A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments

The success of a nanopore experiment relies not only on the quality of the experimental design but also on the performance of the analysis program utilized to decipher the ionic perturbations necessary for understanding the fundamental molecular intricacies. We have developed a data extraction and analysis framework that leverages parallel computing, efficient memory management (minimizing data aggregation), and vectorization, yielding significant performance enhancement. The open-seek-read-close data loading architecture running on multiple cores underpins the swift analysis of large files where an ~ ×18 improvement was found for a 100-minute-long file (~4.5 GB in size) compared to the more traditional single (cell) array data loading method. The proposed application was benchmarked against five other analysis platforms showcasing significant performance enhancement (>×6 to ×1120). The integrated provisions for batch analysis enable concurrently analyzing multiple files, a crucial capability notably absent in most existing analysis platforms. The batch-analysis feature is particularly vital for high-bandwidth experiments, wherein data is distributed across several files rather than consolidated into a single large file. Furthermore, the application is equipped with multi-level data fitting based on abrupt changes in the waveform. The ability to condense the extracted events to a single file improves data portability (e.g., 16 GB file acquired at 200 kHz with 28,182 events reduces to 47.9 MB in size—343× reduction in size) and enable a multitude of post-analysis extraction to be done efficiently. In summary, the utilization of parallel computing, efficient memory management, and vectorized operations have led to a fast analysis platform that delivers significant performance enhancement, making it well-suited for multiple and sizeable nanopore data file analysis.