Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single cell sequencing reveals genomic heterogeneity but faces challenges from whole genome amplification.
• Amplification introduces allelic bias and copy errors, violating bulk sequencing variant caller assumptions.

Purpose of the Study:

• To develop a robust method for accurate single nucleotide variant calling in single cell DNA sequencing data.
• To address the limitations of amplification bias and errors in single cell genomics.

Main Methods:

• ProSolo probabilistically models single cell and bulk sequencing data jointly.
• Integrates site-specific multiple displacement amplification (MDA) biases efficiently.
• Implements a novel approach for reliable false discovery rate control.

Main Results:

• Achieves higher accuracy in single nucleotide variant calling and genotyping compared to state-of-the-art tools.
• Supports imputation of genotypes with insufficient coverage.
• Provides a scalable and computationally efficient solution.

Conclusions:

• ProSolo offers a significant advancement for accurate single cell variant analysis.
• Enables more reliable insights into cell-to-cell genomic heterogeneity.
• Provides an extendable framework for future genomic analyses.