ArticleGenome biology2024
RUBICON: a framework for designing efficient deep learning-based genomic basecallers.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Nanopore Sequencing for Chikungunya Virus: Principles and Application.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.Bioinformatics (Oxford, England) · 2026Article
- Low power reprogrammable DNA basecaller with an efficient HMM accelerator for real time nanopore sequencing.Scientific reports · 2026Article
- Advances and challenges in non-canonical nucleic acids data storage.Nature communications · 2026Review
- Review
- FPGA-based accelerator for adaptive banded event alignment in nanopore sequencing data analysis.BMC bioinformatics · 2025Article
- Long-Read Sequencing for the Rapid Response to Infectious Diseases Outbreaks.Current clinical microbiology reports · 2025Review
- Shedding light on DNA methylation and its clinical implications: the impact of long-read-based nanopore technology.Epigenetics & chromatin · 2024Review
- RUBICON: a framework for designing efficient deep learning-based genomic basecallers.Genome biology · 2024Article
- TargetCall: eliminating the wasted computation in basecalling via pre-basecalling filtering.Frontiers in genetics · 2024Article
- RawHash: enabling fast and accurate real-time analysis of raw nanopore signals for large genomes.Bioinformatics (Oxford, England) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.
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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.