IMPI: An Interface for Low-Frequency Point Mutation Identification Exemplified on Resistance Mutations in Chronic Myeloid Leukemia

Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs.