ArticleBioinformatics (Oxford, England)2022
NanoSplicer: accurate identification of splice junctions using Oxford Nanopore sequencing.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- SpliSync: Genomic language model-driven splice site correction of long RNA sequencing reads.bioRxiv : the preprint server for biology · 2026Article
- DirectASRM: uncovering allele-specific post-transcriptional RNA modifications through direct RNA sequencing.Bioinformatics (Oxford, England) · 2026Article
- SquiDBase: a community resource of raw nanopore data from microbes.NAR genomics and bioinformatics · 2026Article
- A comparative evaluation of deep learning models for the classification of encoded splice-junction sequences.Frontiers in artificial intelligence · 2026Article
- Detection of Chimeric RNAs from RNA-Seq Data with ChiTaRS 8.0: Insights for Liquid Biopsy and Drug Target Identification.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines.Nature methods · 2025Article
- Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment.Nature methods · 2025Article
- Review
- Article
- A molecular standard for circulating HBV RNA detection and quantification assays in patients with chronic hepatitis B.JHEP reports : innovation in hepatology · 2024Article
- Nanopore Deep Sequencing as a Tool to Characterize and Quantify Aberrant Splicing Caused by Variants in Inherited Retinal Dystrophy Genes.International journal of molecular sciences · 2024Article
- Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment.bioRxiv : the preprint server for biology · 2024Article
- Oxford Nanopore Technology and its Application in Liquid Biopsies.Current genomics · 2023Review
- Nanopore sequencing technology and its applications.MedComm · 2023Review
- Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination.Computational and structural biotechnology journal · 2023Review
- Application of third-generation sequencing to herbal genomics.Frontiers in plant science · 2023Review
- Full-length isoform constructor (FLIC) - a tool for isoform discovery based on long reads.Bioinformatics (Oxford, England) · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
motivationLong-read sequencing methods have considerable advantages for characterizing RNA isoforms. Oxford Nanopore sequencing records changes in electrical current when nucleic acid traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identify splice junctions. Existing strategies include utilizing matched short-read data and/or annotated splice junctions to correct nanopore reads but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages.
resultsWe developed 'NanoSplicer' to identify splice junctions using raw nanopore signal (squiggles). For each splice junction, the observed squiggle is compared to candidate squiggles representing potential junctions to identify the correct candidate. Measuring squiggle similarity enables us to compute the probability of each candidate junction and find the most likely one. We tested our method using (i) synthetic mRNAs with known splice junctions and (ii) biological mRNAs from a lung-cancer cell-line. The results from both datasets demonstrate NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated. AVAILABILITY AND IMPLEMENTATION: NanoSplicer is available at https://github.com/shimlab/NanoSplicer and archived at https://doi.org/10.5281/zenodo.6403849. Data is available from ENA: ERS7273757 and ERS7273453. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.