CHOPER filters enable rare mutation detection in complex mutagenesis populations by next-generation sequencing

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Next-generation sequencing (NGS) enables genetic variant identification but struggles with low-frequency variants in heterogeneous populations due to sequencing errors.
• Distinguishing true rare variants from sequencing errors is critical for applications like cancer genomics and infectious disease research.
• Existing methods often require complex experimental modifications or are limited by the skill of the biologist.

Purpose of the Study:

• To develop a novel computational pipeline, Complex Heterogeneous Overlapped Paired-End Reads (CHOPER), for improved detection of sequence variants in complex populations.
• To address the challenge of distinguishing low-frequency variants from sequencing errors in NGS data.
• To facilitate the analysis of genetically heterogeneous samples, including those from All-Codon-Scanning (ACS) mutagenesis.

Main Methods:

• Development of a novel quality filtering and base pruning pipeline named CHOPER.
• Implementation of a fast alignment algorithm with O(n) time complexity specifically designed for heterogeneous data.
• Application of CHOPER to analyze data from a p53 cancer mutant reactivation study utilizing ACS mutagenesis.

Main Results:

• CHOPER demonstrated improved accuracy by approximately 13% compared to traditional Phred quality score-based filtering.
• The pipeline achieved this improvement while discarding only half the number of bases.
• The novel alignment algorithm efficiently processed complex, high-similarity sequence data.

Conclusions:

• CHOPER offers a significant advancement in detecting low-frequency variants in genetically heterogeneous populations using NGS.
• The pipeline enhances the accuracy and efficiency of variant calling, particularly for complex datasets.
• These findings pave the way for broader application of NGS in analyzing challenging genetic samples, such as tumors and microbial populations.