Work

Campolina: a deep neural framework for accurate segmentation of nanopore signals

Year: 2026

Type: article

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 framework 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.

Source: Genome biology

DOI: 10.1186/s13059-026-03950-1

Authors: Sara Bakić, Krešimir Friganović, Bryan Hooi, Mile Šikić

Institutions: National University of Singapore, Genome Institute of Singapore, Singapore-ETH Centre, University of Zagreb

Language: English

Topic: Genomics and Phylogenetic Studies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,490

Funders: Oxford Nanopore Technologies, Medical Research Council, National Medical Research Council