AutoNanopore: An Automated Adaptive and Robust Method to Locate Translocation Events in Solid-State Nanopore Current Traces

Abstract

Solid-state nanopore sequencing has shown impressive performances in several research scenarios but is still challenging, mainly due to the ultrafast speed of DNA translocation and significant noises embedded in raw signals. Hence, event detection, aiming to locate precisely these translocation events, is the fundamental step of data analysis. However, existing event detection methods use either a user-defined global threshold or an adaptive threshold determined by the data, assuming the baseline current to be stable over time. These disadvantages limit their applications in real-world application scenarios, especially considering that the results of different methods are often inconsistent. In this study, we develop an automated adaptive method called AutoNanopore, for fast and accurate event detection in current traces. The method consists of three consecutive steps: current trace segmentation, current amplitude outlier identification by straightforward statistical analyses, and event characterization. Then we propose ideas/metrics on how to quantitatively evaluate the performance of an event detection method, followed by comparing the performance of AutoNanopore against two state-of-the-art methods, OpenNanopore and EventPro. Finally, we examine if one method can detect the overlapping events detected by the other two, demonstrating that AutoNanopore has the highest coverage ratio. Moreover, AutoNanopore also performs well in detecting challenging events: e.g., those with significantly varying baselines.

Portable nanopore-sequencing technology: Trends in development and applications

“…Several automated tools have been proposed to perform event detection by considering the mean value and/or standard deviation of the current values, such as Transalyzer, EasyNanopore ( Tu et al, 2021 ), and EventPro ( Bandara et al, 2021 ). Recently, we have also developed two event detection tools focusing on the local range close to the peak current value in each segmented slice: the former uses a straight-forward statistical approach to select the real events from a considerable amount of slices ( Sun et al, 2022 ), while the latter takes into account the ratio of the diameters of the molecule relative to that of the nanopore. In recent years, machine learning-based methods have also been proposed to perform event detection tasks, and a transformer-based method has been developed ( Dematties et al, 2022 ), as well as a bi-path method to detect the events from low signal-to-noise current traces ( Dematties et al, 2021 ), showing impressive performances.…”

Procedural Data Processing for Single-Molecule Identification by Nanopore Sensors

“…With the rapid development of nanopore sensing, there is great interest in developing tools and methods for robust data analysis within nanopore fields [ 5 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Currently, most of the data analyses have been performed with various open source and commercial software.…”

“…Forstater’s group developed an improved data analysis tool called Modular Single-Molecule Analysis Interface (MOSAIC) for data measured from both biological and solid-state nanopores experiments based on two key algorithms: ADEPT for short-lived events and CUSUM + for longer events [ 12 ]. Meanwhile, Sun’s group provided an automated adaptive and robust AutoNanopore platform for event detection in solid-state nanopore current traces with the highest coverage ratio [ 13 , 14 ]. Dekker et al introduced a local baseline recalculation algorithm by iterative operation for separating DNA folded and unfolded states within translocation events [ 21 ].…”

Analysis of Nanopore Data: Classification Strategies for an Unbiased Curation of Single-Molecule Events from DNA Nanostructures

“…In recent years, a handful of analysis tools 5−8 (each with their own unique limitations on event fitting) have been proposed to facilitate such processing of raw nanopore data at a level that can suit most analysis needs. In practice, despite the availability of analysis tools that support deeper functionalities such as OpenNanopore, 6 MOSAIC, 5 Transalyzer, 7 AutoNanopore, 8 and others, analyte characterization is often limited to basic, single-valued metrics of their translocation signals and ignores information encoded in the internal structure of the events. Distinguishing molecules with similar physical and chemical characteristics under this scheme can be challenging, however, as significant overlap can exist in the distributions of their extracted parameters.…”

“…Subpopulations of events are then assigned to target molecules based on clustering of these statistics. In recent years, a handful of analysis tools – (each with their own unique limitations on event fitting) have been proposed to facilitate such processing of raw nanopore data at a level that can suit most analysis needs. In practice, despite the availability of analysis tools that support deeper functionalities such as OpenNanopore, MOSAIC, Transalyzer, AutoNanopore, and others, analyte characterization is often limited to basic, single-valued metrics of their translocation signals and ignores information encoded in the internal structure of the events.…”