Shimmer: detection of genetic alterations in tumors using next-generation sequence data

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Extensive DNA sequencing (exome and whole-genome) aids in discovering genetic alterations in cancer.
• Stromal contamination and subclonal heterogeneity pose challenges for current detection algorithms.
• Accurate detection of somatic single-nucleotide variants is crucial for cancer research.

Purpose of the Study:

• To introduce Shimmer, a publicly available software for detecting somatic single-nucleotide variants.
• To evaluate Shimmer's performance, particularly in challenging sample conditions.

Main Methods:

• Shimmer utilizes statistical hypothesis testing with multiple testing correction.
• The software was evaluated on tumor and matched normal DNA samples.
• Performance was compared against other available detection software.

Main Results:

• Shimmer demonstrates significantly higher sensitivity and accuracy on highly contaminated or heterogeneous samples.
• The software achieves comparable sensitivity and accuracy on samples of high purity.
• Shimmer provides reliable somatic single-nucleotide variant predictions.

Conclusions:

• Shimmer is an effective tool for detecting somatic single-nucleotide variants, especially in complex cancer samples.
• The software enhances the ability to identify genetic alterations in cancer research.
• Shimmer is available for public use to advance cancer genomics.