CERN: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models

• URL: http://arxiv.org/abs/2603.20420v1
• Date: Fri, 20 Mar 2026 18:41:07 GMT
• Title: CERN: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models
• Abstract summary: We propose CERN, a mechanism that trains and utilizes a Hidden Markov Model (HMM) to accurately correct signal errors.<n>CERN consistently improves the overall mapping accuracy of the underlying raw signal analysis tools.
• Score: 0.5675135779323283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nanopore sequencing can read substantially longer sequences of nucleic acid molecules than other sequencing methods, which has led to advances in genomic analysis such as the gapless human genome assembly. By analyzing the raw electrical signal reads that nanopore sequencing generates from molecules, existing works can map these reads without translating them into DNA characters (i.e., basecalling), allowing for quick and efficient analysis of sequencing data. However, raw signals often contain errors due to noise and mistakes when processing them, which limits the overall accuracy of raw signal analysis. Our goal in this work is to detect and correct errors in raw signals to improve the accuracy of raw signal analyses. To this end, we propose CERN, a mechanism that trains and utilizes a Hidden Markov Model (HMM) to accurately correct signal errors. Our extensive evaluation on various datasets including E. coli, Fruit Fly, and Human genomes shows that CERN 1) consistently improves the overall mapping accuracy of the underlying raw signal analysis tools, 2) minimizes the burden on segmentation algorithm optimization with newer nanopore chemistries, and 3) functions without causing substantial computational overhead. We conclude that CERN provides an effective mechanism to systematically identify and correct the errors in raw nanopore signals before further analysis, which can enable the development of a new class of error correction mechanisms purely designed for raw nanopore signals. CERN is available at https://github.com/STORMgroup/CERN. We also provide the scripts to fully reproduce our results on our GitHub page.

Related papers

• LANTERN: A Machine Learning Framework for Lipid Nanoparticle Transfection Efficiency Prediction [22.613971394957368]
  New ionizable lipids for efficient lipid nanoparticles (LNP)-mediated RNA delivery remains a critical bottleneck for RNA-based therapeutics development.<n>Recent advances have highlighted the potential of machine learning (ML) to predict transfection efficiency from molecular structure.<n>Here, we present LANTERN, a robust ML framework for predicting transfection efficiency based on ionizable lipid representation.
  arXiv  Detail & Related papers  (2025-07-03T22:49:49Z)
• A Solid-State Nanopore Signal Generator for Training Machine Learning Models [0.0]
  We introduce a nanopore signal generator capable of producing extensive synthetic datasets for machine learning applications.<n>We train deep learning models to detect translocation events directly from raw signals, achieving over 99% true event detection with minimal false positives.
  arXiv  Detail & Related papers  (2025-04-07T19:56:35Z)
• Cell Graph Transformer for Nuclei Classification [78.47566396839628]
  We develop a cell graph transformer (CGT) that treats nodes and edges as input tokens to enable learnable adjacency and information exchange among all nodes. Poorly features can lead to noisy self-attention scores and inferior convergence. We propose a novel topology-aware pretraining method that leverages a graph convolutional network (GCN) to learn a feature extractor.
  arXiv  Detail & Related papers  (2024-02-20T12:01:30Z)
• Towards Faster k-Nearest-Neighbor Machine Translation [51.866464707284635]
  k-nearest-neighbor machine translation approaches suffer from heavy retrieve overhead on the entire datastore when decoding each token.<n>We propose a simple yet effective multi-layer perceptron (MLP) network to predict whether a token should be translated jointly by the neural machine translation model and probabilities produced by the kNN.<n>Our method significantly reduces the overhead of kNN retrievals by up to 53% at the expense of a slight decline in translation quality.
  arXiv  Detail & Related papers  (2023-12-12T16:41:29Z)
• Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics [44.97217246897902]
  We address the challenge of using energy-based models to produce high-quality, label-specific data in structured datasets. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing. We use a novel training algorithm that exploits non-equilibrium effects.
  arXiv  Detail & Related papers  (2023-07-13T15:08:44Z)
• Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability [5.623232537411766]
  We develop efficient and scalable approaches for fitting GP models and fast convolution kernels. We implement these improvements by building an open-source Python library called xGPR. We show that xGPR generally outperforms convolutional neural networks on predicting key properties of proteins and small molecules.
  arXiv  Detail & Related papers  (2023-02-07T07:06:02Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Deep metric learning improves lab of origin prediction of genetically engineered plasmids [63.05016513788047]
  Genetic engineering attribution (GEA) is the ability to make sequence-lab associations. We propose a method, based on metric learning, that ranks the most likely labs-of-origin. We are able to extract key signatures in plasmid sequences for particular labs, allowing for an interpretable examination of the model's outputs.
  arXiv  Detail & Related papers  (2021-11-24T16:29:03Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Deep Neural Network: An Efficient and Optimized Machine Learning Paradigm for Reducing Genome Sequencing Error [27.84400682210533]
  It has become known that most of the platforms used in the sequencing process produce significant errors. On the two main types of genome errors - substitution and indels - our work is focused on correcting indels. A deep learning approach was used to correct the errors in sequencing the chosen dataset.
  arXiv  Detail & Related papers  (2020-10-06T08:16:35Z)
• ProDOMA: improve PROtein DOMAin classification for third-generation sequencing reads using deep learning [0.0]
  We introduce ProDOMA, a deep learning model that conducts domain classification for third-generation sequencing reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In the experiments on simulated reads of protein coding sequences and real reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification.
  arXiv  Detail & Related papers  (2020-09-26T13:30:54Z)
• Deep learning for gravitational-wave data analysis: A resampling white-box approach [62.997667081978825]
  We apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. CNNs were quite precise to detect noise but not sensitive enough to recall GW signals, meaning that CNNs are better for noise reduction than generation of GW triggers.
  arXiv  Detail & Related papers  (2020-09-09T03:28:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.