A dual context-aware basecaller for nanopore direct RNA sequencing

Domain: physics
Published: 2026-01-21
Editorial outcome: major_revision

Abstract
Nanopore direct RNA sequencing (DRS) offers distinct advantages for transcriptome analysis over the traditional high-throughput RNA sequencing methods by preserving native RNA modifications, eliminating polymerase chain reaction bias, and simplifying the workflow. However, its high basecalling error rate remains a significant hurdle. Here we introduce Coral, a dual context-aware nanopore DRS basecaller that uses a Transformer-based encoder-decoder architecture to capture contextual dependencies at both the signal and sequence levels, substantially improving accuracy. Coral achieves up to a 6.17% improvement in accuracy on human RNA samples compared to Oxford Nanopore Technologies’ Dorado basecaller. This improved accuracy enables the detection of 26% more annotated transcript isoforms. Coral also enhances the downstream haplotype phasing, reducing switch errors by up to 78.8% and Hamming errors by 76%, while phasing 36% more single nucleotide polymorphisms.

Introduction
Advancements in RNA sequencing technologies have significantly enhanced our understanding of transcriptomes, revealing intricate details about gene expression, splicing events, and RNA modifications1. Traditional high-throughput RNA sequencing (RNA-seq) typically involves the synthesis of complementary DNA (cDNA) followed by sequencing. However, the cDNA synthesis and amplification tend to introduce biases that potentially affect the accuracy of the transcript quantification2. Moreover, the critical information of native RNA modifications is often lost in the cDNA conversion process, hindering comprehensive epitranscriptomics analyzes3. Nanopore direct RNA Sequencing (DRS) developed by Oxford Nanopore Technologies (ONT), enables the direct sequencing of RNA molecules without the need for reverse transcription into cDNA4,5. It has emerged as a powerful alternative to traditional RNA-seq methods, yielding full-length and strand-specific RNA sequences and enabling the direct detection of nucleotide analogs in RNA6–11.

Recent studies have demonstrated the versatility and power of nanopore DRS in transcript structure identification, novel transcripts discovery, and allele-specific analysis. For example, Glinos et al.12 used nanopore DRS to elucidate the genetic effects of rare and common variants on the transcriptome. The analysis provided new insights into how genetic variants influence transcript structure and splicing, contributing to our understanding of gene regulation and disease risk. Berger et al.13 demonstrated that haplotype phasing could be significantly enhanced using the allele-specific information within nanopore DRS data, offering longer and more accurate haplotypes. In a systematic comparison of long-read RNA sequencing methods, Palacios et al.14 demonstrated that libraries with longer reads can improve transcript identification accuracy, further solidifying nanopore DRS as a preferred method for transcriptomic analysis over relying solely on increased sequencing depth. Wang et al.15 investigated a coupled nanopore DRS with the adaptive sampling function to enable the selective enrichment of target transcriptome and identification of unknown transcripts.

Despite significant advancements, a major limitation of nanopore DRS is its inherently high error rate16. The errors primarily arise from noisy ionic-current signals and complex sequence structures (e.g., long homopolymer stretches, hairpins) passing through the pore17. To improve the basecalling accuracy of nanopore sequencing, deep learning-based basecallers have been widely proposed to handle complex noise interference. For example, Chiron18 combines Convolutional Neural Network (CNN) and Recurrent Neural Network to achieve end-to-end basecalling without event segmentation. Causalcall19 employs a modified temporal CNN with a Connectionist Temporal Classification (CTC) decoder to model long-range sequence features. SACall20 incorporates self-attention mechanisms to capture long-term dependencies of electrical currents. Compared to nanopore DNA sequencing basecalling, nanopore DRS basecalling is underexplored5. The early ONT-developed basecaller Guppy employs an alternating gated recurrent unit (GRU) and reverse-GRU layers network architecture combined with a CTC decoder21 to predict variable-length nanopore current signals17. The Guppy DRS model was reported to achieve around 90% accuracy across various organisms in both native and in vitro transcribed samples16. Dorado22 is the latest commercially released basecaller developed by ONT, featuring code optimization across multiple platforms and support for the latest DRS kits (e.g., RNA004). RODAN23 represents one of the most successful third-party open source basecallers of nanopore DRS for demonstrating competitive performance with Guppy. It adopts a fully convolutional architecture and CTC decoder to ensure efficient parallel processing. GCRTcall24 is a recent third-party DRS basecaller that integrates a Transformer25 encoder with gated convolutional networks and relative positional encoding, achieving state-of-the-art performance.

Deep learning-based basecallers have achieved promising results, especially in nanopore DNA sequencing26, yet the accuracy of nanopore DRS is unsatisfactory. This discrepancy is primarily due to the structural differences between RNA and DNA molecules7. The inherent noise in raw electrical signals, compounded by RNA-specific features such as chemical modifications3 and secondary structures27, and slower nanopore translocation speed (70 bp per second for RNA002 and 130 bp per second for RNA004) than DNA (450 bp per second)28, necessitate more specific models for nanopore DRS. Despite numerous basecalling models, most are predominantly designed for nanopore DNA sequencing and rely on an encoder-CTC architecture that estimates symbol probability based on signal information alone. While the reliance on the conditional independence assumption between output symbols in CTC enables parallel decoding, this restricts the ability of the model to capture dependencies among output symbols during decoding.

To address the aforementioned issues, in this study, we propose a dual context-aware basecalling model namely Coral for nanopore DRS. Coral employs an autoregressive Transformer-based encoder-decoder architecture to learn the contextual dependencies at both the signal and sequence levels, and utilizes the dual attention mechanisms (self-attention and cross-attention) for richer sequence contextual learning and capturing the alignment between the raw signals and output RNA sequence. Incorporating dual contextual dependencies introduces an inductive bias that helps direct the model toward a more plausible decoding pathway. Beam search and parallel attention computation are incorporated in decoding to enhance computational efficiency. A fast mode, enabling the simultaneous prediction of multiple bases, is also provided to optimize throughput for large-scale analyzes. Coral was systematically validated against the latest versions of ONT’s basecallers Guppy and Dorado, as well as third-party basecallers RODAN and GCRTcall, across various species and sequencing kits, demonstrating a substantial improvement in basecalling accuracy.

Results
Overview of Coral
Coral employs an encoder-decoder framework for basecalling (Fig. 1a). The encoder leverages a CNN-Transformer network to process the raw electrical signal effectively, capturing complex temporal dependencies and generating high-level signal features (Fig. 1b). The decoder operates in an auto-regressive manner, predicting the current base conditioned on the previously decoded bases (Fig. 1c). Specifically, the decoder utilizes a Transformer network incorporating dual attention mechanisms, i.e., causal self-attention and cross-attention. Causal self-attention models intricate relationships within the output sequence, enabling richer contextual learning, while cross-attention implicitly learns the alignment between signal and sequence contextual representation. The base probabilities are derived from the final decoder features, and the optimal sequence is identified using a beam search strategy. During the training of Coral, an additional regularization loss is introduced to encourage the alignment produced by the final cross-attention layer to be monotonic, reflecting the nature of the sequencing process. Coral can work in two modes: the default mode predicts a single base per step to achieve maximum precision; while the fast mode, denoted as Coral-FAST, predicts k consecutive bases simultaneously to optimize throughput. More details on the implementation of Coral and Coral-FAST can be found in the “Methods” section and the Supplementary Materials. In the following sections, we demonstrate Coral’s performance on real nanopore DRS data of various species, showing substantial improvements in basecalling accuracy and downstream transcriptome analyzes, i.e., transcript discovery and quantification, allele-specific expression (ASE) analysis, and haplotype phasing.

Basecalling accuracy
To validate the basecalling performance, Coral was compared with the latest version of Guppy (v6.5.7)29, RODAN (v1.0)23, GCRTcall (v1.0)24, and Dorado (v0.8.3)22 using real nanopore DRS datasets of both RNA001/002 and RNA004 kits. Guppy and Dorado are the official basecallers provided by ONT and have been trained with broadly representative data. RODAN and GCRTcall models were well trained in the work of refs. 23, 24, respectively. For RNA001/002 reads basecalling, Coral was trained on different sequencing samples of Human (from the NA12878 project, BHAM_Run1)7, Arabidopsis23, Epinano synthetic constructs30, Caenorhabditis elegans (C. elegans)31, Escherichia coli (E. coli)32, Mus musculus (Mouse)33, Populus trichocarpa (Poplar)34, Saccharomyces cerevisiae S288C (Yeast)35,36, and Danio rerio (Zebrafish)37. RNA004 sequencing is featured by increased translocation speed (130 bp/s) and improved throughput compared to RNA001/002. Also, the RNA004 kit chemistry operates at a 4 kHz sampling rate, compared to 3 kHz in RNA001/002, enhancing the resolution of the recorded electrical signal. These advancements necessitate the development of specialized basecalling models to interpret the distinct signal characteristics of RNA004 data accurately. To address this, a distinct Coral model was trained on a publicly available Universal Human Reference RNA004 sample38.

All basecallers were tested on RNA001/002 sequencing datasets from six representative species namely Human (from the NA12878 project, HOPKINS_RUN1)7, Arabidopsis39, Mouse40, Yeast36, Poplar34 and Zebrafish37. The test sets are completely independent of the training sets, with the majority of the reads coming from different biological samples. This allowed us to validate the performance of the basecallers across a diverse range of transcriptome data. For RNA004 sequencing, the basecallers (except RODAN that does not support RNA004 reads) were evaluated on the independent RNA004 sample of the HEK293T human cell line from Singapore Nanopore Expression Project41. More details of the datasets and experimental settings can be found in the “Methods” section, as well as in Tables S1–S3.

The basecalling performance was evaluated in terms of six metrics, including read accuracy, mismatch rate, insertion rate, deletion rate, read length (the length of a read excluding the left and right soft clips), and the number of unaligned reads. There is no available data on unaligned reads for Guppy in the RNA001/002 test datasets, which include only the reads successfully basecalled by Guppy and aligned to the reference sequence. On the RNA001/002 test datasets (Fig. 2a–f), Coral outperformed other basecallers across all evaluation metrics. Coral achieved an advancement in base calling accuracy with an average median read accuracy of ~97%. The improvement is especially notable in the human, Yeast, and Arabidopsis datasets. The average median mismatch, insertion, and deletion rates of Coral were 0.96%, 0.69%, and 1.20%, respectively, markedly lower than those of other basecallers. Coral also yielded the fewest unaligned reads and the longest read lengths, which verifies that higher base calling accuracy can facilitate better alignment to the reference sequence. On the HEK293T RNA004 sample (Fig. 2g), Coral maintained strong performance by achieving the highest median read accuracy of 99.52%. The results suggest that RNA004 reads can be base called more accurately than those from RNA001/002. Coral also achieved the lowest median mismatch, insertion, and deletion rates (0.10%, 0.00%, and 0.14%, respectively). Its identified read length and the proportion of unaligned reads were comparable to Dorado. The findings highlight Coral’s high accuracy and robustness on RNA004 chemistry.

To rigorously assess Coral’s ability to generalize to previously unseen data, we also performed leave-one-out cross-validation experiments at both the species and chromosome levels. For the RNA001/002 datasets, this involved a leave-one-species-out approach where Coral, RODAN, and GCRTcall were trained on all but one species and subsequently evaluated on the held-out species’ test reads. A similar leave-one-chromosome-out validation was conducted for the human RNA004 dataset, with models trained after excluding all reads from the representative chromosome 22. Note that there are no training pipelines available for Guppy and Dorado, yet they were also included in the comparison by using the corresponding original official models, i.e., no leave-one-out training was done for Guppy and Dorado. In the leave-one-species-out evaluations on the RNA001/002 datasets, Coral achieved a robust average median read accuracy of 95.19% on the unseen species—a modest decrease of only 1.74% compared to the fully trained model—while consistently outperforming the other retrainable basecallers as well as Guppy and Dorado (Table S4). On the held-out chromosome evaluation on the human RNA004 dataset, Coral achieved a median read accuracy of 97.78%, higher than that of RODAN (92.44%) and GCRTcall (94.15%), and comparable to that of Dorado (98.13%) (Table S5). These findings indicate that Coral can avoid overfitting to reference-specific details and learn generalizable and robust mapping from raw electrical signals to nucleotide sequences, making it a reliable tool for analyzing novel species or genomic regions.

Given that homopolymer sequencing errors are a significant type of error in nanopore sequencing26, we investigated the accuracy and the proportions of insertion, mismatch, and deletion of the basecallers on calling homopolymers of varying lengths on the RNA001/002 human dataset (Fig. 3a). Coral is more accurate than the other basecallers for all homopolymer lengths, and it works better in identifying shorter homopolymers. Since short homopolymers (lengths of 2 and 3) constitute a significant proportion (90.64%) of nanopore DRS data (Fig. 3b), Coral can effectively refrain the primary homopolymer errors. Similar results can also be observed on other species (Figs. S1 and S2) and the HEK293T RNA004 dataset (Fig. S3a, b).

To evaluate the performance of Coral with respect to different read lengths, we assessed the basecalling accuracy as a function of read length. Since the majority of read lengths fall within the range of (0, 2000] (Fig. 3c), the basecalling accuracy of the five basecallers on the RNA001/002 datasets (Figs. 3d and S4) and the HEK293T RNA004 dataset (Fig. S3c, d) is plotted across different read length intervals within (0, 2000]. It can be seen that the accuracy of all the basecallers decreases as the read length increases, whereas Coral obtained higher accuracy than the others across all length intervals.

We further explored the match accuracies of the basecallers at the individual base level (Fig. 3e). The bases ‘C’ and ‘U’ are more likely to be wrongly identified, and the mismatch errors between them (‘C’ to ‘U’ and ‘U’ to ‘C’) constituted the highest proportion of mismatch errors. The deletion errors of ‘C’ and ‘U’ also account for the majority of deletion errors. This is potentially due to their current signal similarity and longer dwelling time, a finding consistent with the previous study16. Similar results are also observed on the HEK293T RNA004 dataset, and Coral reduced the C/U mismatch error rate from ~1% on RNA001/002 reads to around 0.25% on RNA004 reads (Fig. S3e).

Effectiveness of modeling contextual dependencies
On top of signal context dependencies as considered in most CTC-based basecallers, the encoder-decoder architecture of Coral is also intended to capture the contextual dependencies between output bases. Accurate context information is essential for effective decoding in Coral, as error contexts can lead to noisy contextual representations, which are detrimental to subsequent decoding processes. To investigate the impact of k-mer contextual information on basecalling accuracy, we show the accuracy of the decoded k-mers across varying lengths (5 to 11 bases) (Figs. 3f, S3f, and S5) and the accuracy on predicting the next base given a correct 5-mer context (Figs. 3g and S3g). Coral consistently demonstrated superior accuracy. The advantage increases as the length of k-mers increases, i.e., Coral achieved significantly higher accuracy (>0.8) compared to other basecallers (<0.5) in 9-mer, 10-mer, and 11-mer contexts (Fig. S6). Further investigation into the impact of erroneous 5-mer contexts revealed that while an incorrectly predicted 5-mer context increases the error rate of the immediately subsequent base prediction compared to predictions from correct 5-mer contexts (Fig. S7a), this detrimental impact progressively diminishes after ~4 subsequent bases (Fig. S7b). These findings collectively highlight Coral’s superior utilization of dual contextual information for robust decoding and its resilience to contextual errors.

To evaluate the contributions of key components for modeling contextual dependencies, we performed a series of ablation experiments using the Coral framework on the RNA001/002 and RNA004 test datasets, with detailed architectures and parameter sizes provided in Tables S6 and S11. We analyzed the relationship between model scale and basecalling performance across all ablated models to disentangle the effects of architecture and size (Figs. S10 and S11). The results confirm two key findings. First, comparing models at a similar parameter scale highlights that architectural choices are critical. For instance, Coral-L3 (356 MB) significantly outperforms both Coral-CTC (292 MB, where the original Transformer decoder of Coral was replaced by a single linear layer trained with the CTC loss) and Coral-LSTM (309 MB, where LSTM is short for long short-term memory network), affirming the advantage of the auto-regressive Transformer decoder (Table S7). Likewise, comparing Coral-L3 with the LSTM-Transformer (351 MB) highlights the superiority of the CNN-Transformer encoder (Table S8). Comparison between the two modes of Coral, i.e., the normal mode Coral (predicting a single base per step, 548 MB) and the fast mode Coral-FAST (predicting k bases per step, 554 MB), revealed that the impact of symbol prediction granularity, with Coral yielding slightly better performance (Table S9). Second, the performance of Coral scales with model size, as demonstrated by accuracy gains from Coral-S (44 MB) and Coral-M (115 MB) to the larger Coral-Lx variants (356–548 MB, where x=3, 6, and 12 represents the number of decoder layers) (Table S10). The performance gain becomes moderate when the model size reaches a certain scale, likely due to limitations in training data and the inherent complexity of the task—a trend consistent with neural scaling laws. A similar trend in performance scaling with respect to model size was also observed on the RNA004 dataset (Table S12). Compared with the official Dorado models, Coral also achieved superior performance at comparable model scales on both the RNA002 and RNA004 datasets (Figs. S10 and S11). Overall, the ablation studies provide a robust justification of the current Coral design, demonstrating that the full Transformer encoder-decoder architecture, when appropriately scaled, is critical for achieving state-of-the-art basecalling performance.

Transcript isoform discovery and quantification
To verify the effects of improved basecalling accuracy on the downstream transcript isoform discovery, we applied the five basecallers to nanopore DRS data of three human cell lines, namely A549, HCT116, and NA12878/GM12878, on which high-quality annotated references are available, and for A549 and HCT116 matched deep-coverage Illumina short-read RNA-seq data are also available to enable in silico validation (Table S15). The average median basecalling accuracy of Coral exceeded that of Guppy, RODAN, GCRTcall, and Dorado by 6.87%, 5.23%, 4.33%, and 4.32%, respectively, across the three datasets (Table S16). Coral and Guppy exhibited comparable read lengths, whereas RODAN and Dorado showed a shorter read length, especially on the NA12878 dataset.

Based on the basecalling results, ESPRESSO42, a robust computational tool designed for error-prone long sequencing reads, was utilized to detect the splice junctions (SJs) and transcript isoforms. We investigated the de novo SJs identification, supported by at least two reads with perfect alignments around canonical splice sites. Coral’s outputs achieved comparable precision and outperformed the other basecallers in terms of true positive counts, recall rate, and F1 scores (Fig. 4a). The mean perfect reads count (MPRC) indicates that Coral’s outputs support SJs with a higher number of perfectly aligned reads compared to other basecallers.

We further compared the ESPRESSO results against the well-studied human transcript annotations to discover the transcript isoforms. Based on the confidence and configuration of SJs, ESPRESSO categorizes reads into full splice match (FSM), incomplete splice match (ISM), novel in catalog (NIC), novel not in catalog (NNC), single-exon (SEX), and not completely determined (NCD)43. With Coral’s basecalling outputs, ESPRESSO identified the highest number of FSM reads across all datasets, indicating a superior ability to accurately capture and represent known transcript structures (Fig. 4b). Compared to Guppy, Coral identified comparable ISM reads while maintaining the fewest NCD reads that signify reads with low-confidence SJs. RODAN exhibited a higher read accuracy than Guppy (Table S16), yet it yielded the fewest FSM reads, and the highest number of ISM reads in most datasets. This discrepancy may stem from RODAN’s shorter read alignments, which limit its ability to detect complete SJs, and a similar pattern was also observed with Dorado on the NA12878 dataset.

Coral also led to the highest number of annotated transcript isoforms across all three datasets (Figs. 4c and S12). Transcripts commonly discovered by all basecallers exhibited higher expression levels compared to those detected by only one or two basecallers (Figs. 4c and S12), as low-abundance transcripts are generally more difficult to detect. Among these low-abundance annotated transcripts, Coral exhibited the highest sensitivity by identifying the largest number of such transcripts. Furthermore, among the transcripts uniquely detected by each basecaller, Coral consistently identified the largest number of low-abundance transcripts, and these Coral-specific isoforms were further corroborated by supporting evidence in the Illumina short-read data (2219 in the A549 dataset, 1814 in the HCT116 dataset), significantly outperforming the other basecallers (Figs. 4c and S12). Quantification of these Coral-specific isoforms also showed the strongest concordance with the Illumina data (Fig. S13). Coral was also demonstrated to identify the most annotated transcripts across all read proportions (Fig. S14). As the read proportion increases, Coral’s advantage over other basecallers expands.

To further benchmark the transcript identification and quantification accuracy, we performed an additional experiment using the publicly available HEK293T dataset42 spiked with 25 ng of SIRV-Set1-E2 RNA (Lexogen, lot no. 001418), which comprises 68 artificial transcripts with known sequences and precisely defined concentrations spanning two orders of magnitude. The detected transcripts from different basecallers were then compared with the known SIRV transcripts, and we assessed the correlation between the estimated transcript abundances and the known input concentrations (fmol/ul). The results (Figs. 4d, S15, and S16) highlight the superior performance of Coral in both transcript detection and quantification accuracy. Coral and Dorado successfully identified the largest number of SIRV transcripts, followed by Guppy and GCRTcall, while RODAN detected the fewest SIRV transcripts. Coral also achieved the highest Pearson and Spearman correlation coefficients for abundance estimation compared to the other basecallers.

For the annotated transcripts uniquely detected by Coral, the majority of assigned reads by Coral were classified as FSM or ISM, whereas the same reads in Guppy, RODAN, GCRTcall, and Dorado were mainly classified as NCD or ISM (Fig. 5a). The results indicated that Coral can significantly bolster the reliability of transcript annotation. On the ten most abundant annotated transcripts uniquely discovered by Coral, the read accuracy (by transcriptome alignment) was consistently higher than that of other basecallers (Tables S17–S19). We then inspected the reads assigned to the most abundant annotated transcripts that were identified exclusively by Coral, including TM4SF1-201 in A549, CHCHD10-202 in HCT116, and HLA-A-203 in NA12878 (Fig. 5b). On these transcripts, the reads basecalled by Coral were primarily classified as FSM, whereas the corresponding reads basecalled by other basecallers were mainly categorized as NCD. Coral also achieved a significantly higher proportion of perfectly aligned reads at the splice junctions of these transcript models, further confirming these findings (Fig. S17). These results demonstrated Coral’s superior capability to maintain the integrity of SJs, thus significantly enhancing the reliability of transcript identification and quantification.

We further investigated the biological roles of the uniquely identified transcripts. In the NA12878 dataset, the transcripts uniquely identified by Coral exhibited the highest abundances, with a predominant association with immune regulation. Specifically, the transcripts ENST00000376809.1044, ENST00000376806.945, and ENST00000706900.1, which were identified with abundances of 4656.61, 2330.06, and 564.65, respectively, correspond to different isoforms of the HLA-A gene encoding a major histocompatibility complex class I molecule. The HLA system plays an important role in the occurrence and outcome of infectious diseases, including those caused by the malaria parasite, the human immunodeficiency virus, and the severe acute respiratory syndrome coronavirus. Additionally, ENST00000216802.1046 identified with abundance 1942.10 corresponds to PSME2, encoding PA28 protein which can enhance the generation of antigenic peptides by inducing dual substrate cleavages by the 20S proteasome47. In contrast, transcripts uniquely identified by other basecallers, although also involved in immune regulation, exhibited lower expression levels, such as ENST00000375224.1 (Guppy)48, ENST00000372874.9 (RODAN)49, ENST00000316757.4 (GCRTcall)50, and ENST00000244204.11 (Dorado)51, with abundances of 128.72, 459.67, 68.20, and 93.99, respectively.

In addition to detecting annotated transcripts, identifying fusion genes is also critical, as they serve as key diagnostic markers and therapeutic targets in cancer cells, and long-read transcriptome sequencing enables the detection of fusion genes along with their full-length transcripts52. To further assess Coral’s performance in detecting fusion genes, we used JAFFAL52 to identify fusion genes from an ONT DRS replicate of the MCF-7 cancer cell line41 and compared the results to previously validated fusion events in the Mitelman database53. Coral demonstrated a median base calling accuracy improvement of 9.25%, 7.68%, 6.18%, and 4.91% over Guppy, RODAN, GCRTcall, and Dorado, respectively (Table S16). Coral identified 44 known fusion genes, exceeding Guppy’s 29, RODAN’s 20, GCRTcall’s 33, and Dorado’s 36 (Fig. S18). For validation against experimentally confirmed events, we utilized findings from ref. 41, which experimentally validated 12 specific fusion genes within the same MCF-7 sample using RT-PCR followed by Sanger sequencing. Coral successfully identified all 12 (100%) of the experimentally validated fusion genes, which outperformed other basecallers. Guppy and Dorado both detected 11 out of 12 (91.7%), while RODAN detected the fewest (Fig. 5c). RODAN’s lower detection rate may be due to its significantly shorter read lengths (Table S16). Furthermore, Coral generated the highest number of spanning reads aligning across these validated fusion gene breakpoints, indicating robust detection sensitivity. These findings highlight that Coral’s improved read accuracy enhances the detection of fusion genes in DRS.

Allele-specific expression analysis
ASE refers to the preferential transcription of RNA from either the paternal or maternal allele of a gene. RNA-seq can provide quantitative estimates of ASE for genes with transcribed polymorphisms. To investigate the effects of the basecallers on ASE, we used HapCUT2 to identify reads with allele-informative variants in the NA12878 dataset. Reads were required to contain at least two heterozygous SNPs, with no less than 75% of heterozygous SNPs consistent with parental alleles, leveraging the NA12878 small variant truth set for accurate parental origin assignment. A binomial test was used to test for statistically significant allele-specific analysis, and the P values were corrected for multiple hypothesis testing using the Benjamini–Hochberg procedure, with a false discovery rate (FDR) of 0.01. Coral identified 269 significant ASE genes, surpassing Guppy’s 165, RODAN’s 147, GCRTcall’s 152, and Dorado’s 116 (Fig. 5d). On the ASE genes identified by all basecallers, i.e., GSTP1, CRYZ, and HLA-C, the number of reads assigned to parental alleles at various heterozygous SNP sites clearly showed that Coral exhibited stronger parental allele specificity (Fig. 5e).

Haplotype phasing
The splicing of RNA transcripts as they mature from pre-mRNAs to mRNAs provides an opportunity to mitigate the problem of short-read spans by bringing together exons across large genomic distances, thus enabling the recognition of heterozygous alleles from the same chromosomal copy. RNA-seq enhances this capability by directly capturing splicing events and providing full-length transcript data, which is crucial for accurate haplotype phasing. To evaluate the performance of Coral in haplotype phasing, we analyzed whole-cell nanopore DRS data from the GM12878 cell line. The data were base called, followed by SNP calling with Clair3-RNA54, and haplotype phasing with HapCUT255. The phased haplotype blocks were compared against the gold-standard GIAB validation phases.

The SNP identified across all human chromosomes showcased Coral’s basecalled sequences achieved superior identification precision, recall, and F1 scores (Figs. 6a and S19). In the subsequent haplotyping analysis, the base calling of Coral also exhibited fewer phase switch errors and lower Hamming errors compared to the other basecallers. With Coral, the switch error and Hamming error rates were reduced by up to 78.8% and 76%, respectively, while simultaneously up to 36% more SNPs were also phased, which is critical for maintaining the integrity of haplotype blocks across the genome. Dorado achieved the lower F1 score in SNP identification and phased fewer SNPs than other basecallers, which may stem from Dorado’s low read accuracy (91.4%) and its significantly shorter read length in the GM12878/NA12878 dataset (Table S16). We further assessed the impact of phased SNPs on phasing genes by linking all heterozygous SNPs within these genes. Coral delivered more complete phasing results, which include notable examples that have clinical associations with diseases, e.g., DDR156, PTK2B57, FRA10AC158, CTSC59, and TNFRSF13B60 (Fig. 6c).

Methods
Encoder-decoder architecture
Coral employs a Transformer-based encoder-decoder architecture tailored for DRS basecalling. This architecture comprises two primary components: an encoder that processes raw electrical signals to extract contextual features, and a decoder that interpret these features to generate nucleotide sequences. Given an input raw signal represented as a one-dimensional vector x \in R^T with T indicating the number of sampled values, Coral outputs the corresponding ribonucleotide sequence u \in \Sigma^L, where L is the length of the ribonucleotide sequence and \Sigma = {A, C, G, U} is the alphabet of bases.

Encoder. The encoder of Coral comprises convolution modules followed by Transformer network (Figs. 1b and S20), designed to process the input signals and generate rich contextual representations. Initially, the raw ionic current signals are processed by five convolutional modules. Each convolutional module consists of a one-dimensional convolutional network, batch normalization operation63, and Swish activation function64. The sizes of the convolutional kernels in these modules are in the sequence of 5, 5, 9, 9, and 5, while the corresponding channel numbers expand in the order of 64, 64, 128, 128, and 512. The convolution modules perform a 12-fold down-sampling on the input signal, resulting in an output feature sequence of length t = T/12. The down-sampling of the raw signal not only effectively extracts its local features but also substantially shortens the sequence length, thereby reducing the computational complexity and overhead of the self-attention mechanism in the subsequent Transformer encoder. Following convolutional processing, the encoder integrates 18 Transformer layers. Each Transformer encoder layer comprises a Multi-Head Self-Attention (MHSA) module and a Multi-Layer Perceptron (MLP) module, both with residual connections and layer normalization. Given an input signal feature sequence h \in R^{t \times d}, where d is the feature dimension (d=512), the MHSA mechanism first projects the input features into queries q^{(e)}, keys k^{(e)}, and values v^{(e)} for each attention head e \in {1, ..., E}, where E is the number of heads. Specifically:

q^{(e)} = hQ^{(e)}, k^{(e)} = hK^{(e)}, v^{(e)} = hV^{(e)} (1)

where Q^{(e)}, K^{(e)}, V^{(e)} \in R^{d \times d_e} denote the learnable projection matrices that transform the signal feature sequence into queries, keys, and values, respectively. d_e is the head dimension (d_e = 64). Additionally, rotary positional embeddings65 are then applied to the queries q^{(e)} and keys k^{(e)} to incorporate relative positional information. Afterward, the self-attention weight \alpha^{(e)} and the output representations o^{(e)} are computed for each head:

\alpha^{(e)} = Softmax \left( \frac{q^{(e)} k^{(e) >}}{\sqrt{d_e}} \right) (2)

o^{(e)} = \alpha^{(e)} v^{(e)} (3)

The outputs from all attention heads are concatenated and projected to the original dimension:

MHSA(h) = Concat(o^{(1)}, ..., o^{(E)}) W^O (4)

where W^O \in R^{(E \times d_e) \times d} is a learnable output projection matrix. The MLP module contains a Swish Gated Linear Unit (SwiGLU)66 followed by a linear projection layer. The SwiGLU introduces nonlinearity, dimension expansion, and feature selection into the encoder layer. Following Transformer layers, the resultant features are subsequently up-sampled since decoder requires an output sequence from the encoder that is longer than the target ribonucleotide sequence to ensure a stable and accurate signal-to-sequence alignment, while a 12-fold down-sampling risks shortening the sequence excessively. Specifically, the up-sampling model is achieved through a linear projection. The feature sequence from the encoder h \in R^{t \times d}, is transformed using a learnable matrix U \in R^{d \times 2d} to produce an expanded sequence of doubled length while maintaining the same feature dimension d, resulting in an overall sixfold down-sampling relative to the raw signal (final feature length t = T/6). This sixfold resolution provides finer granularity for decoding and adequately covers the dwell time of most signal events (Fig. S22). To validate the down-then-up sampling strategy, we trained an ablated Coral-L3 model (i.e., Coral with three decoder layers) by removing the up-sampling step and directly applying sixfold down-sampling followed by Transformer layers in the encoder. The ablated Coral-L3 was compared to the standard Coral-L3 model on the HEK293T RNA004 dataset. As shown in Table S13, the standard Coral-L3 attained the same median accuracy while requiring substantially fewer computational resources in terms of GFLOPs and GPU memory usage. The results indicate that 12-fold down-sampling is adequate for Transformer-based contextual modeling and yields significantly higher efficiency.

Decoder. The decoder of Coral employs a Transformer network (Fig. 1c) to extract contextual dependencies between bases and learn the alignment between signal and base sequence. The nucleotide sequence is modeled as a sequential prediction task, where each base is predicted conditioned upon previously decoded bases. To optimize decoding efficiency, the sequence u = (u_1, u_2, ..., u_L) is partitioned into adjustable-length units, or bins, denoted as y = y_0, y_1, y_2, ..., y_l, where y_0 = \langle sos \rangle marks the start of the sequence; y_{l+1} = \langle eos \rangle denotes the end of the sequence. The binning strategy adapts to throughput requirements: single-nucleotide bins for high-accuracy decoding (default mode) and multi-nucleotide bins for high-throughput decoding (fast mode). The binning process is defined as follows: y_i = u_{(i-1)*k+1} u_{(i-1)*k+2} ... u_{i*k} (i \in 1, ..., l) represents a k-bin; The total number of bins l is calculated as: l = L/k + 1. In the fast mode, we use k=5 for RNA001/002 kit and k=6 for RNA004 kit.

Sequence representations y = y_0, y_1, y_2, ..., y_l are first embedded into a high-dimensional space via an embedding layer. Specifically, each symbol is mapped to a vector using a learnable lookup table, resulting in a sequence of embeddings s \in R^{l \times d}. These embeddings are then passed through N stacked decoder blocks. Each block includes two causal self-attention Transformer layers (Fig. S21a) and one fusion layer containing a cross-attention Transformer layer (Fig. S21b). In the causal self-attention layers, a causal mask m_s \in R^{l \times l} is introduced to prevent the sequence representations from attending to any future positions j > i. The causal mask is defined as:

m_s(i, j) = 0 if j \le i; -1 if j > i (5)

In the cross-attention layer, the signal context representation h \in R^{t \times d} serves as the source of keys and values, while the sequence representation s \in R^{l \times d} acts as the source of queries. The cross-attention weight \alpha_c^{(e)} \in R^{l \times t} for each head e \in {1, ..., E} is computed follows the same procedure as that used for self-attention weight \alpha^{(e)} in the encoder. The expected alignment position of y_i for head e \in {1, ..., E} is then given by:

\beta^{(e)} = \sum_{j=1}^{t} \alpha_c^{(e)}(i, j) * j (6)

where \alpha_c^{(e)}(i, j) denotes the probability that symbol y_i aligns to signal representations at position j. Although the alignment position \beta is an average across all positions, the model quickly learns to assign meaningful weights only to relevant parts of the signal. As shown in Fig. S23, as training progresses, the cross-attention matrix \alpha_c of the final cross-attention layer (closest to the output logits) becomes sharply focused and nearly diagonal. The probabilities for distant and unrelated signal positions become vanishingly small (close to zero). Consequently, the contribution of unrelated positions to the weighted average is negligible, and the calculated \beta value accurately reflects the true, localized alignment position.

Finally, a linear layer with Softmax activation produces a probability distribution over nucleotide symbols. In our implementation, we use 4 decoder blocks (12 layers) for RNA001/002 kit, and 5 decoder blocks (15 layers) for RNA004 kit. The Transformer decoder layers adopt an architecture similar to that of the Transformer encoder layers, with distinction in the normalization strategy: encoder layers utilize Post-LayerNorm (Post-LN, places layer norm outside of the residual blocks), whereas decoder layers employ a Pre-LayerNorm (Pre-LN, places layer norm inside of residual blocks), which has been shown to enhance training stability67.

Training and inference details
Datasets. The training dataset for the RNA001/002 sequencing kit was selected from Arabidopsis wild type (SRA accession SRX14052225)23, Homo sapiens (NA12878 RNA project BHAM_Run1)7, Epinano synthetic constructs data (SRA accession SRX5177013)30, C. elegans (ENA accession ERX3290234)31, E. coli (SRA accession SRX8525119)32, Mouse (ENA accession ERX12326526)33, Yeast (SRA accessions SRX8120016 and SRX17942557)35,36, Poplar (SRA accessions SRX9290560 and SRX9290561)34, and Zebrafish (ENA accession ERX9536682)37. The majority of biological samples used for training differ from those in the test sets, except Zebrafish, for which only one public DRS sample is currently available; in this case, training was performed using reads distinct from those in the test dataset. The sequencing reads were first basecalled using Guppy, then aligned to the corresponding transcriptome reference to filter low-quality reads, and finally established signal-to-sequence mappings using the resquiggle command from Tombo v1.5.168. Subsequently, 4 million high-accuracy chunks (accuracy \ge 96%) with 4096 signal samples were randomly selected from the dataset for training.

The training dataset for the RNA004 sequencing kit was created from a publicly available Universal Human Reference RNA sample (ENA accession ERX12369345)38. This RNA004 sample was basecalled using the Dorado v0.8.322 “rna004_130bps_sup@v5.1.0” model and aligned to the human transcriptome reference. We then performed the signal mapping refinement (re-squiggling) using the remora v3.3.069, and extracted 12 million high-quality signal chunks (12,000 samples per chunk, alignment accuracy \ge 98%) for training. More details and data links of the training dataset are provided in Table S1.

The test dataset for the RNA001/002 sequencing kit was comprised of six species, including H. sapiens (Human, NA12878 RNA project HOPKINS_RUN1)7, Arabidopsis thaliana (Arabidopsis, ENA accession ERX3766448)39, M. musculus (Mouse, ENA accession ERX3444723)40, S. cerevisiae S288C (Yeast, SRA accession SRX8120036)36, Populus trichocarpa (Poplar, SRA accession SRX9347134)34, and D. rerio (Zebrafish, ENA accession ERX9536682)37. The test dataset was generated through a series of pre-processing steps. Initially, the sequencing reads were basecalled with Guppy. Subsequently, these reads were aligned to their respective transcriptome references, and reads aligned to the mitochondrial genome were discarded. From the remaining aligned reads in each sample, 100,000 were randomly chosen for inclusion in the test dataset. For the performance evaluation of RNA004, we used a complete RNA004 sample from the HEK293T human cell line provided by the Singapore Nanopore Expression Project41 (Sample ID: SGNex_HEK293T_directRNA_sequencing_run1). The decoding configurations and data links of the test dataset are listed in Table S2.

Optimization and loss functions. The model parameters \theta are estimated by minimizing the loss function with labeled data. We use the cross-entropy loss function F_{CE}(x, y; \theta) as follows:

F_{CE}(x, y; \theta) = -log P(y|x; \theta) = -\sum_{i=1}^{l} log P(y_i|y_{<i}, x; \theta) (7)

In normal mode, Coral is initially trained to predict a single base per step using only the cross-entropy loss. Upon completion, the best-performing checkpoint is used to initialize all parameters except for the cross-attention layers, which are randomly initialized. The model is then retrained using the cross-entropy loss combined with additional monotonic alignment regularization loss F_{RE}70, which penalizes non-monotonic alignments and excessively small alignment gaps between adjacent symbols, following the formulation:

F_{RE} = \frac{1}{E} \sum_{e=1}^{E} \sum_{i=1}^{l-1} max(\beta_i^{(e)} - \beta_{i+1}^{(e)} + \epsilon, 0) (8)

Here, \epsilon is a hyperparameter representing the minimum allowed alignment gap between adjacent symbols. In our implementation, we set \epsilon = k, i.e., \epsilon = 1 for the normal mode training and \epsilon = 5 or 6 for the fast mode training. The regularization loss is applied exclusively to the final cross-attention layer (closest to the output logits) to preserve the model’s capacity for soft alignment in intermediate decoder layers while enforcing a near-diagonal, monotonic pattern in the final alignment. After training the Coral model with the monotonic alignment constraint, we again load its best checkpoint to initialize all parameters, except for the decoder embedding layer and output layer, which are randomly initialized due to the different vocabulary size in the Coral-FAST model compared to the Coral model. The Coral-FAST model, which predicts k bases per auto-regressive step, is then trained with the same monotonic alignment regularization loss. This pre-training strategy is crucial for ensuring training stability. The significantly larger output vocabulary of Coral-FAST makes optimization from a random initialization challenging and can lead to unstable gradients. In contrast, initializing Coral-FAST with weights from a pre-trained Coral model provided a much more stable and effective starting point for optimization, leading to a smoother gradient norm and faster convergence of the loss value (Fig. S24).

Signal file pre-processing. To enable the basecalling of the models, the input FAST5/POD5 files should undergo pre-processing as follows:
1. Convert the discrete 16-bit signals stored in FAST5 into current values through digital-to-analog conversion. This conversion requires some metadata (offset, range, and digitisation) recorded in FAST5. For POD5 files, the raw current values can be directly read from the file using the official API.
2. Remove the open-pore signal at the beginning of the sequencing that exceeds the normal signal level.
3. Normalize the signal using the Median Absolute Deviation:

signal = \frac{signal - median(signal)}{median(|signal - median(signal)|)} (9)

4. Segment the original signal into fixed-length chunks. In our implementation, the chunk length is 4096 for RNA001/002, and 6000 for RNA004. The chunks are decoding independently. Since the ONT nanopore DRS proceeds from the 3′ to 5′ direction, the decoding results should be concatenated to one complete nucleotide sequence, which is then reversed to align with the natural orientation.

Model inference. The standard beam search algorithm is applied to determine the most probable nucleotide sequence, with several optimization strategies to accelerate the decoding process, including key-value caching (KVCache), FlashAttention71, and a two-stage decoding scheme. KV caching avoids redundant computation by storing the keys and values of previously decoded tokens in the decoder’s transformer layers. FlashAttention is an I/O-aware algorithm that provides an accelerated attention implementation, significantly improving the speed and memory efficiency of Transformer models. Additionally, we adopt a two-stage decoding approach: an initial fast decoding pass with a reduced number of auto-regressive steps for most sequences, followed by a second pass using a longer decoding window for remaining unfinished sequences. The beam search decoding process proceeds as follows:
1. The input signal is encoded using the CNN-Transformer encoder to produce the signal feature sequence.
2. Decoding begins with a start-of-sequence input symbol \langle sos \rangle. The cross-attention mask is initialized as m_c(0, j) = 0 (j \in [1, t]). During the subsequent decoding process, this mask is added to the attention logits (before the softmax operation) of the final cross-attention layer to guide alignment-based decoding.
3. At decoding step i, the decoder predicts the probability distribution of the next output symbol based on previously decoded symbols and the signal features. Simultaneously, it estimates the alignment position of the output symbol from the final cross-attention layer, denoted as \beta^*_i, following the formulation:

\beta^*_i = \frac{1}{E} \sum_{e=1}^{E} \beta_i^{(e)} (10)

4. From all expanded sequences, the top-B hypotheses (beam size B) with the highest cumulative log-probabilities are selected. Decoding terminates when any of the following conditions are met by the best hypothesis:
• An end-of-sequence symbol \langle eos \rangle is generated.
• The decoding length reaches the predefined maximum sequence length.
• The alignment position satisfies \beta^*_i \ge t, where t is the length of signal features.
5. If none of the stopping criteria are met, the cross-attention mask m_c(i, j) is updated based on the current alignment position \beta^*_i, following the formulation:

m_c(i, j) = 0 if j \ge \lfloor \beta^*_i \rfloor; -1 if j < \lfloor \beta^*_i \rfloor (11)

This masking approach prevents the sequence representation from attending to any signal representations at positions j < \lfloor \beta^*_i \rfloor for the next symbol decoding, which enforces monotonic decoding and effectively filters out low-contribution noise from earlier or irrelevant positions, without sacrificing contextual representation.
6. Repeat from Step 3 to predict the next output symbol.

Benchmark performance comparison
In the comparison experiments, RODAN and GCRTcall were run using their default configuration. Guppy was executed using the RNA model rna_r9.4.1_70bps_hac.cfg with the option “–disable_qscore_filtering” to deactivate the default quality control. Dorado was executed using the RNA002 model “rna002_70bps_hac@v3” or the RNA004 model “rna004_130bps_sup@v5.1.0” according to the sequencing kit. The download links and parameter configurations of Guppy, RODAN, GCRTcall, Dorado, and Coral are summarized in Table S3. After basecalling, the reads were aligned to the respective transcriptome reference using minimap2 (v2.17-r941)72 with the options “-ax map-ont –secondary=no –eqx” to disable secondary alignments. For the RNA004 sequencing kit, we use minimap2 (v2.28-r1209) with the options “-ax lr:hq –for-only –secondary=no –eqx" for more accurate transcriptome alignment. The unaligned reads and the supplementary alignments were further excluded from the evaluation. The read accuracy is used as the primary metric for evaluating the basecalling performance:

accuracy = \frac{N_{match}}{N_{match} + N_{mis} + N_{ins} + N_{del}} (12)

where N_{match}, N_{mis}, N_{ins}, and N_{del}, extracted from the CIGAR string, represent the numbers of matching bases, mismatches, insertions, and deletions of a read, respectively. The error (mismatch, insertion, and deletion) rate per read can be defined as:

err_{mis/ins/del} = \frac{N_{mis/ins/del}}{N_{match} + N_{mis} + N_{ins} + N_{del}} (13)

Downstream analyzes pipelines
Several publicly available datasets were involved in human transcriptome analysis, including one A549 replicate, one HCT116 replicate, and one MCF-7 replicate from the Singapore Nanopore Expression Project41, 20 runs from the NA12878 RNA project7, and three HEK293T replicates spiked with 25 ng of SIRV-Set1-E2 RNA from ESPRESSO42. The data links of the human dataset are provided in Table S15. To assess the basecalling performance, we aligned the basecalled reads to the human reference transcriptome from GENCODE (v45) using minimap2 (v2.17-r941) with the options “-ax map-ont –secondary=no –eqx”.

The transcriptome analyzes were carried out with ESPRESSO (v1.5.0)42. Before running the ESPRESSO, the basecalled reads were aligned to the human reference genome GRCh38 using minimap2 (v2.17-r941)72 with the options “-ax splice -uf -k14 –secondary=no". We apply “–splice-flank=no" option to let minimap2 only model GT..AG splicing signal for SIRV splice alignment. After the spliced alignment, samtools (v1.10)73 was used to create sorted and filtered BAM files as input for ESPRESSO. ESPRESSO works in three steps, i.e., high-confidence SJ identification ("S”), SJ correction and recovery ("C”), and transcript discovery and quantification ("Q”). The human transcript annotations from GENCODE (v45) were considered during the final step of ESPRESSO. The output files from ESPRESSO, including an updated GTF annotation, abundance of detected isoforms, and compatible isoforms of each read, were used for further statistical analysis. We used JAFFAL (v2.3)52 with default options to detect fusion genes from basecalled reads of cancer cell samples.

The NA12878 DRS dataset was used for allele-specific analysis. We obtained genotype information for NA12878 from existing phased Illumina platinum genome data (https://platinum-genomes.s3-eu-central-1.amazonaws.com/2017-1.0/hg38/small_variants/NA12878/NA12878.vcf.gz). We used bcftools (v1.8)74 to filter variants that are heterozygous in NA12878. We used the extractHAIRS utility of the HapCUT2 (v1.3.4)55 to identify aligned reads with allele-informative variants. We followed the script (https://github.com/nanopore-wgs-consortium/NA12878/blob/master/nanopore-human-transcriptome/scripts/ase.py) provided by ref. 7 for allelic assignment. Finally, we performed a binomial test and adjusted the P values using the Benjamini–Hochberg procedure with a FDR of 0.01 to identify genes with significant ASE. To simulate ASE, we selected hundreds of transcripts containing at least two heterozygous variants from chromosome 1. Parental-specific expression was randomly assigned to each transcript. Using the “trans" mode of PBSIM3 (v3.0.4)62, we generated sequencing reads with varying accuracy, following predefined expression profiles. We then applied the same ASE detection method, with a FDR of 0.01, to analyze the simulated reads.

The NA12878 DRS dataset was used for haplotype phasing. We employed Clair3-RNA (v0.0.1)54 to call SNPs and HAPCUT2 (v1.3.4)55 for haplotype phasing. The phased VCF was compared to the GIAB HG001 benchmark (v3.3.2) using the compare command from whatshap (v2.0)75.

Thousands of transcripts from chromosome 1 were used to establish a transcriptome reference for our transcript discovery simulation experiments. To perform simulation for SNP discovery and haplotype phasing, we also generated a diploid transcriptome reference that includes variation data from the GIAB HG001 benchmark (v3.3.2) using g2gtools (v2.0.0) (https://github.com/churchill-lab/g2gtools). The simulated nanopore sequencing reads varying the accuracies from 89% to 99% were generated using Badread (v0.4.1)61.

Implementation and environment configuration
All experiments were conducted on a Linux server with 2 Intel® Xeon® CPUs (2.20 GHz), four NVIDIA RTX3090 GPUs, and 128 GB of system RAM. The operating system was Ubuntu 22.04.3 LTS. The implementation was developed using Python 3.10 and PyTorch 2.6.0 with CUDA 11.8 for GPU acceleration. Coral was trained using gradient descent with the AdamW optimizer76, an initial learning rate of 0.0002, and a batch size of 128. The model was trained for 10 epochs and converged (achieving the peak validation accuracy) at the 6th epoch. More details of the training configuration are listed in Table S20.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors present Coral, a basecaller for nanopore DRS reads. Coral uses an encoder-decoder architecture with auto-regressive to learn the contextual dependencies at both signal and sequence levels, outperforming the encoder-CTC architecture and the compared methods (Guppy and RODAN) in basecalling accuracy. The authors demonstrate that the improved basecalling accuracy benefits several downstream analyses, including transcript quantification, ASE analysis and haplotype phasing. I agree with the authors that this work provide a benchmark for accuracy in nanopore DRS applications. However, I also have several serious concerns.\n1. I believe one of the main contributions of this work is the auto-regressive decoder which considers the context dependencies at both signal and sequence levels. The authors have shown the next base prediction accuracy given a correct 5-mer context (Fig. 2g). I wonder if a 5-mer was wrongly predicted, how would it influence the prediction of the next 5-mers or bases?\n2. The datasets used in this study are either RNA001 or RNA002 DRS reads. However, both RNA001 and RNA002 are now considered outdated, replaced by the RNA004 kit. There are already publicly available RNA004 data now. It is necessary to test Coral using RNA004 data.\n- Following this, I suggest that the authors develop the Coral tool to support POD5 format.\n3. The authors compared Coral with Guppy and RODAN. However, ONT has already released another basecaller Dorado for practical use. Also, a third-party basecaller GCRTcall was released and published recently (https://doi.org/10.3389/fgene.2024.1443532).\n- The authors should compare Coral with Dorado and GCRTcall using the datasets used in this study.\n- Dorado, Guppy, and GCRTcall all support RNA004 reads. A comparison on RNA004 data should also be performed between Coral and all other methods.\n4. As we all know, basecalling of nanopore reads is a very computationally intensive task, so computational efficiency is an important consideration in the development of basecalling tools. However, I didn’t find any description about the computational efficiency of Coral.\n- Does Coral run faster and need less memory than other methods? The author should compare the time and memory usage of different tools.\n- The authors demonstrated that the auto-regressive decoder in Coral outperforms the CTC-decoder in decoding accuracy. As CTC enables parallel decoding, the authors may also compare the two approaches in terms of computational efficiency for further investigation.\nMinor\n1. Regarding the tool, I suggest the authors output some statistics in the log/stderr of Coral, such as the number of reads that processed, successfully basecalled, and failed.\n2. In Figure 4, the font in panel (e) is inconsistent with other panels.", "reviewer_2": "The manuscript from Xie S. et al described a new base-caller Coral for base-calling direct RNA sequencing data. They show that Coral is more accurate than two other base callers, Guppy and RODAN, and showed that this increase in accuracy enables the authors to identify downstream events such as splicing isoforms and allele-specific transcripts better. While direct RNA sequencing has been useful in the RNA field, its usage has been hampered by its relatively poor accuracy. As such, this work is timely and important. However, additional controls need to be done to truly demonstrate Coral’s improvements over existing tools. Below are my main comments:\n1) The authors benched marked Coral against Guppy and RODAN, unfortunately due to the rapid speed of advancements in the nanopore field, Nanopore now has a more recent base caller Dorado. It would be important for the authors to include Dorado in their benchmarking to show that Coral is still the best.\n2) It is unclear whether the sequencing in this manuscript is done using the direct RNA sequencing 002 kit or the 004 kit. Nanopore has shown improvements in base calling accuracy for direct RNA sequencing based on the 004 kit vs the 002 kit. The authors would need to show compatibility with 004 kit and whether their base-calling for the reads from the 004 kit are also more accurate.\n3) The authors used annotated isoforms from NA12878 as benchmark- while this is useful, it is difficult to know the absolute truth in this dataset. It might be more useful to benchmark on a synthetic RNA pool (such as using SIRV synthetic RNA pools) whereby we know the truth- to determine the ability to detect transcript isoforms and low abundance transcripts. Using this synthetic RNA pool, the authors could also determine the abundance level cutoffs that enable effective detection by the three methods.\n4) For the newly identified isoforms (for eg RPL14-201)- can the authors do some experimental validation, such as RT-PCR, followed by sanger sequencing to determine whether this new isoform indeed exists? I have the same comment for fusion genes.", "reviewer_3": "The paper introduces \"Coral,\" a novel dual context-aware basecaller designed to enhance the accuracy of nanopore direct RNA sequencing (DRS). The authors propose an encoder-decoder network architecture that captures dependencies at both the signal and sequence and signal and sequence levels, improving basecalling accuracy over existing tools such as Guppy and RODAN. The study demonstrates that Coral delivers up to a 4.48% improvement in median accuracy on human RNA samples, enhances transcript isoform detection by 23.2%, and improves haplotype phasing accuracy by reducing switch errors by 66.5% and Hamming errors by 60%.\nMajor comments:\n-The authors compare Coral with Guppy which is deprecated by Oxford Nanopore. To show usefulness of their model they should compare Coral with Dorado. Also, there are new nanopores and sequence kit SQK-RNA004, which are not analysed in this paper\n-The encoder-decoder architecture with latent alignment variables likely introduces computational overhead compared to traditional CTC-based basecallers. While the paper mentions dynamic programming and beam search optimizations, a more detailed discussion of runtime performance and memory consumption would be beneficial.\n-Ablation study should be standard for each deep learning model. The authors should perform an ablation study to better understand the relevance of each component to the model\nMinor comment:\n-Figure 1a it should read “Raw signal” instead “Raw singal”\n(Remarks on code availability)\nThe authors use old python version 3.7.11 which is not supported on newer Mac processors and it would take significant amount of time for a workaround. I suggest authors to update the python version or provide a container image"}, "author_response": "Dear Reviewers,\nThank you for your thoughtful and constructive feedback on our manuscript. We\nsincerely appreciate the opportunity to address your comments and further enhance our\nwork. Following the suggestions, we have carried out additional experiments to\ndemonstrate that Coral is competitive or superior to Guppy, RODAN, Dorado and\nGCRTcall on both RNA001/002 and RNA004 reads (Response Figs. 2-3) in terms of\nbasecalling accuracy. We also expanded validation to SIRV spike-in controls, where\nCoral delivers the highest concordance among all basecallers tested (Response Figure\n7). To address the concerns regarding computational efficiency raised by two\nreviewers, we have improved the decoding efficiency of Coral by replacing the\noriginal LSTM-based latent-alignment decoder with a Transformer decoder that\nintegrates cross-attention alignment and beam-search inference. This change\nenables highly parallel decoding while improving alignment precision. Accordingly, the\nCNN–RNN encoder is updated to a CNN–Transformer architecture, so that both\nencoding and decoding now benefit from multi-head self-attention, improving accuracy.\nThese changes together lead to a reduction of decoding time by nearly four-fold and an\nimprovement of basecalling accuracy (Response Fig. 4).\nWith the new version of Coral, the median read identity on the NA12878 RNA sample\nincreased from 95.56% to 97.69% (Response Fig. 3), which also facilitated the\ntranscript isoforms discovery, haplotype phasing, and allele-specific expression. These\nrefinements are attributed to the better long-range signal and sequence dependencies\nmodeling capability of Transformers than the recurrent networks.\nAll requested analyses have been re-performed with the new version of Coral and the\nresults have been updated in this revision. In summary, we have introduced the\nfollowing major changes:\n1) To reinforce the cross-species generalization, we have expanded the training\ncorpus from four to eight species with RNA001/002 kits, then tested across six\nindependent datasets. Coral achieved an average median accuracy of\napproximately 97%, outperforming other compared basecallers (Fig. 2a–f).\n2) Coral was trained on a human reference RNA004 sample and evaluated on an\nindependent HEK293T RNA004 dataset. Coral achieved a median accuracy of\n99.52% (Fig. 2g), which surpassed other basecallers, including Dorado (98.28%).\n3) As suggested, we have involved Dorado (v0.8.3) and GCRTcall (v1) in comparison\nstudies. Results were consolidated in revised Fig. 2 (panels a–g) and Response Figs.\n2-3. Detailed ablation studies (Tables S4–S7) affirmed the contributions of the\ntransformer encoder-decoder architecture.\n4) We benchmarked transcript-level quantification using SIRV spike-ins and Illumina\ndeep RNA-seq data, and performed additional analysis on MCF-7 cell fusion genes\n(validated by RT-PCR and Sanger sequencing) detection, where Coral showed the\nhighest concordance.\n5) We now offer two modes, i.e., Coral and Coral-FAST. The normal mode (Coral),\npredicting one base at a time, is the used by default to optimize the basecalling\naccuracy. The FAST mode (Coral-FAST) prioritizes speed by predicting k\nconsecutive bases at a time, optimizing for throughput.\nWe hope that these improvements, together with the comprehensive revisions that\nfollow, fully address all your concerns and substantially strengthen the manuscript. The\ndetailed point-by-point responses are provided as follows.\nReviewer #1\nComment 1: I believe one of the main contributions of this work is the auto-regressive\ndecoder which considers the context dependencies at both signal and sequence levels.\nThe authors have shown the next base prediction accuracy given a correct 5-mer\ncontext (Fig. 2g). I wonder if a 5-mer was wrongly predicted, how would it influence\nprediction of the next 5-mers or bases?\nResponse: We appreciate this insightful question regarding the robustness of our auto-\nregressive decoder in the presence of mis-predicted k-mers. To address this concern,\nwe have conducted experiments to evaluate the propagation of errors when k-mer\ncontexts are incorrect. Our findings indicate that an incorrectly predicted 5-mer context\nincreases the error rate of the immediately subsequent base prediction compared to\npredictions from correct 5-mer contexts (Response Fig. 1a). However, as the encoder-\ndecoder architecture of Coral is designed to mitigate error propagation through its\ncomprehensive dual contextual learning, the detrimental impact caused by an initial\nincorrect 5-mer context progressively diminishes after approximately 4 subsequent\nbases (Response Fig. 1b). The corresponding results and discussion have been included\nin the revised manuscript (Section “Effectiveness of modeling contextual\ndependencies” and Fig. S7).\nResponse Figure 1. The impact of error-predicted 5-mer context on the prediction of the\nsubsequent bases. a, Comparison of the error rate for the next predicted base given a correct or\nerroneous predicted 5-mer context. b, Error rate of -th subsequent base given an incorrectly\npredicted 5-mer context.\n𝑑𝑑\nComment 2: The datasets used in this study are either RNA001 or RNA002 DRS reads.\nHowever, both RNA001 and RNA002 are now considered outdated, replaced by the\nRNA004 kit. There are already publicly available RNA004 data now. It is necessary to\ntest Coral using RNA004 data. – Following this, I suggest that the authors develop the\nCoral tool to support POD5 format.\nResponse: We are grateful for this constructive recommendation. We have evaluated\nthe performance of Coral on RNA004 data, and have extended Coral to natively support\nthe POD5 file format. The previous version of Coral with LSTM-based decoder,\ndenoted as Coral-LSTM, is also included in the comparison to showcase the\nimprovement of the new version. Full details are provided below.\nThe RNA004 kit represents a substantial advance over RNA001/002 chemistries,\nfeaturing a new E8.2 motor protein and R10.4.1 nanopores operating at an elevated\nsampling rate of 4 kHz (vs. 3 kHz for RNA001/002). This upgrade yields faster strand\ntranslocation, enhanced signal resolution, and increased throughput. These changes\nfundamentally alter the raw signal landscape, rendering models optimized for older\nchemistries suboptimal. Accordingly, we trained a dedicated Coral model for RNA004\ndata to maintain optimal basecalling accuracy.\nTo develop the RNA004-specific model, we leveraged publicly available Universal\nHuman Reference RNA data (accession ERX12369345) generated using R10.4.1 flow\ncells. Reads were first basecalled using the Dorado 'rna004_130bps_sup@v5.1.0'\nmodel, aligned to the human transcriptome reference, and filtered to high-quality signal\nchunks (length 12,000 points, alignment accuracy ≥ 98%) for training. Model\nperformance was subsequently evaluated on an independent HEK293T RNA004 run\nfrom the Singapore Nanopore Expression Project (Sample ID:\nSGNex_Hek293T_directRNA_replicate5_run1, accessed on [2024-11-21] at\nregistry.opendata.aws/sg-nex-data). We compared the basecalling accuracy of Coral\nand other state-of-the-art basecallers including Dorado, Guppy, and GCRTcall. Note\nthat RODAN was excluded as it does not support RNA004 chemistry. On this\nbenchmark dataset, Coral achieved a median read accuracy of 99.52 %, surpassing\nDorado (98.28 %) and also outperforming both Guppy and GCRTcall (Response Fig.\n2). Coral also exhibited the lowest median mismatch (0.10 %), insertion (0.00 %) and\ndeletion (0.14%) rates, while maintaining read length and the proportion of unaligned\nreads comparable to Dorado. The metrics demonstrated Coral’s robust adaptation to\nRNA004-specific signal characteristics. The RNA004-specific results are now included\nin the revised manuscript (Results section, “Basecalling accuracy”, Fig. 2g).\nIn parallel, we extended Coral’s I/O layer to stream data directly from POD5\narchives, complementing the existing FAST5 support. The updated software release and\ndocumentation reflect this change.\nResponse Figure 2. Median accuracy/mismatch/insertion/deletion/read length and unaligned\nreads proportion of Guppy/GCRTcall/Dorado/Coral-LSTM/Coral in the HEK293T RNA004\nsample.\nComment 3: The authors compared Coral with Guppy and RODAN. However, ONT\nhas already released another basecaller Dorado for practical use. Also, a third-party\nbasecaller GCRTcall was released and published recently (https://doi.org/10.3389/\nfgene.2024.1443532).\n- The authors should compare Coral with Dorado and GCRTcall using the datasets\nused in this study.\n- Dorado, Guppy, and GCRTcall all support RNA004 reads. A comparison on RNA004\ndata should also be performed between Coral and all other methods.\nResponse: Thank you for the suggestions. We have incorporated the latest version of\nDorado (v0.8.3) and GCRTcall (v1) in all comparison studies in this revision. For the\nRNA001/002 chemistry, all basecallers were evaluated on six independent test datasets.\nNote that the previous version of Coral with LSTM-based decoder, denoted as Coral-\nLSTM, was also included in the comparison to showcase the improvement of the new\nversion. Response Fig. 3 shows that Coral-LSTM and Coral outperformed all other\nbasecallers across the evaluation metrics. Notably, in the human NA12878 sample\ndataset, Coral reaches 97.69% median identity; the same trend holds for yeast and\nArabidopsis, where gains over competing basecallers exceed 2 percentage points.\nAcross all test datasets, Coral achieved an average median read accuracy of\napproximately 97%, representing a notable advancement in basecalling accuracy for\nRNA001/002 chemistry. Error-rate decompositions confirm that Coral records the\nlowest median mismatch (0.96%), insertion (0.69%) and deletion (1.20%) rates, while\nsimultaneously producing the longest aligned reads and the fewest unaligned reads\ndemonstrating that improved basecalling accuracy can facilitate better alignment to the\nreference sequence. These results have been incorporated in the revised manuscript\n(Section “Basecalling accuracy”, Fig. 2a-f).\nFor the RNA004 chemistry, a dedicated Coral-LSTM and Coral model was trained on\nUniversal Human Reference RNA sequenced with R10.4.1 pores and validated on an\nindependent SG-NEx HEK293T run. As detailed in our response to Comment 2 (Fig.\n2g; Response Fig. 2), Coral achieves a median accuracy of 99.52%, exceeding Dorado\n(98.28%), Guppy (87.38%) and GCRTcall (90.34%).\nResponse Figure 3. Basecalling performance of the basecallers on RNA001/002 test datasets.\na-d, Heatmap of median accuracy/mismatch/insertion/deletion of six basecallers in the test datasets.\nGrids with the darker colors are better. The 'Avg' represents the average value across the datasets.\ne-f, Bar plot of median read length and the number of unaligned reads in the test datasets. Unaligned\nreads for Guppy are not available as the RNA001/002 test datasets include only reads that were\nsuccessfully basecalled by Guppy and aligned to the reference sequence.\nComment 4: As we all know, basecalling of nanopore reads is a very computationally\nintensive task, so computational efficiency is an important consideration in the\ndevelopment of basecalling tools. However, I didn’t find any description about the\ncomputational efficiency of Coral.\n- Does Coral run faster and need less memory than other methods? The author\nshould compare the time and memory usage of different tools.\n- The authors demonstrated that the auto-regressive decoder in Coral outperforms\nthe CTC-decoder in decoding accuracy. As CTC enables parallel decoding, the\nauthors may also compare the two approaches in terms of computational efficiency\nfor further investigation.\nResponse: We fully agree that computational efficiency is a critical aspect for nanopore\nbasecalling tools. To improve the computational efficiency of Coral, we introduced\narchitectural changes to Coral by replacing the original LSTM-based decoder with a\nTransformer decoder of efficient cross-attention for alignment and standard beam\nsearch for decoding, to reduce the decoding time. We also developed the Coral-FAST\nmode, which predicts k consecutive bases per auto-regressive step. We provided both a\ntheoretical complexity analysis (Response Table 1) and empirical benchmarks\n(Response Figs. 4 and 5) comparing Coral with the existing basecallers in terms of\ndecoding time, memory usage, and basecalling accuracy. These results have also been\ndiscussed in the “Discussion” section of the revised manuscript.\nComparison with existing CTC-based basecallers\nAll CTC-based models—Guppy, RODAN, GCRTcall, Dorado, and Coral-CTC (i.e.,\nCoral with CTC decoder)—share the same theoretical decoding complexity of\n, where is the length of signal features, is the dimension size, and is\n𝑂𝑂(𝑇𝑇∗\nthe alphabet size. This is reflected in our benchmarks (Response Figs. 4 and 5), where\nd𝑑∗𝑉𝑉) 𝑇𝑇 𝑑𝑑 𝑉𝑉\nthe decoding time of the basecallers fall within a comparable range. However, Coral-\nCTC consistently outperforms the others in terms of basecalling accuracy—1–5%\nhigher (e.g., Coral-CTC: 94.49% vs Guppy: 89.52%, RODAN: 90.64%, Dorado: 93.46%\non A549 RNA002 data)—at a similar time and memory cost. We attribute this to the\nencoder in Coral, which provides enhanced feature extraction compared to other models.\nAuto-regressive decoding vs. CTC\nCompared to Coral-CTC, the auto-regressive decoding variants—Coral-LSTM (i.e.,\nCoral with LSTM-based decoder), Coral, and Coral-FAST—all leverage a dual-context\ndecoder that integrates long-range signal- and sequence-level dependencies, resulting\nin substantial accuracy improvements. Notably, Coral achieved a basecalling accuracy\nof 97.29% on RNA002 and 99.44% on RNA004 datasets. However, as shown in\nResponse Table 1, the computational complexity of these models scales with the\nsequence length , leading to a marked increase in decoding time and memory\nconsumption (e.g., ~4000 seconds for Coral, as shown in Response Fig. 5). This\n𝐿𝐿\noverhead primarily arises from the long length of ONT reads and the inherent sequential\nlimitations of auto-regressive models. The complexity of Coral-FAST is theoretically\nreduced to . As a result, Coral-FAST achieves a nearly 4× speed-up\n𝐿𝐿\nover Coral-𝑂𝑂LS(𝑘𝑘T∗M𝑁𝑁 w∗h𝑑𝑑ile∗ 𝑑𝑑fu )rther improving accuracy (from 95.56% to 96.75% on\nRNA002). Compared to Coral, Coral-FAST also demonstrates a 4× speed-up with a\nnegligible reduction in median accuracy (~0.03%) on RNA004, demonstrating a strong\ntrade-off between accuracy and efficiency.\nIn conclusion, while Coral’s auto-regressive variants incur higher computational\ncost, they deliver substantial accuracy improvements. Moreover, the progressive\narchitectural refinements—particularly Coral-FAST—strike a better balance between\ndecoding performance and resource efficiency.\nResponse Table 1. Theoretical decoding complexity of various basecallers.\nBasecaller Decoder Complexity\nGuppy CTC\n𝑂𝑂(𝑇𝑇∗𝑑𝑑∗𝑉𝑉)\nRODAN CTC\n𝑂𝑂(𝑇𝑇∗𝑑𝑑∗𝑉𝑉)\nGCRTcall CTC\n𝑂𝑂(𝑇𝑇∗𝑑𝑑∗𝑉𝑉)\nDorado CTC\n𝑂𝑂(𝑇𝑇∗𝑑𝑑∗𝑉𝑉)\nCoral-CTC CTC\nLSTM 𝑂𝑂(𝑇𝑇∗𝑑𝑑∗𝑉𝑉)\nCoral-LSTM\n(dynamic programming and beam search)\nTransformer 𝑂𝑂(𝐿𝐿 ∗𝑇𝑇∗𝐵𝐵∗𝑑𝑑∗𝑉𝑉)\nCoral (beam search, predict single base per auto-\nregressive step)\n𝑂𝑂(𝐿𝐿∗𝑁𝑁∗𝑑𝑑∗𝑑𝑑 )\nTransformer\nCoral-FAST (beam search, predict k bases per auto-\nregressive step) 𝐿𝐿\n𝑂𝑂( ∗𝑁𝑁∗𝑑𝑑∗𝑑𝑑 )\n( is length of signal features; is dimension size; is alphabet size; is length of outp𝑘𝑘ut sequence; is beam\nsize; represent the number of transformer decoder layers; k=5 for RNA001/002 kits and k=6 for RNA004 kit).\n𝑇𝑇 𝑑𝑑 𝑉𝑉 𝐿𝐿 𝐵𝐵\n𝑁𝑁\nResponse Figure 4. Median basecalling accuracy (blue bars, left axis) and decoding time (red\npoints, right axis) for each model on sampled reads from A549 RNA002 data. Memory usage\nis shown below (green bars).\nResponse Figure 5. Basecalling accuracy and runtime on sampled reads from long-read\nRNA004 data across different methods. Memory usage is shown below (green bars).\nComment 5: Regrading the tool, I suggest the authors output some statistics in the\nlog/stderr of Coral, such as the number of reads that processed, successfully basecalled,\nand failed.\nResponse: Thank you for pointing this out. The new version of Coral now supports\ndisplaying progress bars and logging information.\nComment 6: In Figure 4, the font in panel(e ) is inconsistent with other panels.\nResponse: Thank you for pointing this out. We have revised Fig. 4 in the manuscript\nto fix this issue.\nReviewer #2\nComment 1: The authors benched marked Coral against Guppy and RODAN,\nunfortunately due to the rapid speed of advancements in the nanopore field, Nanopore\nnow has a more recent base caller Dorado. It would be important for the authors to\ninclude Dorado in their benchmarking to show that Coral is still the best.\nResponse: Many thanks for the valuable suggestion. We have included Dorado (v0.8.3)\nin all benchmarking analyses presented in the revised manuscript.\nFor the RNA001/002 chemistry, we expanded our training corpus from four to eight\nspecies to enhance model generalizability. Subsequently, we re-evaluated all basecallers,\nincluding Dorado, on six independent test datasets. As depicted in Response Fig. 3,\nCoral outperformed other basecallers across various evaluation metrics. Notably, in the\nhuman NA12878 sample dataset, Coral achieved a median read identity of 97.69%,\nsurpassing Dorado's performance. Similar trends were observed in yeast and\nArabidopsis datasets, where Coral's accuracy exceeded that of Dorado by more than 2\npercentage points. Across all test datasets, Coral attained an average median read\naccuracy of approximately 97%, representing a significant advancement in basecalling\naccuracy for RNA001/002 chemistry (Response Fig. 3).\nFurthermore, error-rate decompositions revealed that Coral recorded the lowest median\nmismatch (0.96%), insertion (0.69%), and deletion (1.20%) rates among all evaluated\nbasecallers. It also produced the longest aligned reads and the fewest unaligned reads,\nindicating that improved basecalling accuracy facilitates better alignment to reference\nsequences.\nThese comprehensive results have been incorporated into the revised manuscript,\nspecifically in the section titled \"Basecalling accuracy\" and illustrated in Fig. 2a–f.\nComment 2: It is unclear whether the sequencing in this manuscript is done using the\ndirect RNA sequencing 002 kit or the 004 kit. Nanopore has shown improvements in\nbase calling accuracy for direct RNA sequencing based on the 004 kit vs the 002 kit.\nThe authors would need to show compatibility with 004 kit and whether their base-\ncalling for the reads from the 004 kit are also more accurate.\nResponse: We appreciate your insightful comment regarding the compatibility and\nperformance of Coral with the latest Oxford Nanopore Technologies (ONT) Direct\nRNA Sequencing Kit (RNA004). As suggested, we have conducted a comprehensive\nevaluation of Coral's performance on RNA004 data and have incorporated these\nfindings into the revised manuscript (Results section, “Basecalling accuracy”, Fig. 2g).\nThe RNA004 kit represents a significant advancement over its predecessors\n(RNA001/002), featuring a new E8.2 motor protein and R10.4.1 nanopores operating\nat an elevated sampling rate of 4 kHz (compared to 3 kHz for RNA001/002).\nThis upgrade yields faster strand translocation, enhanced signal resolution, and increased\nthroughput, fundamentally altering the raw signal landscape and necessitating tailored\nbasecalling models for optimal performance. To adapt Coral for RNA004 chemistry,\nwe trained a dedicated model using publicly available Universal Human Reference\nRNA data (accession ERX12369345) generated with R10.4.1 flow cells. Reads were\ninitially basecalled using Dorado's 'rna004_130bps_sup@v5.1.0' model, aligned to the\nhuman transcriptome reference, and high-quality signal chunks (length 12,000 points,\nalignment accuracy ≥98%) were extracted for training.\nWe evaluated the performance of Coral on an independent HEK293T RNA004 dataset\nfrom the Singapore Nanopore Expression Project (Sample ID:\nSGNex_Hek293T_directRNA_replicate5_run1, accessed on 2024-11-21 at\nregistry.opendata.aws/sg-nex-data). Comparative analysis included state-of-the-art\nbasecallers such as Dorado, Guppy, and GCRTcall. Note that RODAN was excluded\ndue to its lack of support for RNA004 chemistry. On this benchmark dataset, Coral\nachieved a median read accuracy of 99.52 %, surpassing Dorado (98.28 %) and\nmarkedly outperforming both Guppy and GCRTcall (Response Fig. 2). Coral also\nexhibited the lowest median mismatch (0.10 %), insertion (0.00 %) and deletion\n(0.14 %) rates, while maintaining read length and the proportion of unaligned reads\ncomparable to Dorado. These metrics demonstrated Coral’s robust adaptation to\nRNA004-specific signal characteristics and support its superior basecalling fidelity.\nComment 3: The authors used annotated isoforms from NA12878 as benchmark –\nwhile this is useful, it is difficult to know the absolute truth in this dataset. It might be\nmore useful to benchmark on a synthetic RNA pool (such as using SIRV synthetic RNA\npools) whereby we know the truth- to determine the ability to detect transcript isoforms\nand low abundance transcripts. Using this synthetic RNA pool, the authors could also\ndetermine the abundance level cutoffs that enable effective detection by the three\nmethods.\nResponse: We sincerely appreciate your valuable suggestions and have carried out a\ndedicated SIRV benchmark to provide an absolute, ground-truth assessment of Coral.\nWe analyzed the publicly available HEK293T dataset spiked with 25 ng of SIRV-Set 1-\nE2 RNA (Lexogen, lot no. 001418), which comprises 68 artificial transcripts with\nknown sequences and precisely defined concentrations spanning two orders of\nmagnitude. This dataset offers an ideal benchmark for evaluating transcript\nidentification and quantification accuracy. The analysis involved basecalling raw signal\ndata with Coral and other benchmarked basecallers (Dorado, Guppy, RODAN,\nGCRTcall), followed by alignment of the resulting basecalled reads to the SIRV isoform\nreference sequences using minimap2 in splice-aware mode. The detected transcripts\nwere then compared with the known SIRV transcripts, and we assessed the correlation\nbetween the estimated transcript abundances and the known input concentrations\n(fmol/ul).\nOur results highlight the superior performance of Coral in both transcript detection\nand quantification accuracy. Coral and Dorado recalled the greatest number of\ntranscripts (Response Fig. 6), with Coral achieving the highest concordance with\nground-truth abundances (Response Fig. 7). The results are also integrated in the main\ntext (Section “Transcript isoform discovery and quantification”, Fig. 4d) and the\nSupplementary Materials (Fig. S13, Fig. S14).\nResponse Figure 6. The number of transcripts discovered by Coral, Guppy, RODAN, Dorado,\nand GCRTcall compared with the SIRV ground truth. Upset plot displays the intersection size\nof SIRV transcripts discovered by the specific basecallers indicated by linked filled dots below each\nbar. The total number of SIRV transcripts identified by different basecallers is shown in the left-\nhand horizontal bars.\nResponse Figure 7. The correlation between estimated isoform abundances obtained from the\nvarious basecallers (Coral, Guppy, RODAN, GCRTcall, Dorado) and the true SIRV\nconcentration levels.\nComment 4: For the newly identified isoforms (for e.g., RPL14-201)- can the authors\ndo some experimental validation, such as RT-PCR, followed by sanger sequencing to\ndetermine whether this new isoform indeed exists? I have the same comment for fusion\ngenes.\nResponse: We are grateful for this important suggestion. Because our study is based\nentirely on publicly available datasets, we do not have access to the original biological\nmaterial required for new wet-lab assays. Nevertheless, we have carried out two\ncomplementary in-silico and orthogonal validation analyses that leverage independent\nshort-read and PCR-validated resources to provide rigorous evidence for the\nauthenticity of Coral-specific isoforms and fusion genes.\nIsoform validation with matched Illumina RNA-seq (A549 dataset). We compared\nisoforms discovered and quantified from ONT reads (basecalled by Coral, Dorado,\nGuppy, RODAN, GCRTcall) with deep-coverage Illumina short-read RNA-seq from\nthe same A549 samples. Coral identified 15,257 isoforms, including 2,219 isoforms\nuniquely detected by Coral but absent from other long-read basecallers. Critically, all\n2,219 Coral-specific isoforms are also supported by Illumina reads, whereas only 54\nGuppy-unique, 99 GCRTcall-unique, 753 RODAN-unique and 477 Dorado-unique\nisoforms passed the same Illumina evidence filter (Response Fig. 8). For Coral-specific\nisoforms corroborated by Illumina, the correlation between long-read and short-read\nexpression estimates is the strongest among all basecallers (Pearson R = 0.569,\nSpearman ρ = 0.558), indicating reliable abundance estimation down to low-expression\nlevels (Response Fig. 9). These results demonstrate that the additional isoforms\nrevealed exclusively by Coral are well supported by an independent Illumina\nsequencing modality. And the additional validation and results are also integrated in our\nrevised manuscript Section “Transcript isoform discovery and quantification” (Fig.\n4c) and the Supplementary Materials (Fig. S10, Fig. S11).\nResponse Figure 8. Overlap analysis of detected isoforms across basecallers and Illumina\nRNA-seq in the A549 dataset. Upset plot (bottom) displays the intersection size of annotated\ntranscripts discovered by Illumina and the specific basecallers indicated by linked filled dots below\neach bar. Intersection sets smaller than 300 elements are not displayed. The total number of\nannotated transcripts identified by different basecallers is shown in parentheses, with each method\ndistinguished by a unique color. Strip plot (upper) shows the abundance distribution of each\nbasecaller across different annotated transcript sets.\nResponse Figure 9. Correlation analysis of transcript abundance estimates between ONT and\nIllumina RNA-seq in the A549 dataset. Scatter plot shows the correlation between transcript\nabundance estimates obtained from ONT long-read data (using ESPRESSO) basecalled by Coral,\nGuppy, RODAN, Dorado, and GCRTcall and those from Illumina RNA-seq reads (using Salmon),\nfocusing on transcripts uniquely detected by each basecaller but also identified in the Illumina data\nfrom the A549 dataset.\nFusion-gene validation with an RT-PCR/Sanger-confirmed MCF-7 dataset. We\nanalyzed direct-RNA ONT data from the MCF-7 breast-cancer cell line, for which 12\nfusion genes have been experimentally confirmed by RT-PCR and Sanger sequencing[1].\nReads basecalled by each method were processed with JAFFAL1 for fusion discovery.\nNotably reads basecalled by Coral achieved the highest median read identity (94.78%,\nTable S10 in supplementary), providing a high-quality input for downstream fusion\ndetection. When compared the JAFFAL results to previously validated fusion events in\nthe Mitelman database, Coral identified 44 known fusion genes, exceeding Guppy's 29,\nRODAN's 20, GCRTcall's 33, and Dorado's 36. For further validation against\nexperimentally confirmed events, we utilized findings from a recent publication[1]. This\nstudy experimentally validated 12 specific fusion genes within the same MCF-7 sample\nusing RT-PCR followed by Sanger sequencing. These 12 PCR-confirmed fusion genes\nserved as a high-confidence ground-truth set for our evaluation. As shown in Response\nFig. 10, Coral successfully identified all 12 (100%) of the experimentally validated\nfusion genes. This outperformed other basecallers: Guppy and Dorado both detected 11\nout of 12 (91.7%), while RODAN detected the fewest. RODAN's lower detection rate\nmay be due to its significantly shorter read lengths (Table S10 in supplementary). Coral\nalso provides the largest proportion of spanning reads aligning across these validated\nfusion gene breakpoints, supporting robust breakpoint evidence. These analyses are\nalso added to the revision in Section “Transcript isoform discovery and\nquantification” (Fig. 5c, Fig. S16). These data confirm that Coral’s improved base-\nlevel accuracy translates into superior detection sensitivity for bona-fide fusion\ntranscripts.\nNevertheless, we fully agree that orthogonal wet-lab confirmation remains the gold\nstandard. For forthcoming work, we are partnering with wet-lab groups to perform\nvalidation on newly discovered barley and human isoforms identified by Coral. We\nhope this comprehensive validation strategy satisfies the reviewer’s request and further\nunderlines Coral’s ability to identify genuine, low-abundance transcript isoforms and\nfusion events.\nResponse Figure 10. The detection status and proportion of spanning reads of 12 specific\nMCF-7 cell fusion genes (validated by RT-PCR and Sanger sequencing) using different\nbasecallers.\nReviewer #3\nComment 1: The authors compare Coral with Guppy which is deprecated by Oxford\nNanopore. To show usefulness of their model they should compare Coral with Dorado.\nAlso, there are new nanopores and sequence kit SQK-RNA004, which are not analyzed\nin this paper\nResponse: Thank you for pointing this out. In this revised manuscript we have\nbenchmarked Coral against Dorado as well as other state-of-the-art and third-party\nbasecallers on both RNA001/002 and RNA004 datasets.\nFor the RNA001/002 chemistry, we broadened training corpus from four to eight\nspecies, then re-evaluated all basecallers on six independent test datasets. Response\nFig. 3 shows that Coral outperformed other basecallers across evaluation metrics.\nNotably, in the human NA12878 sample dataset, Coral reaches 97.69% median identity;\nthe same trend holds for yeast and Arabidopsis, where gains over competing callers\nexceed 2 percentage points. Across all test datasets, Coral achieved an average median\nread accuracy of approximately 97%, representing a notable advancement in\nbasecalling accuracy for RNA001/002 chemistry. Error-rate decompositions confirm\nthat Coral records the lowest median mismatch (0.96%), insertion (0.69%) and deletion\n(1.20%) rates, while simultaneously producing the longest aligned reads and the fewest\nunaligned reads demonstrating that improved basecalling accuracy can facilitate better\nalignment to the reference sequence. These results were also incorporated in the revised\nmanuscript (Section “Basecalling accuracy”, Fig. 2a-f).\nFor the RNA004 chemistry, a dedicated Coral model was trained on Universal Human\nReference RNA sequenced with R10.4.1 pores and validated on an independent SG-\nNEx HEK293T run. We compared Coral with other state-of-the-art basecallers\nincluding Dorado, Guppy, and GCRTcall. Note that RODAN was excluded as it does\nnot support RNA004 chemistry. On this benchmark dataset, Coral achieved a median\nread accuracy of 99.52 %, surpassing Dorado (98.28 %) and markedly outperforming\nboth Guppy and GCRTcall (Response Fig. 2). Coral also exhibited the lowest median\nmismatch (0.10 %), insertion (0.00 %) and deletion (0.14 %) rates, while maintaining\nread length and the proportion of unaligned reads comparable to Dorado. These metrics\ndemonstrated Coral’s robust adaptation to RNA004-specific signal characteristics and\nsupport its superior basecalling fidelity. These RNA004-specific results are now\nincluded in the revised manuscript (Results section, “Basecalling accuracy”, Fig. 2g).\nComment 2: The encoder-decoder architecture with latent alignment variables likely\nintroduces computational overhead compared to traditional CTC-based basecallers.\nWhile the paper mentions dynamic programming and beam search optimizations, a\nmore detailed discussion of runtime performance and memory consumption would be\nbeneficial.\nResponse: We appreciate your insightful observation regarding the computational\noverhead introduced by Coral's encoder-decoder architecture with latent alignment\nvariables (Coral-LSTM). To improve the computational efficiency of Coral, we\nintroduced architectural changes to Coral by replacing the original LSTM-based\ndecoder with a Transformer decoder with efficient cross-attention for alignment and\nstandard beam search for decoding, to reduce the decoding time, and developed the\nCoral-FAST mode, which predicts k consecutive bases per auto-regressive step. We\nprovide both a theoretical complexity analysis (Response Table 1) and empirical\nbenchmarks (Response Figs. 4 and 5) comparing Coral with existing basecallers in\nterms of decoding time, memory usage, and basecalling accuracy. We have also\nincorporated this analysis into the “Discussion” section of the revised manuscript.\nComparison with existing CTC-based basecallers\nAll CTC-based models—Guppy, RODAN, GCRTcall, Dorado, and Coral-CTC (i.e.,\nCoral with CTC decoder)—share the same theoretical decoding complexity of\n𝑂𝑂(𝑇𝑇∗\n, where is the length of signal features, is the dimension size, and is\nthe alphabet size. This is reflected in our benchmarks (Response Figs. 4 and 5), where\nd𝑑∗𝑉𝑉) 𝑇𝑇 𝑑𝑑 𝑉𝑉\nthe decoding time of the basecallers fall within a comparable range. However, Coral-\nCTC consistently outperforms the others in terms of basecalling accuracy—1–5%\nhigher (e.g., Coral-CTC: 94.49% vs Guppy: 89.52%, RODAN: 90.64%, Dorado: 93.46%\non A549 RNA002 data)—at a similar time and memory cost. We attribute this to the\nencoder in Coral, which provides enhanced feature extraction compared to other models.\nAuto-regressive decoding vs. CTC\nCompared to Coral-CTC, our auto-regressive decoding variants—Coral-LSTM (i.e.,\nCoral with LSTM-based decoder), Coral, and Coral-FAST—all leverage a dual-context\ndecoder that integrates long-range signal- and sequence-level dependencies, resulting\nin substantial accuracy improvements. Notably, Coral achieved a basecalling accuracy\nof 97.29% on RNA002 and 99.44% on RNA004 datasets. However, as shown in\nResponse Table 1, the computational complexity of these models scales with the\nsequence length , leading to a marked increase in decoding time and memory\nconsumption (e.g., ~4000 seconds for Coral, as shown in Response Fig. 5). This\n𝐿𝐿\noverhead primarily arises from the long length of ONT reads and the inherent sequential\nlimitations of auto-regressive models. The complexity of Coral-FAST is theoretically\nreduced to . As a result, Coral-FAST achieves a nearly 4× speed-up\n𝐿𝐿\nover Coral-𝑂𝑂LS(𝑘𝑘T∗M𝑁𝑁 w∗h𝑑𝑑ile∗ 𝑑𝑑fu )rther improving accuracy (from 95.56% to 96.75% on\nRNA002). Compared to Coral, Coral-FAST also demonstrates a 4× speed-up with a\nnegligible reduction in median accuracy (~0.03%) on RNA004, demonstrating a strong\ntrade-off between accuracy and efficiency.\nIn conclusion, while Coral’s auto-regressive variants incur higher computational cost,\nthey deliver substantial accuracy improvements. Moreover, the progressive\narchitectural refinements—particularly Coral-FAST—strike a better balance between\ndecoding performance and resource efficiency.\nComment 3: Ablation study should be standard for each deep learning model. The\nauthors should perform an ablation study to better understand the relevance of each\ncomponent to the model.\nResponse: Thank you for the suggestions. We have now performed an ablation study\nfor the Coral architecture, and the results are presented in the revised manuscript in\nSection “Effectiveness of modeling contextual dependencies” and Tables S4, S5, S6\nand S7 in the Supplementary Material. The study investigates the impact of key\ncomponents, including:\nFirst, different decoder architectures (Transformer, LSTM, and CTC) were evaluated\nwhile keeping the CNN-Transformer encoder fixed (Response Table 2). The\nTransformer decoder and LSTM decoder consider both signal and sequence context\ndependencies, while the CTC decoder considers only the context dependencies at the\nsignal level. The transformer decoder outperforms both LSTM and CTC decoders,\nwhich highlights the benefit of the Transformer architecture for decoding.\nSecond, variations in the encoder design were tested with a fixed Transformer decoder.\nThe CNN-Transformer encoder demonstrated superior performance compared to a\nCNN-LSTM encoder (Response Table 3), validating the choice of a fully Transformer-\nbased attention mechanism in the encoder.\nThird, the effect of symbol granularity was investigated by comparing a model\npredicting a single base per step (Coral) with one predicting five consecutive bases per\nstep (Coral-FAST). While the Coral-FAST model is designed for higher throughput, the\nCoral model yielded slightly better accuracy (Response Table 4).\nFinally, the impact of decoder depth was examined by varying the number of decoder\nlayers (3, 6, 9, and 12 layers, corresponding to 1, 2, 3, and 4 decoder blocks,\nrespectively). Performance generally improved with increasing layers, with the 9-layer\nconfiguration achieving the highest accuracy across multiple datasets. Further\nincreasing to 12 layers resulted in either comparable or slightly reduced accuracy,\nsuggesting that performance may reach a saturation point (Response Table 5). These\nstudies collectively affirm the design choices of the Coral architecture.\nComment 4: Figure 1a it should read “Raw signal” instead “Raw singal”\nResponse: Thank you for pointing this out. We have corrected it in this revision.\nComment 5: The authors use old python version 3.7.11 which is not supported on\nnewer Mac processors and it would take significant amount of time for a workaround.\nI suggest authors to update the python version or provide a container image.\nResponse: Thank you for the suggestion. The new version of Coral now supports\nPython 3.10. We also provide the docker container image to facilitate rapid software\ninstallation.\nResponse Table 2. Basecalling performance of Coral decoder variants on test\ndatasets.\nReads Decoder Read Accuracy Read Length Unaligne\nDataset Mismatch (%) Insertion (%) Deletion (%)\nCount network (%) (bp) d Reads\nTransformer 96.93 97.69 0.84 0.59 0.84 0.56 1.4 0.92 991 733 93\nHuman 100000 LSTM 94.84 95.56 1.45 1.23 1.3 0.97 2.42 2.01 988 731 77\nCTC 94.28 95.03 1.28 1.09 0.88 0.72 3.55 3.03 967 717 226\nTransformer 94.14 94.97 1.89 1.64 1.02 0.83 2.94 2.34 657 533 92\nMouse 100000 LSTM 92.38 93.01 2.48 2.27 1.26 1.06 3.87 3.49 657 534 115\nCTC 91.46 92.13 2.25 2.06 0.93 0.81 5.37 4.87 638 518 369\nTransformer 96.92 98.07 1.02 0.6 0.79 0.47 1.27 0.65 855 605 108\nYeast 100000 LSTM 95.44 96.36 1.63 1.25 1.1 0.81 1.83 1.42 855 605 135\nCTC 94.67 95.6 1.49 1.15 0.89 0.72 2.95 2.39 842 597 355\nTransformer 96.76 97.63 0.92 0.64 0.9 0.57 1.42 0.97 984 874 50\nArabidops\n100000 LSTM 95.07 95.97 1.45 1.16 1.22 0.86 2.27 1.87 983 874 52\nis\nCTC 94.39 95.3 1.26 1.01 0.88 0.64 3.47 2.92 961 852 166\nTransformer 95.79 96.93 1.31 0.96 1.34 0.83 1.55 1.06 808 679 54\nPoplar 100000 LSTM 94.28 95.34 1.81 1.47 1.66 1.13 2.25 1.83 809 680 60\nCTC 93.49 94.59 1.69 1.38 1.25 0.86 3.57 2.98 789 662 201\nTransformer 95.44 96.27 1.65 1.34 1.2 0.87 1.72 1.28 1013 880 200\nZebrafish 100000 LSTM 93.98 94.78 2.05 1.77 1.51 1.08 2.46 2.06 1019 888 251\nCTC 93.18 93.96 1.96 1.69 1.03 0.77 3.82 3.35 988 858 385\nThe best results are marked as bold text. The left and right side of the columns “Read Accuracy”, “Mismatch”,\n“Insertion”, “Deletion”, and “Read Length” represent the mean and median values respectively.\nResponse Table 3. Basecalling performance of Coral encoder-variants on test\ndatasets.\nReads Read Accuracy Read Length Unaligned\nDataset Encoder network Mismatch (%) Insertion (%) Deletion (%)\nCount (%) (bp) Reads\nCNN-Transformer 96.93 97.69 0.84 0.59 0.84 0.56 1.4 0.92 991 733 93\nHuman 100000\nCNN-LSTM 95.38 96.49 0.97 0.78 1.19 0.94 2.46 1.48 970 714 330\nCNN-Transformer 94.14 94.97 1.89 1.64 1.02 0.83 2.94 2.34 657 533 92\nMouse 100000\nCNN-LSTM 92.83 93.76 2.02 1.83 1.33 1.12 3.82 2.98 636 519 459\nCNN-Transformer 96.92 98.07 1.02 0.6 0.79 0.47 1.27 0.65 855 605 108\nYeast 100000\nCNN-LSTM 95.55 96.89 1.15 0.82 1.2 0.92 2.1 1.13 836 591 500\nArabidops CNN-Transformer 96.76 97.63 0.92 0.64 0.9 0.57 1.42 0.97 984 874 50\n100000\nis CNN-LSTM 95.53 96.59 1.03 0.8 1.23 0.92 2.21 1.45 963 853 248\nCNN-Transformer 95.79 96.93 1.31 0.96 1.34 0.83 1.55 1.06 808 679 54\nPoplar 100000\nCNN-LSTM 94.27 95.65 1.44 1.17 1.76 1.3 2.54 1.58 787 658 352\nCNN-Transformer 95.44 96.27 1.65 1.34 1.2 0.87 1.72 1.28 1013 880 200\nZebrafish 100000\nCNN-LSTM 93.69 94.9 1.84 1.57 1.66 1.35 2.81 1.87 988 855 585\nThe best results are marked as bold text. The left and right side of the columns “Read Accuracy”, “Mismatch”,\n“Insertion”, “Deletion”, and “Read Length” represent the mean and median values respectively.\nResponse Table 4. Basecalling performance of Coral (predict single base per\nstep) and Coral-FAST (predict 5 bases per step) on test datasets.\nReads Read Accuracy Read Length Unaligne\nDataset Model Mismatch (%) Insertion (%) Deletion (%)\nCount (%) (bp) d Reads\nCoral 96.93 97.69 0.84 0.59 0.84 0.56 1.4 0.92 991 733 93\nHuman 100000\nCoral-FAST 96.18 97.23 0.97 0.77 0.82 0.59 2.03 1.14 980 724 160\nCoral 94.14 94.97 1.89 1.64 1.02 0.83 2.94 2.34 657 533 92\nMouse 100000\nCoral-FAST 93.71 94.61 2.05 1.84 1.03 0.85 3.21 2.46 651 529 175\nCoral 96.92 98.07 1.02 0.6 0.79 0.47 1.27 0.65 855 605 108\nYeast 100000\nCoral-FAST 96.48 97.7 1.17 0.8 0.8 0.51 1.55 0.75 848 601 153\nArabidops Coral 96.76 97.63 0.92 0.64 0.9 0.57 1.42 0.97 984 874 50\n100000\nis Coral-FAST 96.35 97.42 1 0.74 0.89 0.58 1.76 1.05 976 866 68\nCoral 95.79 96.93 1.31 0.96 1.34 0.83 1.55 1.06 808 679 54\nPoplar 100000\nCoral-FAST 95.33 96.69 1.36 1.05 1.3 0.82 2 1.13 800 671 104\nCoral 95.44 96.27 1.65 1.34 1.2 0.87 1.72 1.28 1013 880 200\nZebrafish 100000\nCoral-FAST 95.19 96.18 1.67 1.38 1.11 0.79 2.03 1.33 1009 877 241\nThe best results are marked as bold text. The left and right side of the columns “Read Accuracy”, “Mismatch”,\n“Insertion”, “Deletion”, and “Read Length” represent the mean and median values respectively.\nResponse Table 5. Basecalling performance of Coral using different decoder\nlayers on test datasets.\nReads Decoder Read Accuracy Read Length Unaligned\nDataset Mismatch (%) Insertion (%) Deletion (%)\nCount layers (%) (bp) Reads\n3 96.43 97.29 0.94 0.72 0.94 0.66 1.68 1.11 987 730 125\n6 96.69 97.44 0.91 0.68 0.93 0.65 1.48 1.02 991 733 95\nHuman 100000\n9 97 97.8 0.78 0.54 0.86 0.54 1.36 0.89 991 733 86\n12 96.93 97.69 0.84 0.59 0.84 0.56 1.4 0.92 991 733 93\n3 92.11 93.97 1.89 1.67 1.03 0.84 4.97 2.99 619 508 469\n6 93.97 94.79 1.97 1.72 1.12 0.94 2.94 2.34 656 532 115\nMouse 100000\n9 94.4 95.19 1.81 1.56 1.03 0.83 2.76 2.21 658 534 105\n12 94.14 94.97 1.89 1.64 1.02 0.83 2.94 2.34 657 533 92\n3 96.43 97.65 1.12 0.74 0.88 0.59 1.57 0.8 850 603 148\n6 96.75 97.84 1.08 0.68 0.89 0.56 1.27 0.71 856 606 110\nYeast 100000\n9 97.12 98.24 0.94 0.51 0.81 0.46 1.13 0.58 857 606 109\n12 96.92 98.07 1.02 0.6 0.79 0.47 1.27 0.65 855 605 108\n3 96.35 97.36 0.97 0.7 0.96 0.63 1.73 1.11 976 866 62\nArabidop 6 96.63 97.49 0.96 0.69 0.98 0.65 1.42 1.01 984 874 48\n100000\nsis 9 96.9 97.76 0.87 0.59 0.93 0.57 1.3 0.9 985 875 46\n12 96.76 97.63 0.92 0.64 0.9 0.57 1.42 0.97 984 874 50\n3 95.44 96.68 1.35 1.01 1.37 0.88 1.85 1.17 803 674 60\n6 95.67 96.79 1.36 1.01 1.42 0.91 1.56 1.08 809 679 50\nPoplar 100000\n9 95.93 97.06 1.25 0.88 1.35 0.82 1.47 1.01 809 680 41\n12 95.79 96.93 1.31 0.96 1.34 0.83 1.55 1.06 808 679 54\n3 95.31 96.18 1.66 1.36 1.15 0.83 1.88 1.38 1011 878 242\n6 95.42 96.25 1.66 1.35 1.19 0.86 1.74 1.3 1013 881 207\nZebrafish 100000\n9 95.58 96.43 1.56 1.25 1.17 0.84 1.68 1.26 1013 881 192\n12 95.44 96.27 1.65 1.34 1.2 0.87 1.72 1.28 1013 880 200\n1. The best results are marked as bold text.\n2. The left and right side of the columns “Read Accuracy”, “Mismatch”, “Insertion”, “Deletion”, and “Read\nLength” represent the mean and median values respectively.\nResponse of Manuscript NCOMMS-24-71768A\nWe sincerely thank the reviewers for the thorough evaluation of our manuscript and for\nproviding insightful and constructive comments. Your feedback has been invaluable in\nhelping us improve the clarity and rigor of the paper. We have carefully addressed each\npoint raised, revised the manuscript accordingly, and believe that the changes have\nsignificantly strengthened the work.\nReviewer #1\nComment 1: I thank the authors for their hard work. Most of my concerns have been\naddressed. I have one remaining concern regarding the computational efficiency of\nCoral. Fig.S9 indicates that Coral/Coral-FAST requires 2-8 times more processing time\nthan Dorado. I suggest the authors further elaborate in the Discussion section on the\nspecific benefits that users can gain from utilizing Coral over Dorado.\nResponse: Many thanks for the valuable suggestion. We acknowledge that Coral\ncurrently requires more processing time than Dorado. This represents a deliberate\ndesign trade-off that prioritizes accuracy over raw computational speed. The primary\nbenefit for users choosing Coral is the ability to train or fine-tune the basecalling model\non bespoke datasets. This capability is particularly crucial for novel research\napplications, such as sequencing non-standard sample types or accurately identifying\nmodified bases, where a proprietary basecaller like Dorado may be suboptimal.\nFurthermore, Coral's open-source framework dismantles the \"black box\" nature of\ncommercial basecallers, empowering the scientific community to scrutinize, innovate,\nand extend the basecalling architecture itself. Therefore, while Dorado is positioned for\nhigh-throughput production sequencing, Coral serves the distinct need for a\ncustomizable tool that can be tailored to push the boundaries of nanopore technology.\nLooking ahead, we are actively exploring methods to accelerate Coral's inference speed.\nMoreover, the implementation of Coral can be further optimized in specific application\nscenarios, i.e., model quantization, knowledge distillation, and hardware acceleration\ncan be applied to significantly reduce the computational overhead. We have\nincorporated this into the “Discussion” section of the revised manuscript to provide\nusers with a clear understanding of Coral's value proposition.\nReviewer #3\nComment 1: The authors evaluate the proposed models on multiple RNA001/002\ndatasets and a human RNA004 dataset. While the evaluation sets correspond to\ndifferent cell lines, and the benefits of having a model trained on as much data as\npossible are obvious, a comprehensive argumentation of the model's generalization\nability needs to include a leave-one-out approach. The authors should evaluate the\nmodel on data that was not seen by the model during the training in any way. This\nwould correspond to removing one species from the RNA001/002 training dataset, and\nevaluating the model on that species, as well as removing one chromosome from the\nRNA004 training dataset and evaluating the model on that chromosome. Such an\nanalysis would show that the model does not memorize reference-specific details and\ncan be used on previously unseen species without the risk of collapsing.\nResponse: Thank you very much for the valuable suggestions. We have conducted the\nleave-one-out cross-validation (LOOCV) experiments to evaluate the generalization\ncapability of the proposed model. Specifically, on the RNA001/002 datasets, we re-\ntrained Coral and other retrainable third-party basecallers, i.e., RODAN and GCRTcall,\nin six runs, each time excluding one species from the training set and evaluating\nperformance on that species’ test reads. For the RNA004 dataset, we re-trained the\nmodels after excluding reads from the representative chromosome 22 in the training\ndata and evaluated them on chr22 reads. Note that Guppy and Dorado do not offer a\ntraining pipeline, we cannot perform LOOCV evaluations with these two basecallers.\nAs shown in Response Table 1, across the six LOOCV runs on the RNA001/002\ndatasets, Coral achieved an average median read accuracy of 95.19%, approximately\n1.74% decrease compared to the original Coral model. Nevertheless, Coral consistently\noutperformed the other two basecallers. In the LOOCV with RNA004 dataset, Coral\nachieved a median accuracy of 97.78%, comparable to Dorado and markedly higher\nthan RODAN (92.44%) and GCRTcall (94.15%) (Response Table 2). These results\ndemonstrate only a slight degradation in accuracy when species- or chromosome-\nspecific data are excluded from training, indicating that Coral effectively learns a robust\nmapping between raw signal and sequence rather than merely memorizing reference-\nspecific patterns. The LOOCV accuracy of Coral is also comparable or superior to that\nof Guppy and Dorado without LOOCV. The corresponding results and discussions have\nbeen included in the revised manuscript and supplementary materials (Section\n“Basecalling accuracy”, Table S4, and Table S5).\nResponse Table 1. Basecalling performance of leave-one-out cross-validation on\nRNA001/002 test datasets.\nDataset Read Read Length\nBasecalle Mismatch(%) Insertion(%) Deletion(%) Unaligned\n(Read Accuracy(%) (bp)\nrs Reads\nCount) mean median mean median mean median mean median mean median\nRODAN 90.45 91.11 2.32 2.09 1.84 1.63 5.39 4.85 847 681 4063\nHuman\nGCRTcall 91.68 92.69 1.8 1.56 1.87 1.42 4.65 4.07 973 722 1445\n(100000)\nCoral 94.8 95.55 1.5 1.28 1.23 1.01 2.46 2 987 730 117\nRODAN 87.78 88.12 3.19 3.05 1.62 1.44 7.4 7.11 498 450 4813\nMouse\nGCRTcall 89.54 90.19 2.69 2.51 1.67 1.49 6.09 5.6 643 522 911\n(100000)\nCoral 92.69 93.46 2.45 2.23 1.26 1.09 3.6 3.03 652 529 154\nYeast RODAN 91.15 91.9 2.51 2.21 2.08 1.89 4.25 3.81 652 553 3465\n(100000) GCRTcall 91.94 92.97 2.22 1.87 1.98 1.77 3.86 3.24 847 601 1071\nCoral 94.65 95.83 1.91 1.46 1.36 1.05 2.08 1.57 853 604 273\nArabidops RODAN 91.6 92.29 2.01 1.81 1.85 1.59 4.54 4.13 723 667 1907\nis GCRTcall 92.17 93.24 1.74 1.48 1.91 1.5 4.17 3.59 969 859 565\n(100000) Coral 95.21 96.15 1.38 1.09 1.02 0.74 2.4 1.9 979 870 65\nRODAN 90.87 91.64 2.49 2.24 2.25 1.95 4.38 4 617 559 3025\nPoplar\nGCRTcall 90.85 91.94 2.44 2.11 2.41 2.01 4.3 3.72 797 669 1168\n(100000)\nCoral 94.48 95.58 1.75 1.41 1.56 1.1 2.2 1.73 805 676 70\nRODAN 88.7 89.27 3.16 2.95 1.75 1.59 6.4 5.92 759 696 2798\nZebrafish\nGCRTcall 88.93 89.6 2.96 2.72 1.8 1.54 6.31 5.83 977 848 1537\n(100000)\nCoral 93.7 94.55 2.25 1.95 1.52 1.2 2.53 2.07 1008 875 348\nThe best results are marked as bold text.\nResponse Table 2. Basecalling performance of leave-one-out evaluation on chr22\nreads of HEK293T RNA004 sample.\nDataset Read Read Length\nMismatch(%) Insertion(%) Deletion(%) Unaligned\n(Read Basecallers Accuracy(%) (bp)\nReads\nCount) mean median mean median mean median mean median mean median\nHEK293T RODAN 91.31 92.44 1.94 1.72 1.32 1.09 5.44 4.55 1074 875 8327\nRNA004 GCRTcall 92.86 94.15 1.48 1.27 1.32 0.93 4.34 3.49 1094 880 3545\nchr22 reads 96.54 97.78 0.93 0.70 0.70 0.36 1.83 1.03 1099 869 1750\nCoral\n(233936)\nThe best results are marked as bold text.\nComment 2: The authors support the proposed encoder-decoder architecture by\nperforming a set of ablation experiments. However, they do not provide many details of\nthe performed ablation experiments. How is the Transformer-decoder replaced by the\nCTC-decoder? How does the architecture of the obtained model compare to Dorado,\nwhich incorporates Transformer architecture and CTC loss? If Coral's CTC decoder\narchitecture corresponds closely to Dorado, what brings the improvement compared\nwith Dorado; is it the size of the model? How does the size of the ablated model compare\nto the original Coral model? Are there differences in that aspect that would affect the\naccuracy difference between the original and ablated model? The proposed encoder\narchitecture includes a 12-fold down-sampling, does that impose issues to CTC\ndecoding?\nResponse: Many thanks for these insightful questions regarding the ablation\nexperiments. To improve the clarity, we have expanded on the methodology and results\nin the revised manuscript. The detailed architecture and parameter size of ablated\nmodels are provided in Response Table 3.\nIn the CTC ablation experiment (Coral-CTC), the autoregressive Transformer decoder\nwas replaced with a single linear projection layer that maps encoder output features to\nnucleotide logits, and the model was subsequently trained with the CTC loss while the\nencoder architecture remained unchanged.\nWhen comparing Coral-CTC and Dorado, it is important to note that Dorado does not\nemploy a single model, but rather utilizes distinct architectures optimized for different\nsequencing kits. Dorado uses an LSTM-encoder with a CTC-decoder (Dorado-\nRNA002) for the RNA001/002 data, but a Transformer-encoder with a CTC-decoder\n(Dorado-RNA004) for the RNA004 data. Coral-CTC is roughly equivalent to Dorado-\nRNA004, since they have the same architecture and a similar model size. The\nperformance gain observed with Coral-CTC over Dorado-RNA002 on the\nRNA001/002 datasets is therefore attributable to the combined effects of its more\npowerful Transformer-based encoder and the associated increase in model parameters.\n(Response Figure 1).\nOur ablation studies were designed to systematically disentangle the effects of model\narchitecture and scale (Response Table 3). The results, summarized in Response\nFigure 1, demonstrate that performance gains are driven by both factors. First, the\nperformance of Coral scales with model size, as demonstrated by the accuracy gains\nfrom Coral-S (44 MB), Coral-M (115 MB), to the larger Coral-Lx variants (356–548\nMB, where x = 3,6,12 represents the number of decoder layers). When the model size\nreaches a certain scale, performance begins to saturate, likely due to limitations in\ntraining data and the inherent complexity of the task— a trend consistent with neural\nscaling laws. Second, architecture remains a critical determinant when comparing\nmodels at a similar parameter scale. For instance, the superior performance of Coral-\nL3 (356MB) over Coral-CTC (292 MB) and Coral-LSTM (309 MB) affirms the\nadvantage of the autoregressive Transformer decoder, while the performance gap\nbetween Coral-L3 and LSTM-Transformer (351MB) highlights the effectiveness of the\nCNN-Transformer encoder. Together, these comparisons provide a clear justification\nfor our adoption of a full Transformer encoder-decoder architecture.\nThe 12-fold down-sampling of our encoder architecture risks creating a signal feature\nsequence too short for stable CTC alignment, as CTC requires the input to be longer\nthan the target nucleotide sequence. To solve this, we added an up-sampling module\nbefore the decoder to preserve sufficient temporal resolution for robust decoding by\nboth CTC and our auto-regressive decoder.\nAll corresponding results and detailed explanations have been provided in the revised\nmanuscript and supplementary materials (Section “Effectiveness of modeling\ncontextual dependencies”, Tables S6, S7, S8, and S10, and Fig. S10).\nResponse Table 3. Architecture and parameter size of ablated models used in\nRNA001/002 test datasets.\nAblated Decoder Encoder Decoder Feature Param\nEncoder type\nmodel type layers layers dimension size(MB)\nLSTM-\nCNN-LSTM Transformer 5 12 512 351\nTransformer\nCoral-CTC CNN-Transformer CTC 18 1 512 292\nCoral-LSTM CNN-Transformer LSTM 18 1 512 309\nCoral-S CNN-Transformer Transformer 3 2 256 44\nCoral-M CNN-Transformer Transformer 1 6 384 115\nCoral-L3 CNN-Transformer Transformer 18 3 512 356\nCoral-L6 CNN-Transformer Transformer 18 6 512 420\nCoral (i.e.,\nCNN-Transformer Transformer 18 12 512 548\nCoral-L12)\nCoral-FAST CNN-Transformer Transformer 18 12 512 554\nResponse Figure 1. Basecalling performance of Coral ablated models with different size on\nRNA001/002 test datasets. ‘Coral-S’ represents a small-scale variant of the Coral model. ‘Coral-\nM’ represents a medium-scale variant of the Coral model. ‘Coral-L3’, ‘Coral-L6’ and ‘Coral’\nrepresent large-scale variants of the Coral model with 3/6/12 decoder layers. ‘Coral-CTC’ and\n‘Coral-LSTM’ are two Coral decoder variants that replace the Transformer decoder with ‘CTC’ and\n‘LSTM’. ‘LSTM-Transformer’ is Coral encoder variant that replace the CNN-Transformer encoder\nwith CNN-LSTM encoder. The basecalling performance is measured using average median\naccuracy across six RNA001/002 test datasets.\nComment 3: The authors do not comment on the overall size of the proposed Coral\nmodel compared with other architectures. This information would benefit the\nunderstanding of all advancements proposed by Coral's architecture.\nResponse: Thanks for the precious suggestion. The results of the ablation studies\n(Response Table 3 and Response Figure 1) demonstrate that performance gains are\ndriven by both model architecture and scale. While accuracy improves with model size\nfor Coral’s Transformer encoder-decoder architecture (e.g., Coral-S vs. Coral-M vs.\nCoral-L), in line with neural scaling laws, architecture remains a critical determinant.\nThis is evident when comparing models of a similar size: the superior performance of\nCoral-L3 over Coral-CTC and Coral-LSTM affirms the advantage of the autoregressive\nTransformer decoder. Similarly, comparing Coral-L3 with LSTM-Transformer\nhighlights the effectiveness of the Transformer encoder. Taken together, these\ncomparisons provide a clear justification for our adoption of a full Transformer\nencoder-decoder architecture with a larger parameter scale to function optimally. Coral\npossesses the largest model scale compared to other basecallers (Response Table 4).\nThis increased scale reflects the demands of its more expressive architecture, which\ncontributes to the state-of-the-art accuracy observed in our results. We have added this\ndiscussion into the revised manuscript and supplementary materials to better\ncontextualize the benefits of our approach (Section “Effectiveness of modeling\ncontextual dependencies”, Tables S6 and S11, and Fig. S10).\nResponse Table 4. Architecture and size of basecallers\nBasecaller Encoder Decoder Param size (MB)\nGuppy CNN-LSTM CTC 11\nRODAN CNN CTC 41\nGCRTcall CNN-Transformer CTC 349\nCNN-LSTM (RNA002); 25 (RNA002);\nDorado CTC\nCNN-Transformer (RNA004) 300 (RNA004)\nTransformer\n548 (RNA002);\nCoral CNN-Transformer (predict single base per\n612 (RNA004)\nauto-regressive step)\nTransformer\n554 (RNA002);\nCoral-FAST CNN-Transformer (predict k bases per auto-\n634 (RNA004)\nregressive step)\nComment 4: The authors propose the architecture where the input is 12-fold down-\nsampled before being passed to the Transformer encoder, and up-sampled at the output\nof the Transformer. Justification for this approach, along with the details of the up-\nsampling module, would be beneficial for understanding the architecture.\nResponse: Many thanks for pointing this out. In the encoder architecture of Coral, a\n12-fold down-sampling is firstly applied to the raw signal to efficiently extract local\ncontextual features and reduce the sequence length, thereby lowering the computational\ncomplexity of the subsequent self-attention operations within the Transformer encoder.\nSince CTC and Transformer decoders require an output sequence from the encoder that\nis longer than the target ribonucleotide sequence to ensure a stable and accurate signal-\nto-sequence alignment, a 12-fold down-sampling risks shortening the sequence\nexcessively. To mitigate this, an up-sampling module is incorporated after the\nTransformer encoder. This module effectively doubles the sequence length, resulting in\nan overall 6-fold down-sampling relative to the raw signal, which preserves the\nnecessary temporal resolution for robust decoding. Specifically, the up-sampling is\nachieved through a linear projection. The feature sequence from the encoder ℎ ∈ 𝑅𝑇×𝑑\n(T is sequence length and d is feature dimension), is transformed using a learnable\nmatrix 𝑈 ∈ 𝑅𝑑×2𝑑 to produce an expanded sequence ℎ′ = ℎ𝑈 ∈ 𝑅𝑇×2𝑑. This output\nis then reshaped and interpreted as a sequence of length 2T with the same feature\ndimension d, denoted as ℎ′ ∈ 𝑅2𝑇×𝑑. This architectural detail and its justification have\nbeen added to the “Methods” section of our revised manuscript for greater clarity.\nComment 5: The proposed regularization loss FRE aims to penalize non-monotonic\nalignments by comparing the consecutive expected alignment positions \\beta. Can the\nauthors comment on the cases where values obtained for \\beta seem to be monotonic\nbut are actually averaged values of distant and unrelated positions in the signal?\nResponse: Thank you for this insightful question regarding the robustness of the\nexpected alignment position calculation. The concern that β could be skewed by\naveraging over distant, unrelated signal positions is valid in theory. However, in practice,\nthe nature of the attention mechanism during training mitigates this issue. The expected\nalignment position 𝛽 for a symbol 𝑦 is calculated as a weighted average over all\n𝑖 𝑖\nsignal positions: 𝛽 = ∑𝑇 𝛼 (𝑖,𝑗) ∗𝑗 , where the attention weight 𝛼 (𝑖,𝑗)\n𝑖 𝑗=1 𝑐 𝑐\nrepresents the learned probability that symbol 𝑦 aligns to the signal at position j.\n𝑖\nWhile this is an average across all positions, the model quickly learns to assign\nmeaningful weights only to relevant parts of the signal. As shown in Response Figure\n2, as training progresses, the cross-attention matrix 𝛼 of final cross-attention layer\n𝑐\n(closest to the output logits) becomes sharply focused and nearly diagonal. The\nprobabilities for distant and unrelated signal positions become vanishingly small (close\nto zero). Consequently, their contribution to the weighted average is negligible, and the\ncalculated value of β accurately reflects the true, localized alignment position.\nTherefore, the regularization loss ℱ effectively further penalizes non-monotonicity\n𝑅𝐸\nwithout being confounded by spurious alignments. A more detailed explanation of the\ncomputation of the expected alignment position has been added to the “Methods”\nsection and the supplementary Fig. S21.\nResponse Figure 2. Visualization of the cross-attention alignment matrix 𝜶 of final cross-\n𝒄\nattention layer at different training steps. The matrix shows the alignment probability between\nthe ribonucleotide sequence (y-axis) and signal representations (x-axis). As training progresses from\nStep 149 to Step 7699, the attention becomes increasingly focused and diagonally aligned.\nComment 6: It would be beneficial if the authors could comment and support the\ndesign choice for training the Coral-FAST model using part of the weights trained with\nthe original Coral approach. Have you tried training from zero? How do the pretrained\nweights help the training, given the objective shift?\nResponse: Many thanks for the suggestion. The decision was primarily driven by the\nneed to ensure training stability. The Coral-FAST model has a significantly larger\noutput vocabulary than the standard Coral model because it predicts k-mers (e.g., 5-\nmers or 6-mers) instead of single bases. This expanded vocabulary makes the model\nmore challenging to train from scratch. Our experiments confirmed this: when training\nfrom a random initialization, the gradient of the loss function exhibited significant\nfluctuations. In contrast, initializing Coral-FAST with weights from a pre-trained Coral\nmodel provided a much more stable and effective starting point for optimization,\nleading to a smoother gradient norm and faster convergence of the loss value (Response\nFigure 3). The reason for training of Coral-Fast with pretrained model weights has been\nadded to the “Methods” section and the supplementary Fig. S22 of the revised\nmanuscript.\nResponse Figure 3. Comparison of training cross-entropy loss (top) and gradient norm (below)\nfor Coral-FAST with and without pre-trained weight initialization over training steps.\nComment 7: It would be important to mention that experimental validation of new\nisoforms and fusion genes is necessary.\nResponse: Thank you very much for the valuable suggestion, and we wholeheartedly\nagree. As our study was based entirely on publicly available data, we did not have\naccess to the original biological material required for wet-lab assays. To rigorously\nvalidate our findings, we therefore performed two complementary in-silico and\northogonal analyses. We validated Coral-specific isoforms using matched Illumina\nshort-read RNA-seq data from the same samples, confirming that our novel discoveries\nare well-supported by this independent sequencing modality. We also validated our\nfusion gene detection using findings from a recent publication that experimentally\nconfirmed 12 specific fusion genes within the same MCF-7 sample with RT-PCR and\nSanger sequencing. Future work will be dedicated to collaborating with experimental\npartners to perform targeted validation of high-confidence transcripts and apply Coral\nto new datasets where biological samples are available, creating a direct pipeline from\ncomputational discovery to experimental verification. We have incorporated this into\nthe “Discussion” section of the revised manuscript to provide a discussion of the current\nstudy's limitation.\nReviewer #4\nComment 1: The installation and running instructions on Github are well written. The\nconda installation and running the model were seamless. The output was well formatted.\nI recommend integrating support for running the model on multiple GPUs. Given the\ncomplexity and amount of data that we usually have for basecalling, this would make\nthe model more practically applicable.\nResponse: Thank you very much for the recommendation. We have updated the code\nof Coral and now the new version supports multiple GPU inference.\nResponse of Manuscript NCOMMS-24-71768B\nWe sincerely thank the reviewers for the thorough evaluation of our manuscript and for\nproviding insightful and constructive comments. Your feedback has been invaluable in\nhelping us improve the clarity and rigor of the paper. We have carefully addressed each\npoint raised, revised the manuscript accordingly, and believe that the changes have\nsignificantly strengthened the work.\nReviewer #3\nComment 1: It would be beneficial if the authors would include the performance of\nGuppy/Dorado on the same subset of reads for the leave-one-out generalization\nevaluation for comparison. We understand that these models are not retrained for\ngeneralization evaluation, but this would provide the information on the exact\ncomparison.\nResponse: Thank you very much for the valuable suggestion. In this revision, we have\nnow included the basecalling performance of Guppy and Dorado in the leave-one-out\ncross-validation (LOOCV) experiments, providing a direct baseline for comparison\n(Response Tables 1 and 2).\nAs shown in Response Table 1, across the six leave-one-species-out runs on the\nRNA001/002 datasets, the LOOCV-trained Coral model consistently shows superiority\nto all other basecallers, including Guppy and Dorado. In the leave-one-chromosome-\nout experiment of the RNA004 dataset (Response Table 2), Coral achieved a median\naccuracy of 97.78%, which is better than the other retrainable models RODAN (92.44%)\nand GCRTcall (94.15%), and remains highly comparable to the non-LOOCV-trained\nDorado model (98.13%). Since the RNA004 dataset is derived from a universal human\nreference, the official Dorado model is supposed to be optimally trained for this type of\ndata. The results demonstrate that Coral could effectively learn a robust mapping\nbetween raw signal and sequence rather than merely memorizing reference-specific\npatterns, highlighting its strong generalization capabilities. The corresponding results\nand discussions have been incorporated into the revised manuscript and supplementary\nmaterials (Section “Basecalling accuracy”, Table S4 and S5).\nResponse Table 1. Basecalling performance of leave-one-species-out cross-\nvalidation on RNA001/002 test datasets.\nDataset Read Accuracy Read Length\nMismatch(%) Insertion(%) Deletion(%) Unaligned\n(Read Basecallers (%) (bp)\nReads\nCount) mean median mean median mean median mean median mean median\nGuppy* 89.88 90.6 2.56 2.32 2.21 2 5.35 4.91 964 713 N/A\nDorado* 90.77 91.32 2.76 2.55 2.17 2 4.3 3.93 900 665 1316\nHuman\nRODAN 90.45 91.11 2.32 2.09 1.84 1.63 5.39 4.85 847 681 4063\n(100000)\nGCRTcall 91.68 92.69 1.8 1.56 1.87 1.42 4.65 4.07 973 722 1445\nCoral 94.8 95.55 1.5 1.28 1.23 1.01 2.46 2 987 730 117\nGuppy* 87.17 87.66 3.88 3.68 2.66 2.52 6.29 5.96 647 524 N/A\nDorado* 88.84 89.47 3.59 3.35 1.96 1.8 5.61 5.21 639 519 1700\nMouse\nRODAN 87.78 88.12 3.19 3.05 1.62 1.44 7.4 7.11 498 450 4813\n(100000)\nGCRTcall 89.54 90.19 2.69 2.51 1.67 1.49 6.09 5.6 643 522 911\nCoral 92.69 93.46 2.45 2.23 1.26 1.09 3.6 3.03 652 529 154\nGuppy* 90.51 91.36 2.78 2.38 2.51 2.32 4.2 3.8 842 597 N/A\nDorado* 93.21 94.42 2.21 1.69 1.8 1.54 2.78 2.25 842 598 848\nYeast\nRODAN 91.15 91.9 2.51 2.21 2.08 1.89 4.25 3.81 652 553 3465\n(100000)\nGCRTcall 91.94 92.97 2.22 1.87 1.98 1.77 3.86 3.24 847 601 1071\nCoral 94.65 95.83 1.91 1.46 1.36 1.05 2.08 1.57 853 604 273\nGuppy* 90.73 91.59 2.32 2.03 2.18 1.93 4.77 4.32 964 855 N/A\nDorado* 93.36 94.36 1.86 1.51 1.69 1.39 3.09 2.63 966 854 509\nArabidopsis\nRODAN 91.6 92.29 2.01 1.81 1.85 1.59 4.54 4.13 723 667 1907\n(100000)\nGCRTcall 92.17 93.24 1.74 1.48 1.91 1.5 4.17 3.59 969 859 565\nCoral 95.21 96.15 1.38 1.09 1.02 0.74 2.4 1.9 979 870 65\nGuppy* 89.26 90.16 2.95 2.64 2.87 2.51 4.91 4.47 792 665 N/A\nDorado* 92.51 93.66 2.26 1.86 2.17 1.74 3.07 2.57 795 668 477\nPoplar\nRODAN 90.87 91.64 2.49 2.24 2.25 1.95 4.38 4 617 559 3025\n(100000)\nGCRTcall 90.85 91.94 2.44 2.11 2.41 2.01 4.3 3.72 797 669 1168\nCoral 94.48 95.58 1.75 1.41 1.56 1.1 2.2 1.73 805 676 70\nGuppy* 86.45 86.9 3.86 3.61 2.18 1.94 7.51 7.23 950 823 N/A\nDorado* 92.81 93.66 2.31 2 1.93 1.63 2.94 2.52 1011 880 506\nZebrafish\nRODAN 88.7 89.27 3.16 2.95 1.75 1.59 6.4 5.92 759 696 2798\n(100000)\nGCRTcall 88.93 89.6 2.96 2.72 1.8 1.54 6.31 5.83 977 848 1537\nCoral 93.7 94.55 2.25 1.95 1.52 1.2 2.53 2.07 1008 875 348\n* Since there are no training pipelines available for Guppy and Dorado, the corresponding original pretrained\nofficial models were used. LOOCV was no applicable to these two models. There is no guarantee that data\nidentical to the test set, or data from the same or closely related species, was unseen during the training process.\nThe best results among all methods are marked as bold text.\nThe best LOOCV results (excluding Guppy and Dorado) are underlined.\n“N/A” on unaligned reads for Guppy in the RNA001/002 test datasets, which include only the reads successfully\nbasecalled by Guppy and aligned to the reference sequence.\nResponse Table 2. Basecalling performance of leave-one-chromosome-out\nevaluation on the chr22 reads of HEK293T RNA004 sample.\nRead\nDataset Read Length\nAccuracy Mismatch(%) Insertion(%) Deletion(%) Unaligned\n(Read Basecallers (bp)\n(%) Reads\nCount)\nmean median mean median mean median mean median mean median\nGuppy* 86.43 87.02 3.81 3.68 2.57 2.37 7.20 6.75 1130 966 26408\nHEK293T\nDorado* 96.77 98.13 0.76 0.55 0.98 0.36 1.49 0.79 1122 898 1747\nRNA004\nRODAN 91.31 92.44 1.94 1.72 1.32 1.09 5.44 4.55 1074 875 8327\nchr22 reads\nGCRTcall 92.86 94.15 1.48 1.27 1.32 0.93 4.34 3.49 1094 880 3545\n(233936)\nCoral 96.54 97.78 0.93 0.70 0.70 0.36 1.83 1.03 1099 869 1750\n* Since there are no training pipelines available for Guppy and Dorado, the corresponding original pretrained\nofficial models were used. LOOCV was no applicable to these two models. There is no guarantee that data\nidentical to the test set, or data from the same or closely related species, was unseen during the training process.\nThe best results among all methods are marked as bold text.\nThe best LOOCV results (excluding Guppy and Dorado) are underlined.\nComment 2: Response Table 3 and Figure 1 show the relation between the model size\nand performance for RNA002 chemistry, where we see that the full Coral model is ~20\ntimes larger in size, and only Coral-S can compare in size with the comparable\nperformance. On the contrary, we see no such comparison for RNA004, where the size\nof the Dorado model is far more comparable to that of Coral. We believe those\ncomparisons would be beneficial.\nResponse: Thank you for the precious suggestion. We have now performed the ablation\nstudy examining performance relative to model size on the HEK293T RNA004 dataset.\nThe architectures, parameter sizes, and performance of the ablated models are provided\nin Response Tables 3 and 4. The results were also visualized in Response Figure 1.\nTo have a fair comparison with Dorado-RNA004 in terms of model size, we configured\na new Coral variant namely Coral-ML to have a parameter size of 308MB, which is\nvery close to that of Dorado-RNA004 model (300MB). Coral-ML achieved a median\naccuracy of 98.71% and outperformed Dorado-RNA004 (98.25%) at the same\nparameter scale. The new results together with the previous RNA001/002 results\nconfirm the advantages of the Coral architecture and the performance of the model\nscales up with the model size (from Coral-S up to Coral).\nThe new results have been provided and discussed in the revised manuscript and\nsupplementary materials (Section “Effectiveness of modeling contextual\ndependencies”, Table S11, Table S12, and Fig S11).\nResponse Table 3. Architecture and parameter size of ablated models used in\nRNA004 test dataset.\nAblated Decoder Encoder Decoder Feature Param\nEncoder type\nmodel type layers layers dimension size(MB)\nLSTM-\nCNN-LSTM Transformer 5 12 512 351\nTransformer\nCoral-CTC CNN-Transformer CTC 18 1 512 292\nCoral-LSTM CNN-Transformer LSTM 18 1 512 309\nCoral-S CNN-Transformer Transformer 3 2 256 44\nCoral-M CNN-Transformer Transformer 1 6 384 115\nCoral-ML CNN-Transformer Transformer 15 3 512 308\nCoral-L3 CNN-Transformer Transformer 18 3 512 356\nCoral-L6 CNN-Transformer Transformer 18 6 512 420\nCoral (i.e.,\nCNN-Transformer Transformer 18 15 512 612\nCoral-L15)\nCoral-FAST CNN-Transformer Transformer 18 15 512 634\nResponse Table 4. Basecalling performance of Coral ablated models on sampled\nreads from HEK293T RNA004 dataset.\nDataset Read Accuracy Read Length\nMismatch (%) Insertion (%) Deletion (%) Unaligned\n(Read Model (%) (bp)\nReads\nCount) mean median mean median mean median mean median mean median\nCoral-CTC 95.32 96.39 0.83 0.63 0.61 0.41 3.25 2.32 1025 759 176111\nCoral-LSTM 96.28 97.55 0.90 0.64 0.81 0.50 2.01 1.17 1044 772 171153\nLSTM-\n96.05 98.15 0.74 0.38 1.02 0.33 2.18 0.74 1015 735 173998\nHEK293T Transformer\nRNA004 Coral-S 93.38 94.37 1.57 1.28 1.65 1.23 3.40 2.66 1026 754 181249\nsampled Coral-M 94.44 96.05 1.47 1.02 1.26 0.79 2.84 1.77 1030 756 178516\nreads Coral-ML 97.33 98.71 0.63 0.31 0.46 0.13 1.58 0.61 1045 775 171036\n(623781) Coral-L3 97.35 98.80 0.65 0.33 0.60 0.19 1.41 0.49 1050 777 170695\nCoral-L6 97.45 98.84 0.59 0.29 0.50 0.14 1.46 0.48 1046 775 170323\nCoral-FAST 97.75 99.37 0.46 0.15 0.38 0.00 1.41 0.21 1045 774 170233\nCoral 97.74 99.39 0.39 0.06 0.35 0.00 1.52 0.33 1047 776 169982\nThe best results are marked as bold text.\nResponse Figure 1. Basecalling performance of Coral ablated models with different size on\nHEK293T RNA004 dataset. ‘Coral-S’ represents a small-scale variant of the Coral model. ‘Coral-\nM’ represents a medium-scale variant of the Coral model. ‘Coral-ML’ represents a medium-large-\nscale variant of the Coral model. ‘Coral-L3’, ‘Coral-L6’ and ‘Coral’ represent large-scale variants\nof the Coral model with 3/6/15 decoder layers. ‘Coral-CTC’ and ‘Coral-LSTM’ are two Coral\ndecoder variants that replace the Transformer decoder with ‘CTC’ and ‘LSTM’. ‘LSTM-\nTransformer’ is Coral encoder variant that replace the CNN-Transformer encoder with CNN-LSTM\nencoder. The basecalling performance is measured using median accuracy on 623k sampled reads\nfrom HEK293T RNA004 data.\nComment 3: The authors motivate the down-then up-sampling approach by the need\nto preserve sufficient temporal resolution for robust decoding, with the up-sampling\ndone along the embedding dimension rather than the temporal dimension. It should be\ndiscussed why a smaller scale down-sampling would not be a good choice compared\nwith this approach, and how exactly this up-sampling approach helps preserve\nsufficient temporal resolution.\nResponse: We thank the reviewer for this insightful question. The encoder architecture\nof Coral is intentionally designed to balance the Transformer's computational cost with\nthe decoder’s requirement for high temporal resolution. To validate this choice, we have\ntrained an ablated Coral-L3 model by removing the up-sampling step and directly\napplying 6-fold down-sampling followed by Transformer layers in the encoder.\nThe ablated Coral-L3 was compared to the standard Coral-L3 model on the RNA004 dataset.\nAs shown in Response Table 5, the standard Coral-L3 attained the same median\naccuracy while requiring substantially fewer computational resources in terms of\nGFLOPs and GPU memory usage. The results indicate that a 12-fold down-sampling\nis sufficient for Transformer-based contextual modeling and is considerably more\nefficient.\nThe up-sampling applied after the Transformer encoder restores an effective 6-fold\ndown-sampling resolution. The design ensures that, during training, the signal feature\nsequence remains longer than the target nucleotide sequence—a necessary condition\nfor stable alignment in both CTC and Transformer decoder. In addition, the 6-fold\nresolution provides finer granularity for decoding. As shown in Response Figure 2,\nthis resolution adequately covers the dwell time of most signal events.\nAll corresponding results and detailed explanations have been provided in the revised\nmanuscript and supplementary materials (Section “Methods”, Table S13, and Fig S22).\nResponse Table 5. Basecalling performance of Coral-L3 and Coral-L3 without up-\nsampling in the encoder on sampled reads from HEK293T RNA004 data.\nDataset Model Read Accuracy Read Length\nMismatch(%) Insertion(%) Deletion(%) GPU Memory\n(Read (Aligned (%) (bp) GFLOPS\nUsage (MB)\nCount) reads) mean median mean median mean median mean median mean median\nHEK293T Coral-L3\n97.40 98.81 0.64 0.32 0.58 0.18 1.37 0.47 1000 726 131 1526\nRNA004 (596880)\nsampled Coral-L3 (w/o\nreads up-sampling) 97.21 98.81 0.60 0.24 0.50 0.11 1.69 0.58 993 722 524 2780\n(830885) (595795)\nThe best results are marked as bold text. GFLOPS (Giga Floating-point Operations Per Second) denotes the\ncomputational cost of the matrix-multiplication operations in the Transformer encoder layers under a batch size of\n64 and a feature dimension of 512. GPU memory usage is reported for the operations performed within the\nTransformer encoder.\nResponse Figure 2. Distribution of dwell times (measured signal points per base) calculated\nfrom one million bases in the HEK293T RNA004 dataset. Dwell times were obtained by aligning\nthe ribonucleotide sequence to the raw electrical signal using the ont-remora tool."}, {"round": 2, "reviewer_comments": {"reviewer_3": "We thank the authors for their hard work and extensive experimental efforts. Most of our concerns regarding the model's generalizability have been addressed with the leave-one-out experiments. Additionally, a substantial portion of the model’s size and design details have been provided. Our concerns regarding the proposed regularization loss FRE, and training of the Coral-FAST model have been addressed fully.\nHowever, several concerns remain regarding the evaluation setup when comparing the models, as well as the lack of comparisons for the RNA004 model.\n1. It would be beneficial if the authors would include the performance of Guppy/Dorado on the same subset of reads for the leave-one-out generalization evaluation for comparison. We understand that these models are not retrained for generalization evaluation, but this would provide the information on the exact comparison.\n2, Response Table 3 and Figure 1 show the relation between the model size and performance for RNA002 chemistry, where we see that the full Coral model is ~20 times larger in size, and only Coral-S can compare in size with the comparable performance. On the contrary, we see no such comparison for RNA004, where the size of the Dorado model is far more comparable to that of Coral. We believe those comparisons would be beneficial.\n3. The authors motivate the down-then up-sampling approach by the need to preserve sufficient temporal resolution for robust decoding, with the up-sampling done along the embedding dimension rather than the temporal dimension. It should be discussed why a smaller scale down-sampling would not be a good choice compared with this approach, and how exactly this up-sampling approach helps preserve sufficient temporal resolution.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response of Manuscript NCOMMS-24-71768B\nWe sincerely thank the reviewers for the thorough evaluation of our manuscript and for\nproviding insightful and constructive comments. Your feedback has been invaluable in\nhelping us improve the clarity and rigor of the paper. We have carefully addressed each\npoint raised, revised the manuscript accordingly, and believe that the changes have\nsignificantly strengthened the work.\nReviewer #3\nComment 1: It would be beneficial if the authors would include the performance of\nGuppy/Dorado on the same subset of reads for the leave-one-out generalization\nevaluation for comparison. We understand that these models are not retrained for\ngeneralization evaluation, but this would provide the information on the exact\ncomparison.\nResponse: Thank you very much for the valuable suggestion. In this revision, we have\nnow included the basecalling performance of Guppy and Dorado in the leave-one-out\ncross-validation (LOOCV) experiments, providing a direct baseline for comparison\n(Response Tables 1 and 2).\nAs shown in Response Table 1, across the six leave-one-species-out runs on the\nRNA001/002 datasets, the LOOCV-trained Coral model consistently shows superiority\nto all other basecallers, including Guppy and Dorado. In the leave-one-chromosome-\nout experiment of the RNA004 dataset (Response Table 2), Coral achieved a median\naccuracy of 97.78%, which is better than the other retrainable models RODAN (92.44%)\nand GCRTcall (94.15%), and remains highly comparable to the non-LOOCV-trained\nDorado model (98.13%). Since the RNA004 dataset is derived from a universal human\nreference, the official Dorado model is supposed to be optimally trained for this type of\ndata. The results demonstrate that Coral could effectively learn a robust mapping\nbetween raw signal and sequence rather than merely memorizing reference-specific\npatterns, highlighting its strong generalization capabilities. The corresponding results\nand discussions have been incorporated into the revised manuscript and supplementary\nmaterials (Section “Basecalling accuracy”, Table S4 and S5).\nResponse Table 1. Basecalling performance of leave-one-species-out cross-\nvalidation on RNA001/002 test datasets.\nDataset Read Accuracy Read Length\nMismatch(%) Insertion(%) Deletion(%) Unaligned\n(Read Basecallers (%) (bp)\nReads\nCount) mean median mean median mean median mean median mean median\nGuppy* 89.88 90.6 2.56 2.32 2.21 2 5.35 4.91 964 713 N/A\nDorado* 90.77 91.32 2.76 2.55 2.17 2 4.3 3.93 900 665 1316\nHuman\nRODAN 90.45 91.11 2.32 2.09 1.84 1.63 5.39 4.85 847 681 4063\n(100000)\nGCRTcall 91.68 92.69 1.8 1.56 1.87 1.42 4.65 4.07 973 722 1445\nCoral 94.8 95.55 1.5 1.28 1.23 1.01 2.46 2 987 730 117\nGuppy* 87.17 87.66 3.88 3.68 2.66 2.52 6.29 5.96 647 524 N/A\nDorado* 88.84 89.47 3.59 3.35 1.96 1.8 5.61 5.21 639 519 1700\nMouse\nRODAN 87.78 88.12 3.19 3.05 1.62 1.44 7.4 7.11 498 450 4813\n(100000)\nGCRTcall 89.54 90.19 2.69 2.51 1.67 1.49 6.09 5.6 643 522 911\nCoral 92.69 93.46 2.45 2.23 1.26 1.09 3.6 3.03 652 529 154\nGuppy* 90.51 91.36 2.78 2.38 2.51 2.32 4.2 3.8 842 597 N/A\nDorado* 93.21 94.42 2.21 1.69 1.8 1.54 2.78 2.25 842 598 848\nYeast\nRODAN 91.15 91.9 2.51 2.21 2.08 1.89 4.25 3.81 652 553 3465\n(100000)\nGCRTcall 91.94 92.97 2.22 1.87 1.98 1.77 3.86 3.24 847 601 1071\nCoral 94.65 95.83 1.91 1.46 1.36 1.05 2.08 1.57 853 604 273\nGuppy* 90.73 91.59 2.32 2.03 2.18 1.93 4.77 4.32 964 855 N/A\nDorado* 93.36 94.36 1.86 1.51 1.69 1.39 3.09 2.63 966 854 509\nArabidopsis\nRODAN 91.6 92.29 2.01 1.81 1.85 1.59 4.54 4.13 723 667 1907\n(100000)\nGCRTcall 92.17 93.24 1.74 1.48 1.91 1.5 4.17 3.59 969 859 565\nCoral 95.21 96.15 1.38 1.09 1.02 0.74 2.4 1.9 979 870 65\nGuppy* 89.26 90.16 2.95 2.64 2.87 2.51 4.91 4.47 792 665 N/A\nDorado* 92.51 93.66 2.26 1.86 2.17 1.74 3.07 2.57 795 668 477\nPoplar\nRODAN 90.87 91.64 2.49 2.24 2.25 1.95 4.38 4 617 559 3025\n(100000)\nGCRTcall 90.85 91.94 2.44 2.11 2.41 2.01 4.3 3.72 797 669 1168\nCoral 94.48 95.58 1.75 1.41 1.56 1.1 2.2 1.73 805 676 70\nGuppy* 86.45 86.9 3.86 3.61 2.18 1.94 7.51 7.23 950 823 N/A\nDorado* 92.81 93.66 2.31 2 1.93 1.63 2.94 2.52 1011 880 506\nZebrafish\nRODAN 88.7 89.27 3.16 2.95 1.75 1.59 6.4 5.92 759 696 2798\n(100000)\nGCRTcall 88.93 89.6 2.96 2.72 1.8 1.54 6.31 5.83 977 848 1537\nCoral 93.7 94.55 2.25 1.95 1.52 1.2 2.53 2.07 1008 875 348\n* Since there are no training pipelines available for Guppy and Dorado, the corresponding original pretrained\nofficial models were used. LOOCV was no applicable to these two models. There is no guarantee that data\nidentical to the test set, or data from the same or closely related species, was unseen during the training process.\nThe best results among all methods are marked as bold text.\nThe best LOOCV results (excluding Guppy and Dorado) are underlined.\n“N/A” on unaligned reads for Guppy in the RNA001/002 test datasets, which include only the reads successfully\nbasecalled by Guppy and aligned to the reference sequence.\nResponse Table 2. Basecalling performance of leave-one-chromosome-out\nevaluation on the chr22 reads of HEK293T RNA004 sample.\nRead\nDataset Read Length\nAccuracy Mismatch(%) Insertion(%) Deletion(%) Unaligned\n(Read Basecallers (bp)\n(%) Reads\nCount)\nmean median mean median mean median mean median mean median\nGuppy* 86.43 87.02 3.81 3.68 2.57 2.37 7.20 6.75 1130 966 26408\nHEK293T\nDorado* 96.77 98.13 0.76 0.55 0.98 0.36 1.49 0.79 1122 898 1747\nRNA004\nRODAN 91.31 92.44 1.94 1.72 1.32 1.09 5.44 4.55 1074 875 8327\nchr22 reads\nGCRTcall 92.86 94.15 1.48 1.27 1.32 0.93 4.34 3.49 1094 880 3545\n(233936)\nCoral 96.54 97.78 0.93 0.70 0.70 0.36 1.83 1.03 1099 869 1750\n* Since there are no training pipelines available for Guppy and Dorado, the corresponding original pretrained\nofficial models were used. LOOCV was no applicable to these two models. There is no guarantee that data\nidentical to the test set, or data from the same or closely related species, was unseen during the training process.\nThe best results among all methods are marked as bold text.\nThe best LOOCV results (excluding Guppy and Dorado) are underlined.\nComment 2: Response Table 3 and Figure 1 show the relation between the model size\nand performance for RNA002 chemistry, where we see that the full Coral model is ~20\ntimes larger in size, and only Coral-S can compare in size with the comparable\nperformance. On the contrary, we see no such comparison for RNA004, where the size\nof the Dorado model is far more comparable to that of Coral. We believe those\ncomparisons would be beneficial.\nResponse: Thank you for the precious suggestion. We have now performed the ablation\nstudy examining performance relative to model size on the HEK293T RNA004 dataset.\nThe architectures, parameter sizes, and performance of the ablated models are provided\nin Response Tables 3 and 4. The results were also visualized in Response Figure 1.\nTo have a fair comparison with Dorado-RNA004 in terms of model size, we configured\na new Coral variant namely Coral-ML to have a parameter size of 308MB, which is\nvery close to that of Dorado-RNA004 model (300MB). Coral-ML achieved a median\naccuracy of 98.71% and outperformed Dorado-RNA004 (98.25%) at the same\nparameter scale. The new results together with the previous RNA001/002 results\nconfirm the advantages of the Coral architecture and the performance of the model\nscales up with the model size (from Coral-S up to Coral).\nThe new results have been provided and discussed in the revised manuscript and\nsupplementary materials (Section “Effectiveness of modeling contextual\ndependencies”, Table S11, Table S12, and Fig S11).\nResponse Table 3. Architecture and parameter size of ablated models used in\nRNA004 test dataset.\nAblated Decoder Encoder Decoder Feature Param\nEncoder type\nmodel type layers layers dimension size(MB)\nLSTM-\nCNN-LSTM Transformer 5 12 512 351\nTransformer\nCoral-CTC CNN-Transformer CTC 18 1 512 292\nCoral-LSTM CNN-Transformer LSTM 18 1 512 309\nCoral-S CNN-Transformer Transformer 3 2 256 44\nCoral-M CNN-Transformer Transformer 1 6 384 115\nCoral-ML CNN-Transformer Transformer 15 3 512 308\nCoral-L3 CNN-Transformer Transformer 18 3 512 356\nCoral-L6 CNN-Transformer Transformer 18 6 512 420\nCoral (i.e.,\nCNN-Transformer Transformer 18 15 512 612\nCoral-L15)\nCoral-FAST CNN-Transformer Transformer 18 15 512 634\nResponse Table 4. Basecalling performance of Coral ablated models on sampled\nreads from HEK293T RNA004 dataset.\nDataset Read Accuracy Read Length\nMismatch (%) Insertion (%) Deletion (%) Unaligned\n(Read Model (%) (bp)\nReads\nCount) mean median mean median mean median mean median mean median\nCoral-CTC 95.32 96.39 0.83 0.63 0.61 0.41 3.25 2.32 1025 759 176111\nCoral-LSTM 96.28 97.55 0.90 0.64 0.81 0.50 2.01 1.17 1044 772 171153\nLSTM-\n96.05 98.15 0.74 0.38 1.02 0.33 2.18 0.74 1015 735 173998\nHEK293T Transformer\nRNA004 Coral-S 93.38 94.37 1.57 1.28 1.65 1.23 3.40 2.66 1026 754 181249\nsampled Coral-M 94.44 96.05 1.47 1.02 1.26 0.79 2.84 1.77 1030 756 178516\nreads Coral-ML 97.33 98.71 0.63 0.31 0.46 0.13 1.58 0.61 1045 775 171036\n(623781) Coral-L3 97.35 98.80 0.65 0.33 0.60 0.19 1.41 0.49 1050 777 170695\nCoral-L6 97.45 98.84 0.59 0.29 0.50 0.14 1.46 0.48 1046 775 170323\nCoral-FAST 97.75 99.37 0.46 0.15 0.38 0.00 1.41 0.21 1045 774 170233\nCoral 97.74 99.39 0.39 0.06 0.35 0.00 1.52 0.33 1047 776 169982\nThe best results are marked as bold text.\nResponse Figure 1. Basecalling performance of Coral ablated models with different size on\nHEK293T RNA004 dataset. ‘Coral-S’ represents a small-scale variant of the Coral model. ‘Coral-\nM’ represents a medium-scale variant of the Coral model. ‘Coral-ML’ represents a medium-large-\nscale variant of the Coral model. ‘Coral-L3’, ‘Coral-L6’ and ‘Coral’ represent large-scale variants\nof the Coral model with 3/6/15 decoder layers. ‘Coral-CTC’ and ‘Coral-LSTM’ are two Coral\ndecoder variants that replace the Transformer decoder with ‘CTC’ and ‘LSTM’. ‘LSTM-\nTransformer’ is Coral encoder variant that replace the CNN-Transformer encoder with CNN-LSTM\nencoder. The basecalling performance is measured using median accuracy on 623k sampled reads\nfrom HEK293T RNA004 data.\nComment 3: The authors motivate the down-then up-sampling approach by the need\nto preserve sufficient temporal resolution for robust decoding, with the up-sampling\ndone along the embedding dimension rather than the temporal dimension. It should be\ndiscussed why a smaller scale down-sampling would not be a good choice compared\nwith this approach, and how exactly this up-sampling approach helps preserve\nsufficient temporal resolution.\nResponse: We thank the reviewer for this insightful question. The encoder architecture\nof Coral is intentionally designed to balance the Transformer's computational cost with\nthe decoder’s requirement for high temporal resolution. To validate this choice, we have\ntrained an ablated Coral-L3 model by removing the up-sampling step and directly\napplying 6-fold down-sampling followed by Transformer layers in the encoder.\nThe ablated Coral-L3 was compared to the standard Coral-L3 model on the RNA004 dataset.\nAs shown in Response Table 5, the standard Coral-L3 attained the same median\naccuracy while requiring substantially fewer computational resources in terms of\nGFLOPs and GPU memory usage. The results indicate that a 12-fold down-sampling\nis sufficient for Transformer-based contextual modeling and is considerably more\nefficient.\nThe up-sampling applied after the Transformer encoder restores an effective 6-fold\ndown-sampling resolution. The design ensures that, during training, the signal feature\nsequence remains longer than the target nucleotide sequence—a necessary condition\nfor stable alignment in both CTC and Transformer decoder. In addition, the 6-fold\nresolution provides finer granularity for decoding. As shown in Response Figure 2,\nthis resolution adequately covers the dwell time of most signal events.\nAll corresponding results and detailed explanations have been provided in the revised\nmanuscript and supplementary materials (Section “Methods”, Table S13, and Fig S22).\nResponse Table 5. Basecalling performance of Coral-L3 and Coral-L3 without up-\nsampling in the encoder on sampled reads from HEK293T RNA004 data.\nDataset Model Read Accuracy Read Length\nMismatch(%) Insertion(%) Deletion(%) GPU Memory\n(Read (Aligned (%) (bp) GFLOPS\nUsage (MB)\nCount) reads) mean median mean median mean median mean median mean median\nHEK293T Coral-L3\n97.40 98.81 0.64 0.32 0.58 0.18 1.37 0.47 1000 726 131 1526\nRNA004 (596880)\nsampled Coral-L3 (w/o\nreads up-sampling) 97.21 98.81 0.60 0.24 0.50 0.11 1.69 0.58 993 722 524 2780\n(830885) (595795)\nThe best results are marked as bold text. GFLOPS (Giga Floating-point Operations Per Second) denotes the\ncomputational cost of the matrix-multiplication operations in the Transformer encoder layers under a batch size of\n64 and a feature dimension of 512. GPU memory usage is reported for the operations performed within the\nTransformer encoder.\nResponse Figure 2. Distribution of dwell times (measured signal points per base) calculated\nfrom one million bases in the HEK293T RNA004 dataset. Dwell times were obtained by aligning\nthe ribonucleotide sequence to the raw electrical signal using the ont-remora tool."}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have answered all of our comments. We support the publication of this manuscript.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter