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bioRxiv · 10.1101/2025.07.08.663658

Campolina: A Deep Neural Framework for Accurate Segmentation of Nanopore Signals

Bakic, S.·Friganovic, K.·Hooi, B.·Sikic, M.
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Abstract

Nanopore sequencing enables real-time, long-read analysis by processing raw signals as they are produced. A key step, segmentation of signals into events, is typically handled algorithmically, struggling in noisy regions. We present Campolina, a first deep-learning frame-work for accurate segmentation of raw nanopore signals. Campolina uses a convolutional model to identify event boundaries and significantly outperforms the traditional Scrappie algorithm on R9.4.1 and R10.4.1 datasets. We introduce a comprehensive evaluation pipeline and show that Campolina aligns better with reference-guided ground-truth segmentation. We show that integrating Campolina segmentation into real-time frameworks, Sigmoni and RawHash2, improves their performance while maintaining time efficiency.

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Bakic, S., Friganovic, K., Hooi, B., Sikic, M.. 2025-07-11. Campolina: A Deep Neural Framework for Accurate Segmentation of Nanopore Signals. https://doi.org/10.1101/2025.07.08.663658

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