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