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. An event extraction framework is developed that leverages parallel computing, efficient memory management, and vectorization, yielding significant performance enhancement. The newly developed abf-ultra-simple function extracts key parameters from the header critical for the operation of open-seek-read-close data loading architecture running on multiple cores. This underpins the swift analysis of large files where an ≈ × 18 improvement is found for a 100 min-long file (≈4.5 GB) compared to the more traditional single (cell) array data loading method. The application is benchmarked against five other analysis platforms showcasing significant performance enhancement (>2 ×-1120 ×). The integrated provisions for batch analysis enable concurrently analyzing multiple files (vital for high-bandwidth experiments). Furthermore, the application is equipped with multi-level data fitting based on abrupt changes in the event waveform. The application condenses the extracted events to a single binary file improving data portability (e.g., 16 GB file with 28 182 events reduces to 47.9 MB-343 × size reduction) and enables a multitude of post-analysis extractions to be done efficiently.

Conductance-based profiling of nanopores: Accommodating fabrication irregularities.

Solid-state nanopores are nanoscale channels through otherwise impermeable membranes. Single molecules or particles can be passed through electrolyte-filled nanopores by, e.g. electrophoresis, and then detected through the resulting physical displacement of ions within the nanopore. Nanopore size, shape, and surface chemistry must be carefully controlled, and on extremely challenging <10 nm-length scales. We previously developed a framework to characterize nanopores from the time-dependent changes in their conductance as they are being formed through solution-phase nanofabrication processes with the appeal of ease and accessibility. We revisited this simulation work, confirmed the suitability of the basic conductance equation using the results of time-dependent experimental conductance measurements during nanopore fabrication by Yanagi et al., and then deliberately relaxed the model constraints to allow for (i) the presence of defects; and (ii) the formation of two small pores instead of one larger one. Our simulations demonstrated that the time-dependent conductance formalism supports the detection and characterization of defects, as well as the determination of pore number, but with implementation performance depending on the measurement context and results. In some cases, the ability to discriminate numerically between the correct and incorrect nanopore profiles was slight, but with accompanying differences in candidate nanopore dimensions that could yield to post-fabrication conductance profiling, or be used as convenient uncertainty bounds. Time-dependent nanopore conductance thus offers insight into nanopore structure and function, even in the presence of fabrication defects.