ArticleNature communications2024
Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Genome-guided generative adversarial learning enables nanopore adaptive sequencing.Nature communications · 2026Article
- Enhancing low-level genome-edited crop detection and identification in food mixtures using nanopore adaptive sampling: Rice-Soybean mixture as proof-of-concept.NPJ science of food · 2026Article
- Isoform-specific m6A deposition and coordinated splicing shape mammalian transcriptome evolution.Nature communications · 2026Article
- Evaluating the potential and limitations of nanopore adaptive sampling for targeted transcriptome sequencing.Genome biology · 2025Article
- Realfreq: real-time base modification analysis for nanopore sequencing.Bioinformatics (Oxford, England) · 2025Article
- De novo basecalling of RNA modifications at single molecule and nucleotide resolution.Genome biology · 2025Article
- Environmental community transcriptomics: strategies and struggles.Briefings in functional genomics · 2025Review
- Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER.Nature communications · 2024Article
- Adaptive sampling for nanopore direct RNA-sequencing.RNA (New York, N.Y.) · 2023Article
- DeepSelectNet: deep neural network based selective sequencing for oxford nanopore sequencing.BMC bioinformatics · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduces their respective read counts by more than 85%, resulting in an increase in sequencing depth of 47% on average for long non-coding RNAs. We also apply RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as an increase in non-globin reads by 16% on average. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER's modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser .
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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.