Species-specific basecallers improve actual accuracy of nanopore sequencing in plants

Abstract

Background Long-read sequencing platforms offered by Oxford Nanopore Technologies (ONT) allow native DNA containing epigenetic modifications to be directly sequenced, but can be limited by lower per-base accuracies. A key step post-sequencing is basecalling, the process of converting raw electrical signals produced by the sequencing device into nucleotide sequences. This is challenging as current basecallers are primarily based on mixtures of model species for training. Here we utilise both ONT PromethION and higher accuracy PacBio Sequel II HiFi sequencing on two plants, Phebalium stellatum and Xanthorrhoea johnsonii, to train species-specific basecaller models with the aim of improving per-base accuracy. We investigate sequencing accuracies achieved by ONT basecallers and assess accuracy gains by training single-species and species-specific basecaller models. We also evaluate accuracy gains from ONT’s improved flowcells (R10.4, FLO-PRO112) and sequencing kits (SQK-LSK112). For the truth dataset for both model training and accuracy assessment, we developed highly accurate, contiguous diploid reference genomes with PacBio Sequel II HiFi reads. Results Basecalling with ONT Guppy 5 and 6 super-accurate gave almost identical results, attaining read accuracies of 91.96% and 94.15%. Guppy’s plant-specific model gave highly mixed results, attaining read accuracies of 91.47% and 96.18%. Species-specific basecalling models improved read accuracy, attaining 93.24% and 95.16% read accuracies. R10.4 sequencing kits also improve sequencing accuracy, attaining read accuracies of 95.46% (super-accurate) and 96.87% (species-specific). Conclusions The use of a single mixed-species basecaller model, such as ONT Guppy super-accurate, may be reducing the accuracy of nanopore sequencing, due to conflicting genome biology within the training dataset and study species. Training of single-species and genome-specific basecaller models improves read accuracy. Studies that aim to do large-scale long-read genotyping would primarily benefit from training their own basecalling models. Such studies could use sequencing accuracy gains and improving bioinformatics tools to improve study outcomes.

Sequencing accuracy and systematic errors of nanopore direct RNA sequencing

“…From our evaluation, the performance of RODAN holds up well against Guppy in terms of read accuracy, especially for organisms in its training data. As species-specific training data are known to improve performance of nanopore basecallers in those species [Wick et al, 2019, Ferguson et al, 2022, the improvements of RODAN suggest a promising direction for training species-specific basecallers also for dRNA-seq data. Lastly, the presence of the same systematic error patterns in RODAN points to more fundamental causes of errors in the raw signal data, necessitating further development of better pore chemistry to produce higher quality dRNA-seq data.…”

Exploring polymorphic interspecies structural variants in Eucalyptus: Unravelling Their Role in Reproductive Isolation and Adaptive Divergence

“…We performed Hi-C scaffolding, grouping, ordering, and orienting of our previously assembled E. melliodora genome into pseudo-chromosomes (Ferguson, et al, 2022). Leaves were obtained from the reference tree, and a proximity ligation library for chromosome conformation capture was created with a Phase Genomics Proximo Hi-C (Plant) Kit (version 4), according to the manufacturer’s instructions (document KT3040B).…”

Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution

“…These tools can already identify the differences between nuclear and mitochondrial genomes (Danilevsky et al 2022 ) as well as human and bacterial genomes (Bao et al 2021 ). Species-specific basecallers trained on the genomes of specific species of plants are able to outperform ‘universal’ base-callers (Ferguson et al 2022 ). Similarly, a rise in species-specific variant callers such as PEPPER-DeepVariant (Shafin et al 2021 ) suggest information rich differences exist in variants as well.…”