Campolina: a deep neural framework for accurate segmentation of nanopore signals
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.
Fields of Study & Subjects
Nanopore and Nanochannel Transport Studies
Genomics and Phylogenetic Studies
Slime Mold and Myxomycetes Research
Publication details
- Journal / source
- Genome biology
- Publisher
- Springer Science and Business Media LLC
- Volume
- 27
- Issue
- 1
- Pages
- 46–46
- Publication date
- 27 January 2026
- Language
- EN