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Related Experiment Video

Updated: May 9, 2026

Membrane Transport Processes Analyzed by a Highly Parallel Nanopore Chip System at Single Protein Resolution

Membrane Transport Processes Analyzed by a Highly Parallel Nanopore Chip System at Single Protein Resolution

Published on: August 16, 2016

Data Sieving for Scalable Real-Time Multichannel Nanopore Sensing.

Matteo Cartiglia^1,2, Natan Biesmans^1,2, Wannes Peeters¹

¹imec, Kapeldreef 75, 3001 Heverlee, Belgium.

|May 8, 2026

Summary

This summary is machine-generated.

Data Sieving, a GPU-accelerated framework, significantly reduces data volume in nanopore experiments by selectively storing informative molecular events. This enables scalable, real-time analysis of single-molecule dynamics across diverse applications.

Keywords:

GPU-accelerated processing data reduction edge-computing parallel nanopore experiments real-time event detection single-molecule detection solid state nanopores

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Last Updated: May 9, 2026

Membrane Transport Processes Analyzed by a Highly Parallel Nanopore Chip System at Single Protein Resolution

Membrane Transport Processes Analyzed by a Highly Parallel Nanopore Chip System at Single Protein Resolution

Published on: August 16, 2016

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

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Published on: October 31, 2013

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Published on: March 13, 2017

Area of Science:

Nanotechnology
Biophysics
Data Science

Background:

High-throughput nanopore experiments generate vast data streams, creating storage and processing challenges.
Only a small fraction of this data contains valuable molecular information, limiting experimental scalability.

Purpose of the Study:

To introduce Data Sieving, a GPU-accelerated framework for real-time event detection and selective data storage in nanopore experiments.
To overcome data bottlenecks and enable scalable, high-bandwidth single-molecule analysis.

Main Methods:

Developed a GPU-accelerated acquisition framework integrating real-time event detection.
Employed a lightweight rolling-average and min-max trigger for parallel event candidate identification.
Implemented selective storage of molecular translocation snapshots and continuous baseline monitoring for closed-loop actuation.

Main Results:

Reduced stored data volume by up to 98% while preserving complete molecular signatures.
Successfully captured molecular dynamics across microsecond to second timescales (DNA, proteins, nanoparticles).
Autonomous pore declogging restored conductance, minimizing non-productive states without interrupting measurements.

Conclusions:

Data Sieving links data storage to molecular information content, enabling scalable, real-time operation of parallel nanopore sensors.
Provides a hardware-agnostic foundation for long-duration, high-bandwidth single-molecule experiments and event-driven sensing.
Offers a pathway toward high-bandwidth, multiplexed recording for hundreds of nanopore channels.