bioRxiv · 10.1101/2023.05.16.540986
Icarust, a real-time simulator for Oxford Nanopore adaptive sampling.
Abstract
1SummaryOxford Nanopore Technologies (ONT) sequencers enable real-time generation of sequence data, which allows for concurrent analysis during a run. Adaptive sampling leverages this real-time capability in extremis, rejecting or accepting reads for sequencing based on assessment of the sequence from the start of each read. This functionality is provided by ONTs software, MinKNOW (https://community.nanoporetech.com/downloads). Designing and developing software to take advantage of adaptive sampling can be expensive due to the cost of using real sequencing consumables, using precious samples and preparing sequencing libraries. MinKNOW addresses this issue in part by providing the ability to replay previously sequenced runs for testing, using its playback functionality. However, as we have illustrated here, the sequencing output only partially changes in response to adaptive sampling instructions. Here we present Icarust, a tool enabling more accurate approximations of sequencing runs. Icarust recreates all the required endpoints of MinKNOW to perform adaptive sampling and writes output compatible with current base-callers and analysis pipelines. Icarust is capable of serving nanopore signal to simulate a MinION or PromethION flow cell experiment from any reference genome using either R9 or R10 pore signal. We show that simulating sequencing runs with Icarust provides a realistic testing and development environment for software exploiting the real-time nature of Nanopore sequencing. Availability and ImplementationAll code is open source and freely available here - https://github.com/LooseLab/Icarust. Icarust is implemented in Rust, with an easy to install and use docker container available. Supplementary InformationSupplementary information is available here - https://github.com/Adoni5/Icarust_paper.
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Munro, R. J., Payne, A., Loose, M. W.. 2023-05-18. Icarust, a real-time simulator for Oxford Nanopore adaptive sampling.. https://doi.org/10.1101/2023.05.16.540986
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