ArticleBioinformatics (Oxford, England)2015
Analysis of nanopore data using hidden Markov models.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A maternal exon H splice-site variant leading to pseudohypoparathyroidism type 1B with broad methylation defects in GNAS-differentially methylated regions.Clinical epigenetics · 2026Article
- Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.Bioinformatics (Oxford, England) · 2026Article
- Uncovering Hidden Protein Conformations with High Bandwidth Nanopore Measurements.Nano letters · 2026Article
- A comprehensive long-read sequencing system to assess DNA methylation at differentially methylated regions and imprinting-disorder-related genes.Genome medicine · 2025Article
- The Potential of Nanopore Technologies in Peptide and Protein Sensing for Biomarker Detection.Biosensors · 2025Review
- A generalized protein identification method for novel and diverse sequencing technologies.NAR genomics and bioinformatics · 2024Article
- Machine learning empowered next generation DNA sequencing: perspective and prospectus.Chemical science · 2024Review
- T-S2Inet: Transformer-based sequence-to-image network for accurate nanopore sequence recognition.Bioinformatics (Oxford, England) · 2024Article
- Unidirectional single-file transport of full-length proteins through a nanopore.Nature biotechnology · 2023Article
- Active learning for efficient analysis of high-throughput nanopore data.Bioinformatics (Oxford, England) · 2023Article
- Calling the amino acid sequence of a protein/peptide from the nanospectrum produced by a sub-nanometer diameter pore.Scientific reports · 2022Article
- Article
- [Unsupervised deep learning for identifying the OSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- A Guide to Signal Processing Algorithms for Nanopore Sensors.ACS sensors · 2021Article
- Uncertainties in synthetic DNA-based data storage.Nucleic acids research · 2021Review
- FORK-seq: replication landscape of the Saccharomyces cerevisiae genome by nanopore sequencing.Genome biology · 2020Article
- SquiggleKit: a toolkit for manipulating nanopore signal data.Bioinformatics (Oxford, England) · 2019Article
- Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing.Nature biotechnology · 2019Article
- Nanocall: an open source basecaller for Oxford Nanopore sequencing data.Bioinformatics (Oxford, England) · 2017Article
- MOSAIC: A Modular Single-Molecule Analysis Interface for Decoding Multistate Nanopore Data.Analytical chemistry · 2016Article
Corrections and comments
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2 authors.
Funding
Abstract
motivationNanopore-based sequencing techniques can reconstruct properties of biosequences by analyzing the sequence-dependent ionic current steps produced as biomolecules pass through a pore. Typically this involves alignment of new data to a reference, where both reference construction and alignment have been performed by hand.
resultsWe propose an automated method for aligning nanopore data to a reference through the use of hidden Markov models. Several features that arise from prior processing steps and from the class of enzyme used can be simply incorporated into the model. Previously, the M2MspA nanopore was shown to be sensitive enough to distinguish between cytosine, methylcytosine and hydroxymethylcytosine. We validated our automated methodology on a subset of that data by automatically calculating an error rate for the distinction between the three cytosine variants and show that the automated methodology produces a 2-3% error rate, lower than the 10% error rate from previous manual segmentation and alignment. AVAILABILITY AND IMPLEMENTATION: The data, output, scripts and tutorials replicating the analysis are available at https://github.com/UCSCNanopore/Data/tree/master/Automation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.