needlestack

needlestack detects low-abundance somatic mutations in multi-sample next-generation sequencing (NGS) data by dynamically estimating sequencing error rates across samples.

Key Features:

• Ultra-Sensitive Detection: Accurately calls mutations at very low variant allele frequencies, enabling detection of subclonal mutations and tumor-derived alterations in body fluids and histologically normal tissue.
• Dynamic Error Rate Estimation: Estimates sequencing error rates by analyzing multiple samples concurrently to model systematic sequencing errors and reduce false positives.
• Robust Performance Across Variations: Demonstrates robust detection across different genomic positions and outperforms state-of-the-art methods for identifying low-abundance mutations.

Scientific Applications:

• Cancer genomics: Detection of low-frequency somatic mutations to study tumor heterogeneity and evolution.
• Liquid biopsy/ctDNA analysis: Identification of tumor-derived alterations in body fluids.
• Somatic mutation analysis in normal tissue: Detection of low-abundance somatic variants in histologically normal tissue.

Methodology:

Estimates systematic sequencing error rates by leveraging data from multiple samples and applies these error models to refine mutation calling in NGS data, reducing false positives.