics by offering a validated platform and method recommendations.