Hooi, B. | bioRxiv Science

bioRxiv2025

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

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.

bioinformatics↗

bioRxiv2024

A Comparative Review of Deep Learning Methods for RNA Tertiary Structure Prediction

Several deep learning-based tools for RNA 3D structure prediction have recently emerged, including DRfold, DeepFoldRNA, RhoFold, RoseTTAFoldNA, trRosettaRNA, and AlphaFold 3. In this study, we systematically evaluate these six models on three datasets: RNA Puzzles, CASP15 RNA targets, and a newly generated dataset of sequentially distinct RNAs, which serves as a benchmark for generalization capabilities. To ensure a robust evaluation, we also introduce a fourth, more stringent dataset that contains both sequentially and structurally distinct RNAs. We observed that each model predicts the best structure for certain RNAs, and evaluated whether commonly used scoring functions, Rosetta score and ARES, can reliably identify the most accurate structure from the predictions. Finally, since many RNA chains in the Protein Data Bank are part of complexes, we compare the performance of RoseTTAFoldNA and AlphaFold 3 in predicting RNA structures within complexes versus isolated RNA chains extracted from these complexes. This comprehensive evaluation highlights the strengths and limitations of current deep learning-based tools and provides valuable insights for advancing RNA 3D structure prediction.

bioinformatics↗