PipeIT: A Singularity Container for Molecular Diagnostic Somatic Variant Calling on the Ion Torrent Next-Generation...

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Accurate somatic mutation identification is crucial for tumor profiling and precision medicine.
• Current Ion Torrent analysis on the Ion Reporter platform requires extensive manual review and lacks optimization for custom panels.
• Existing custom bioinformatics pipelines often suffer from poor reproducibility and portability due to complex parameterization.

Purpose of the Study:

• To develop and present PipeIT, a reproducible and portable bioinformatics pipeline for somatic mutation detection.
• To improve the accuracy and efficiency of analyzing Ion Torrent sequencing data for targeted cancer gene panels.
• To reduce the manual curation burden in molecular diagnostics.

Main Methods:

• PipeIT is implemented as a stand-alone Singularity container.
• The pipeline is designed for analyzing matched tumor-normal Ion Torrent sequencing data.
• It automates somatic mutation calling and filtering for targeted sequencing panels.

Main Results:

• PipeIT successfully identifies pathogenic variants in key cancer genes (e.g., BRAF, KRAS, TP53).
• For custom targeted panels, PipeIT achieved 100% positive predictive value and 99% sensitivity.
• This performance significantly surpasses the default Ion Reporter workflow (68-80% PPV, 92-96% sensitivity).

Conclusions:

• PipeIT offers a highly accurate and reproducible solution for somatic mutation analysis.
• The pipeline streamlines workflows, reduces manual review, and enhances precision medicine applications.
• PipeIT is easily deployable and executable with minimal user input, ensuring consistent results across laboratories.