A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments.

Autor: Bandara YMNDY; Nanotechnology Research Laboratory, Research School of Chemistry, The Australian National University, Canberra, ACT, 2601, Australia., Dutt S; Department of Materials Physics, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia., Karawdeniya BI; Department of Electronic Materials Engineering, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia., Saharia J; Department of Engineering, University of Houston-Clear Lake, Houston, TX, 77058, USA., Kluth P; Department of Materials Physics, Research School of Physics, The Australian National University, Canberra, ACT, 2601, Australia., Tricoli A; Nanotechnology Research Laboratory, Research School of Chemistry, The Australian National University, Canberra, ACT, 2601, Australia.; Nanotechnology Research Laboratory, School of Biomedical Engineering, Faculty of Engineering University of Sydney, Sydney, NSW, 2008, Australia.
Jazyk: angličtina
Zdroj: Small methods [Small Methods] 2024 Jul 05, pp. e2400045. Date of Electronic Publication: 2024 Jul 05.
DOI: 10.1002/smtd.202400045
Abstrakt: 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.
(© 2024 The Author(s). Small Methods published by Wiley‐VCH GmbH.)
Databáze: MEDLINE
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