article Open AccessTop 1% cited
Performance of neural network basecalling tools for Oxford Nanopore sequencing
Genome biology · 2019 · Vol. 20(1) · pp. 129–129
Ryan R. Wick✉(Monash University)Louise M. Judd(Monash University)Kathryn E. Holt(London School of Hygiene & Tropical Medicine)
Abstract
Basecalling accuracy has seen significant improvements over the last 2 years. The current version of ONT's Guppy basecaller performs well overall, with good accuracy and fast performance. If higher accuracy is required, users should consider producing a custom model using a larger neural network and/or training data from the same species.
Genomics and Phylogenetic StudiesRNA modifications and cancerMachine Learning in BioinformaticsBiologyNanopore sequencingGenome BiologyHuman geneticsComputational biologyArtificial neural networkDNA sequencingNanoporeEvolutionary biologyGenomics
MeSH terms
Klebsiella pneumoniaeSoftwareNeural Networks, ComputerSequence Analysis, DNANanopores
Funding
- Bill and Melinda Gates Foundation
- Sylvia and Charles Viertel Charitable Foundation
- Australian Government
Citations
3,207
FWCI
122.66
field-weighted impact
References
32
Percentile
100%
vs. same field & year
Citations per year
Cited by
Opportunities and challenges in long-read sequencing data analysis
Genome biology · 2020 · 2,566 citations
References
Versatile and open software for comparing large genomes
Genome biology · 2004 · 5,792 citations
Fast and accurate de novo genome assembly from long uncorrected reads
Genome Research · 2017 · 3,339 citations
Minimap2: pairwise alignment for nucleotide sequences
Bioinformatics · 2018 · 16,068 citations
Physical–Chemical Properties of Biogenic Selenium Nanostructures Produced by Stenotrophomonas maltophilia SeITE02 and Ochrobactrum sp. MPV1
Frontiers in Microbiology · 2018 · 959 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.
