AI Article Synopsis

  • - The identification of somatic mutations is crucial for cancer profiling and personalized medicine, but existing methods often require manual analysis and lack efficient workflows.
  • - PipeIT is a new software tool packaged as a Singularity container that improves the detection of cancer mutations, matching clinical standards and significantly reducing manual curation effort.
  • - It demonstrated high accuracy in identifying pathogenic variants with 100% positive predictive value and 99% sensitivity, outperforming previous methods, and is easy to deploy and use in any lab.

Article Abstract

The accurate identification of somatic mutations has become a pivotal component of tumor profiling and precision medicine. In molecular diagnostics laboratories, somatic mutation analyses on the Ion Torrent sequencing platform are typically performed on the Ion Reporter platform, which requires extensive manual review of the results and lacks optimized analysis workflows for custom targeted sequencing panels. Alternative solutions that involve custom bioinformatics pipelines involve the sequential execution of software tools with numerous parameters, leading to poor reproducibility and portability. We describe PipeIT, a stand-alone Singularity container of a somatic mutation calling and filtering pipeline for matched tumor-normal Ion Torrent sequencing data. PipeIT is able to identify pathogenic variants in BRAF, KRAS, PIK3CA, CTNNB1, TP53, and other cancer genes that the clinical-grade Oncomine workflow identified. In addition, PipeIT analysis of tumor-normal paired data generated on a custom targeted sequencing panel achieved 100% positive predictive value and 99% sensitivity compared with the 68% to 80% positive predictive value and 92% to 96% sensitivity using the default tumor-normal paired Ion Reporter workflow, substantially reducing the need for manual curation of the results. PipeIT can be rapidly deployed to and ensures reproducible results in any laboratory and can be executed with a single command with minimal input files from the users.

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Source
http://dx.doi.org/10.1016/j.jmoldx.2019.05.001DOI Listing

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