All2: A tool for selecting mosaic mutations from comprehensive multi-cell comparisons.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Accurate somatic mutation discovery in single cells is hindered by limitations in current analytical methods.
• Existing approaches either miss common mutations by comparing to bulk samples or suffer from low sensitivity when analyzing bulk data.

Purpose of the Study:

• To introduce All2, a computational tool for precise mutation filtering in single cells, independent of bulk sample data.
• To classify base pair substitutions and indels as germline variants, mosaic mutations, or false positives.

Main Methods:

• All2 employs pairwise comparisons of all cells within a dataset.
• The tool accounts for dropped-out regions, making it suitable for whole genome and exome analysis.
• It is applicable to both cloned and amplified cellular data.

Main Results:

• All2 significantly reduces false positives in mutation calls.
• The tool enables sensitive detection of high-frequency somatic mutations.
• Demonstrated utility across various datasets, confirming its effectiveness.

Conclusions:

• All2 provides an accurate and sensitive method for single-cell somatic mutation detection.
• The tool is crucial for high-resolution cell lineage tracing and understanding clonal evolution.
• It overcomes limitations of traditional bulk-based and comparative approaches.