Genomic language model mitigates chimera artifacts in nanopore direct RNA sequencing

Domain: chemistry
Published: 2026-01-19
Editorial outcome: major_revision

Abstract
Chimera artifacts in nanopore direct RNA sequencing (dRNA-seq) introduce substantial inaccuracies, complicating downstream applications such as transcript annotation and gene fusion detection. Current base calling models are unable to detect or mitigate these artifacts, limiting the reliability and utility of dRNA-seq for transcriptomics research. To address this challenge, we present DeepChopper, a genomic language model specifically designed to identify and remove adapter sequences from base-called dRNA-seq long reads with single-base precision. Operating independently of raw signal or alignment information, DeepChopper effectively eliminates adapter-bridged artifacts. Here, we show that DeepChopper enhances the accuracy of downstream analyses and unlocks the full potential of nanopore dRNA-seq, establishing it as a more robust tool for diverse transcriptomics applications.

Introduction
Long-read RNA sequencing technologies are revolutionizing transcriptomic research by providing unparalleled resolution for detecting complex splicing and gene fusion events often missed by conventional short-read RNA-seq methods. Among these technologies, Oxford Nanopore Technologies (ONT) dRNA-seq stands out by sequencing full-length RNA molecules directly, preserving native RNA modifications and allowing a more accurate and comprehensive analysis of RNA biology. This approach bypasses the inherent limitations of cDNA-based sequencing methods, such as artifacts arising from reverse transcription (RT), template switching, and polymerase chain reaction (PCR) amplification1–3.

Despite these advantages, a critical question remains: Does ONT dRNA-seq introduce technical artifacts? A previous study has suggested that dRNA-seq might generate chimera artifacts, leading to multi-mapped reads4, but systematic characterization of these artifacts (their prevalence, formation mechanisms, and persistence with modern sequencing chemistries such as RNA004 and current Dorado base calling5) remains limited in peer-reviewed literature. These artifacts may result from ligation during library preparation or chimeric reads produced by software missing the open pore signal, potentially confounding downstream analyses such as transcriptome assembly, quantification, and detection of alternative splicing and gene fusion events. Detecting these chimera artifacts is challenging because long-read aligners often produce chimerical alignments from such artifacts that are indistinguishable from those derived from true gene fusion events. Importantly, chimera artifacts frequently contain internal adapter sequences4, suggesting that these adapter-bridged chimeras could theoretically be distinguished from biological chimeras by detecting the presence of internal adapters. However, ONT dRNA-seq base callers, trained in RNA, struggle to properly call these DNA-based adapter sequences under an RNA model6. As a result, current adapter detection tools7–9 cannot exploit this feature to eliminate these adapter-bridged chimeras, leaving the issue unresolved (Supplementary Table 1).

To address this unmet need, we developed DeepChopper, a genomic language model (GLM) for long-read sequence analysis. Leveraging recent advances in large language model (LLM) that can interpret complex genetic patterns10, DeepChopper processes long genomic contexts with single-nucleotide resolution. This capability enables precise identification of ONT adapter sequences within base-called long reads, facilitating the detection and removal of chimera artifacts in dRNA-seq data. Through analysis of both existing and newly generated dRNA-seq data, including those using the most recent RNA004 chemistry, we uncovered the prevalence of chimera artifacts, a critical issue previously overlooked in the long-read sequencing field.

We demonstrated that these artifacts significantly impact transcriptomic analysis by complicating gene fusion detection, transcript annotation, and alternative splicing studies. By both identifying and addressing this problem, our work enhances the reliability and precision of dRNA-seq data, improving its utility in transcriptomic research.

Results
DeepChopper architecture and training
DeepChopper leverages the long-context genomic language model (LCGLM) HyenaDNA11, which excels at capturing long-range dependencies (Fig. 1a). To process sequencing base quality information, DeepChopper extends its framework by incorporating a dedicated quality block, which is a neural network comprising multiple multi-layer perceptrons (MLPs) with residual connections12 (Fig. 1b, See Methods for details). This addition enables the effective utilization of sequencing base quality, a crucial feature for improving prediction accuracy, particularly for distinguishing genuine adapter sequences from similar motifs that may occur naturally within reads or from low-quality regions. By combining broad contextual understanding with nucleotide-level precision, this hybrid architecture allows DeepChopper to accurately identify and process adapters sequences. Reads containing internal adapters are split into multiple records, with 3' end adapters simultaneously trimmed (Fig. 1a), thereby preserving authentic biological sequences and eliminating chimera artifacts.

To further refine the prediction accuracy, DeepChopper implements a post-processing stage using a sliding window and majority vote approach, as illustrated in Fig. 1c. The model applies a sliding window with a stride of 1 across the read, analyzing the distribution of predicted adapter positions within each window (See Methods for details). This refinement process operates on the initial predictions independently for each position, ensuring that each base’s final classification reflects an aggregation of local context without error propagation. By maintaining precise boundary detection at single-nucleotide resolution, this strategy ensures that predicted adapter sequences correspond to biologically plausible boundaries, enabling accurate splitting of chimeric reads into their component sub-reads while minimizing spurious fragmentation.

Comparing to existing general-purpose GLM, DeepChopper is specifically optimized for long-read sequence analysis at single-nucleotide resolution. This fine-grained resolution provides a critical advantage for genomic analysis tasks requiring precise base-level predictions. While DNABERT13 is limited to input sequences of approximately 512bp, DNABERT214 to 10,000bp, and Nucleotide Transformer15 to 6000bp, DeepChopper supports input lengths up to 32 kilobases, sufficient to encompass most complete mRNA transcripts. This 32K nucleotide input limit was selected based on both technical and biological considerations. Technically, the constraint reflects the architectural design and context window limitations of the underlying HyenaDNA model. Biologically, human protein-coding transcripts have a median length of approximately 2.7kb (95th percentile: ~8.7kb, 99th percentile: ~14kb), with over 99.97% of transcripts falling below 32kb based on current annotations (Supplementary Fig. 1). Empirical analysis of our dRNA-seq datasets confirmed that only 0−0.0032% of reads exceeded this threshold across multiple cell lines and chemistries, with mean read lengths ranging from 683 to 1148bp (Supplementary Table 2). This extended input capacity, combined with single-nucleotide tokenization, enables DeepChopper to accurately identify non-reference elements such as ONT adapter sequences with base-pair precision, an essential capability for detecting and recovering adapter-bridged chimeras in dRNA-seq data.

In addition, DeepChopper’s lightweight architecture, consisting of only 4.6 million parameters, makes it computationally efficient and scalable for large-scale dRNA-seq analysis. This is in contrast to models like Evo16, which require billions of parameters and more computational resources.

To train DeepChopper for identifying adapter sequences within dRNA-seq long reads, we utilized data from six human cell lines: HEK293T, A549, HCT116, HepG2, K562 and MCF-7 provided by the Singapore Nanopore Expression Project (SG-NEx)17 (Supplementary Table 2). We curated a training set of 480,000 long reads and a validation set of 60,000 ones initially deemed free of adapters and inserted putative adapter sequences, derived from the raw dRNA-seq data, into these reads to create instances containing internal and 3' end adapters (See Methods for details). An independent test set comprising 60,000 long reads was held out for performance evaluation.

DeepChopper benchmarking and model optimization
We conducted comprehensive benchmarking of DeepChopper against existing ONT adapter trimming tools including Pychopper7, Porechop8, and Porechop_ABI9, though it should be noted that none of these existing tools were specifically designed for dRNA-seq data analysis. Performance evaluation was carried out using the synthetic testing dataset (n=60,000 reads), enabling rigorous assessment of precision, recall, and F1-score metrics. As shown in Supplementary Fig. 2, all existing tools demonstrated negligible performance metrics when processing dRNA-seq adapter sequences, indicating fundamental incompatibility with the dRNA-seq protocol. In contrast, DeepChopper achieved exceptional accuracy in identifying both terminal and internal adapters, with recall, precision, and F1 scores consistently exceeding 0.99. These results highlight DeepChopper’s unique capability to address the specific complexities inherent to dRNA-seq reads and underscore the critical need for purpose-built solutions in this domain.

In addition to adapter-trimming tools, we also evaluated Breakinator18, which is designed to detect chimeric artifacts rather than adapter boundaries. Breakinator was benchmarked separately using real VCaP RNA002 dRNA-seq data. As shown in Supplementary Fig. 3, Breakinator reduced detected artifacts by 62% but simultaneously decreased cDNA support to 4.8% (baseline 5.8%), reflecting substantial loss of biologically supported signal. In contrast, DeepChopper reduced artifacts by 91% while increasing cDNA support to 49%, demonstrating selective removal of true artifacts while preserving biological transcript complexity. A substantial fraction of dRNA-seq artifacts do not appear as chimeric alignments or fail to map (Supplementary Fig. 4), making them inherently undetectable by alignment-rule-based chimera callers.

To further evaluate DeepChopper’s classification accuracy, we assessed its performance at single-nucleotide resolution. As shown in Supplementary Fig. 5, the model exhibited sharply bimodal probability distributions for base-level classification, stratified by ground truth labels. For true adapter bases (Supplementary Fig. 5a), the model consistently assigned high probabilities (>0.8) to the adapter class while suppressing probabilities for the non-adapter class (<0.2). Conversely, for true non-adapter bases (Supplementary Fig. 5b), the model reliably predicted high probabilities for the non-adapter class and low probabilities for the adapter class. The minimal presence of intermediate probability values suggests that DeepChopper makes highly confident predictions with low ambiguity between classes. This decisive classification behavior reflects the model’s robust feature representation, well-calibrated decision boundaries, and strong generalization to previously unseen data.

We further conducted an ablation experiment to assess the contribution of the quality block component in the model architecture. As shown in Supplementary Table 3, inclusion of the quality block led to a marked improvement in performance, with the F1 score increasing from 0.97 to 0.99. This module leverages per-base quality scores to better distinguish genuine adapter sequences from similar motifs that may occur naturally within reads. The enhanced performance suggests that incorporating sequencing quality information enables the model to more effectively filter out spurious signal and improve classification robustness. This experiment was performed using the same training methodology as previous evaluations but with a newly generated, focused dataset of 100,000 reads (See Methods for details).

Chimeric read artifact detection in cancer dRNA-seq data
To assess DeepChopper’s ability to detect chimera artifacts in real data, we generated an independent dRNA-seq dataset using the prostate cancer VCaP cell line, which was excluded from model training. This dataset provides a robust framework for evaluating chimera artifacts in genuine dRNA-seq samples, ensuring that DeepChopper’s performance generalizes beyond the training data. We conducted dRNA-seq of VCaP cells using ONT’s SQK-RNA002 chemistry, consistent with that used in the SG-NEx project. Using a MinION sequencer with four R9.4 flow cells, we generated 9,177,639 long reads in FASTQ format, with base-calling performed using SG-NEx’s Dorado software5. DeepChopper processes input data through a three-stage pipeline: (1) FASTQ-to-Parquet conversion for efficient input/output, (2) adapter prediction using a neural network, and (3) post-processing to trim and segment reads based on predicted adapter positions. To improve runtime, we implemented core functions in Rust, enabled GPU-based inference, and parallelized key components across the pipeline.

We systematically benchmarked DeepChopper’s computational performance to assess scalability for large-scale dRNA-seq studies, testing datasets ranging from 0.1M to 23M reads. Using VCaP read subsamples from 0.1M to 9M reads, we observed near-linear runtime scaling (Supplementary Fig. 6a), with the 9M VCaP dataset requiring approximately 5h on two NVIDIA A100 GPUs. Memory usage increased with input size, peaking at approximately 93GB for CPU-based inference and 56GB for GPU-based execution (Supplementary Fig. 6b). To evaluate performance at substantially larger scale, we benchmarked a merged dataset of 23 million reads combining five cell lines (A549, HCT116, HepG2, K562, and MCF7), which required approximately 10.6h total processing time (23 min for FASTQ conversion, 8.5h for prediction, 1.7h for post-processing). Importantly, runtime continued to scale near-linearly and memory usage remained stable across the entire range, demonstrating that DeepChopper can process datasets larger than 23M reads with no fundamental computational barriers to scaling (See Methods for details).

Applying DeepChopper to the full VCaP dataset increased usable read yield by 3%, resulting in 9,357,913 adapter-trimmed reads. It identified 8,218,172 adapter sequences across 7,990,102 reads (87% of total), most measuring ~70bp, consistent with the expected length of the RMX adapter used in ONT’s SQK-RNA002 dRNA-seq kit (Fig. 2a)19. Analysis of adapter locations revealed that 7,777,624 reads had adapters at the 3' end, while 148,452 contained internal adapters (Fig. 2b), indicating that chimeric artifacts are common in VCaP dRNA-seq data.

Further examination showed that chimera artifacts could arise from the joining of multiple long reads, with the most frequent pattern involving two reads joined by a single internal adapter (Fig. 2c). To validate these findings, we analyzed minimap220 chimeric alignments and compared them to a matched VCaP direct cDNA-seq dataset, which we generated as part of this study. Chimeric reads fully supported by cDNA sequencing were considered bonafide events (See Methods for details). Notably, we also evaluated whether ONT’s Dorado base caller trimming feature could mitigate these artifacts. However, we found that Dorado alone, regardless of trimming, was insufficient to eliminate spurious chimeric alignments. In contrast, DeepChopper reduced unsupported chimeric alignments by around 95% and increased the fraction of cDNA-supported chimeric events from 5.8% to 48.7%, whether applied before or after Dorado trimming (Fig. 2d). These results underscore DeepChopper’s ability to distinguish true biological chimeras from technical artifacts.

To further evaluate the artifactual nature of internal adapters, we analyzed their base quality scores and aligned them to the human reference genome using BLAST-like alignment tool (BLAT)21. Adapter regions identified within chimera artifacts exhibited lower base quality (mean Q-score: 9.96) (Fig. 2e) and poor sequence identity (mean BLAT identity: 0.36) to the reference genome (Fig. 2f), supporting their non-human and non-biological origin.

Finally, we evaluated post-processing performance as a function of the sliding window size used for segmentation. Using a 1M-read subsample, we tested window sizes of 11, 21, 31, 41, and 51 bp. Smaller windows yielded slightly higher cDNA support percentages (47.5% for 11bp vs. 43.3% for 51bp; Supplementary Fig. 7a), but increased fragmentation of reads into 4+ segments (Supplementary Fig. 7b). A 21bp window provided the optimal balance, maintaining high support while minimizing over-segmentation. Based on these results, 21bp was selected as the default setting, and DeepChopper allows users to adjust this parameter for dataset-specific optimization (See Methods for details).

Multi-sample validation across platforms and species
To further evaluate DeepChopper’s performance beyond the VCaP dataset, we performed multi-sample validation across diverse biological systems and sequencing platforms. We began by analyzing dRNA-seq data from the SG-NEx project, comparing chimeric alignments before and after DeepChopper trimming. DeepChopper detected internal adapters in 0.67–1.25% of reads across these datasets (A549: 0.92%, MCF7: 0.67%, HCT116: 1.22%, K562: 0.96%, HepG2: 1.25%), representing 15,690–57,122 affected reads per sample (Supplementary Table 4). Critically, internal adapters accounted for 63–85% of all chimeric reads across cell lines, identifying adapter-bridged artifacts as the predominant source of false RNA cDNA rearrangement22. This systematic occurrence of internal adapters across all tested cell lines indicates that adapter-bridged chimeras are not specific to VCaP but represent a general characteristic of dRNA-seq data. Across these samples, DeepChopper reduced unsupported chimeric alignments by 62% to 84%, while preserving cDNA-supported chimeric alignments without noticeable reduction (Fig. 2g). These results reinforce the widespread presence of chimera artifacts in dRNA-seq and the effectiveness of DeepChopper in selectively removing them without compromising true biological signals.

We next applied DeepChopper to the human WTC11 induced pluripotent stem cell line using data from the Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP)23. This dataset includes cDNA-based long-read sequencing generated with multiple protocols (PCR-cDNA, CapTrap, R2C2) across ONT and Pacific Biosciences (PacBio) platforms, providing a robust benchmarking resource. DeepChopper selectively eliminated only those chimeric alignments not supported by any cDNA-based method (Fig. 2h), further demonstrating its precision in distinguishing genuine chimeras from technical artifacts.

To assess cross-species generalizability, we extended the analysis to the F121-9 mouse embryonic stem cell line, also from the LRGASP dataset. DeepChopper reliably removed chimeric reads not supported by orthogonal cDNA-based sequencing platform (Supplementary Fig. 8), confirming its applicability to both human and non-human transcriptomes.

Importantly, across all datasets, DeepChopper consistently outperformed ONT’s Dorado adapter trimming alone, even when applied as a post-processing step, underscoring its distinct and additive utility in chimera artifact correction.

Chimera artifact analysis in RNA004 chemistry
Recently, ONT released a new SQK-RNA004 chemistry for dRNA-seq, but it remains unclear whether chimera artifacts persist with this update. To investigate, we generated new data from the VCaP cell line using this updated chemistry. We first applied DeepChopper in a zero-shot setting to assess cross-chemistry generalization, as the model was trained exclusively on RNA002 adapter patterns.

In zero-shot application, DeepChopper detected internal adapters in 0.33% of VCaP RNA004 reads (38,878 reads out of 11,714,520 total), lower than the 1.62% observed in VCaP RNA002 (Supplementary Table 4). DeepChopper reduced chimeric alignments by 21% compared to Dorado base-called and adapter-trimmed reads, increasing the proportion of cDNA-supported chimeric alignments from approximately 25% to 30% (Supplementary Fig. 9a). Similar results were observed when DeepChopper was applied after Dorado’s adapter trimming, demonstrating compatibility with standard preprocessing pipelines. Internal adapter-like sequences identified by DeepChopper exhibited low base quality scores (mean Q-score: 7.85) and poor alignment identity to the human genome (mean BLAT identity: 0.38), supporting their classification as artifacts (Supplementary Fig. 9b).

To optimize performance on RNA004 data, we fine-tuned DeepChopper using a dataset with 300,000 reads created from VCaP RNA004 reads (70% training, 20% validation, 10% test) (See Methods for details). The fine-tuned model achieved marginal additional improvement, reducing chimeric alignments by 23–25% compared to Dorado-processed reads, a 3-4% improvement over the zero-shot model (Supplementary Fig. 10). Critically, both the original RNA002-trained model and the RNA004-fine-tuned model preserved all cDNA-supported chimeric alignments, demonstrating that DeepChopper specifically targets adapter-bridged artifacts rather than biological RNA rearrangements22.

While DeepChopper’s reduction of chimeric alignments in RNA004 (21-22%) is lower than in RNA002 (91%), both the reduced artifact prevalence (0.33% vs 1.62%) and the lower reduction magnitude are expected given chemistry improvements designed to reduce artifact formation3,24. Nonetheless, the systematic detection of internal adapters across both RNA002 and RNA004 chemistries confirms that adapter-bridged chimeras remain an inherent characteristic of current dRNA-seq workflows, and DeepChopper’s ability to generalize across chemistries without retraining highlights its robustness for emerging platforms. Both the original RNA002-trained model and the RNA004-fine-tuned model are available in the DeepChopper repository, providing users with optimized options for different sequencing chemistries.

Impact on downstream transcriptome analysis
To investigate factors contributing to chimera artifact formation, we examined gene expression levels and transcript lengths associated with chimeric read artifacts. Genes involved in these artifacts showed higher expression than the general transcriptome (p-value < 2.2 × 10−16; Supplementary Fig. 11a), while exhibiting a similar gene length distribution (Supplementary Fig. 11b). Analysis of chimeric junctions across the genome revealed uneven distribution among chromosomes, with the mitochondrial chromosome (ChrM) showing the highest frequency of chimeric connections per base pair—suggesting a potential hotspot for artifact formation (Fig. 3a). This pattern persisted in RNA004 dRNA-seq data (Supplementary Fig. 11c), indicating that chimera artifacts remain a fundamental limitation of dRNA-seq, regardless of chemistry improvements.

We next assessed how DeepChopper correction influences downstream transcriptome analyses. Using IsoQuant25 to annotate transcripts from VCaP dRNA-seq data, we found that DeepChopper nearly doubled the number of identified transcripts compared to uncorrected data (Fig. 3b). Similar results were observed with RNA004 data (Supplementary Fig. 11d) and when DeepChopper was applied after Dorado’s adapter trimming. The largest gains were observed in full-length transcripts (Full splice match (FSM) category), with additional increases in alternatively spliced isoforms (Incomplete splice match (ISM), Novel in catalog (NIC), and Novel not in catalog (NNC) categories). These findings underscore the effectiveness of DeepChopper in mitigating the detrimental effects of chimera artifacts on transcript annotation.

To further assess the implications of artifact removal, we examined gene fusion detection. DeepChopper-corrected reads yielded an 89% reduction in gene fusion calls by FusionSeeker26 compared to Dorado-trimmed data. Importantly, these reduced fusion calls were not supported by fusions detected in matched short-read RNA-seq data using Arriba27 (Fig. 3c), suggesting they were false positives. Applying DeepChopper after Dorado trimming yielded consistent results, reinforcing its utility regardless of prior processing steps.

Closer inspection of the filtered gene fusion calls revealed a strong enrichment for ribosomal protein genes (Supplementary Fig. 12a). Gene Ontology (GO) enrichment analyses in VCaP (Fig. 3d) and SG-NEx cell lines confirmed this trend, with ribosomal genes frequently appearing in artifact-associated fusions. This enrichment extended to chimera artifacts in RNA004 data as well (Supplementary Fig. 12b). A manual review of a chimeric read identified as an RPS29-COX8A fusion revealed that the DeepChopper-processed region, interpreted as an internal adapter sequence, aligned with low-intensity raw current signals, which is consistent with ONT adapter characteristics (Fig. 3e). The presence of polyA and open pore signals at the boundary of this region further supported an artifact origin rather than a bonafide fusion event. Supplementary Fig. 13 illustrates the corresponding sequence and base qualities, including the alignment of upstream and downstream segments around the detected adapter, confirming that the apparent RPS29-COX8A fusion arises from an adapter-bridged chimera.

In summary, DeepChopper improves the quality of nanopore dRNA-seq data by accurately identifying and splitting adapter-bridged chimera that otherwise confound transcript annotation and gene fusion detection. These improvements are robust across different sequencing chemistries and preprocessing pipelines.

Methods
Cell culture
The human male prostate cancer cell line VCaP was obtained from the American Type Culture Collection (ATCC, Cat# CRL-2876) and cultured under sterile conditions to maintain optimal growth and viability. The cells were grown in Dulbecco’s Modified Eagle Medium (DMEM, high glucose; Gibco, Cat# 11-965-092) supplemented with 10% fetal bovine serum (FBS Opti-Gold, Performance Enhanced, US Origin; Gendepot, Cat# F0900-050) to provide essential growth factors. In addition, the culture medium was enriched with 5ml of 100mM Sodium Pyruvate (Gendepot, Cat# CA017-010) to support cellular metabolism and 5ml of Antibiotics-Antimycotics (100×) (Gendepot, Cat# CA002-010) to prevent microbial contamination. Cells were cultured in 100mm cell culture treated dishes (Thermo Fisher Scientific, Cat# 12-556-002) and incubated at 37°C in a humidified atmosphere containing 5% CO2, with media changes performed every 72h to ensure nutrient availability and waste removal. Cell confluency was regularly monitored and subculturing was performed before reaching 80% confluency to maintain healthy growth conditions and prevent over-confluence stress.

RNA extraction and quantification
Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Cat# 74104) according to the protocol of the manufacturer. The quality and concentration of RNA were assessed using an Agilent 2100 Bioanalyzer. Poly(A)+ RNA was then enriched from total RNA using the Dynabeads™ mRNA Purification Kit (Invitrogen, Cat# 65001), which utilizes oligo(dT) beads for selective mRNA binding. The mRNA was quantified using a Qubit 4 fluorometer and a Qubit RNA HS Assay Kit (Thermo Fisher Scientific, Cat# Q32852). The mRNA preparations were either immediately used to prepare a sequencing library or frozen and stored at -80°C until further use.

Nanopore sequencing
We performed nanopore dRNA-seq sequencing of the enriched mRNA using two different sets: the RNA002 kits with R9.4.1 flow cells and the RNA004 kits with R10.4.1 flow cells. The decision to incorporate the RNA004 kit, a newly released option, was driven by our intention to test its capabilities in conjunction with our DeepChopper tool to optimize data quality and sequencing efficiency. For the RNA002 library, 1μg of poly(A)+ RNA was used as input for library preparation using the Direct RNA Sequencing Kit (SQK-RNA002, ONT) following the manufacturer’s instructions. Nanopore dRNA-seq employs a reverse transcriptase adapter (RTA) that typically binds to the poly(A) tails of messenger RNA (mRNA); subsequently, a sequencing adapter is ligated to the RTA, which guides the mRNA through the nanopore for sequencing. The prepared library was loaded onto four MinION R9.4 flow cells (FLO-MIN106) and sequenced for 48h using the Oxford Nanopore MinION device. For the RNA004 library, 300ng of poly(A)+ RNA was used as input for library preparation using the Direct RNA Sequencing Kit (SQK-RNA004, ONT) according to the protocol of the manufacturer. The library was then loaded onto a PromethION RNA Flow Cell (FLO-PRO004RA) and sequenced on the Oxford Nanopore PromethION device for 72h.

For Direct cDNA sequencing, we utilized the Direct cDNA Sequencing Kit (SQK-DCS109, ONT) following the manufacturer’s protocol. Briefly, 5μg of total RNA was used as input for first-strand cDNA synthesis using Maxima H Minus Reverse Transcriptase (Thermo Fisher Scientific) with the SSP and VN primers provided in the kit. To eliminate potential RNA contamination, we treated the sample with RNase Cocktail Enzyme Mix (Thermo Fisher Scientific). Second-strand cDNA synthesis was carried out using LongAmp Taq Master Mix (New England Biolabs). The resulting double-stranded cDNA underwent end-repair and dA-tailing using the NEBNext Ultra End Repair/dA-Tailing Module (New England Biolabs). Subsequently, sequencing adapters were ligated to the prepared cDNA using Blunt/TA Ligase Master Mix (New England Biolabs). Between each enzymatic step, the cDNA and libraries were purified using AMPure XP beads (Agencourt, Beckman Coulter). We quantified the libraries using a Qubit Fluorometer 3.0 (Life Technologies) to ensure adequate concentration and quality. The final library was loaded onto a MinION R9.4 flow cell and sequenced on the Oxford Nanopore MinION device for 72h.

Training data preparation
We acquired ONT dRNA-seq FAST5 data from the SG-NEx project, which includes six human cell lines: HEK293T, A549, K562, HepG2, MCF7, and HCT11617. The FAST5 files were converted to POD5 format using the POD5 conversion tool (https://pod5-file-format.readthedocs.io). Subsequently, FASTQ files were generated using Dorado (v0.5.2)5 with adapter trimming disabled (–no-trim) and the rna002_70bps_hac@v3 model. The reads were then aligned to the human reference genome (GRCh38) using minimap2 (v2.24)20 with ONT direct RNA-specific parameters (-ax splice -uf -k14) for optimized alignment. The resulting SAM files were then converted to BAM format, indexed, and sorted using SAMtools (v1.19.2)32.

For adapter sequence extraction, we selected primary alignments without supplementary alignments and implemented a refined identification protocol. While 3' end soft-clipped regions were candidates for adapter sequences, we did not assume all such regions corresponded to adapters. Instead, we incorporated a critical biological refinement step: we first identified polyA tails at the beginning of soft-clipped regions, as these represent reliable biological indicators of transcript termination. Only sequences following these polyA tails were designated as potential adapter sequences, while aligned regions were classified as non-adapter sequences. This approach improved the precision of our training data by distinguishing true adapter sequences from other non-adapter soft-clipped regions that might result from alignment artifacts or sequencing errors. By anchoring our adapter identification to known biological features, we reduced the risk of misclassification and ensured the training data more accurately reflected the natural transcript-adapter boundaries encountered in dRNA-seq.

To create artificial chimeric reads, we randomly combined two non-adapter sequences with one adapter sequence to create FASTQ records. The dataset consists of positive examples containing adapter sequences (with a 1:1 ratio of 3' end and internal adapters) and negative examples without any adapter sequences, in a 9:1 ratio. In total, 600,000 data points were generated and divided into training (N=480,000), validation (N=60,000), and test sets (N=60,000) in an 8:1:1 ratio using stratified random sampling.

Language model architecture
DeepChopper approaches adapter sequence identification as a token classification task, utilizing a model with 4.6 million trainable parameters. The system tokenizes biological sequences at single-nucleotide resolution, with each nucleotide (A, C, G, T, and N) serving as a fundamental token. This nucleotide-level granularity enables precise discrimination between artificial adapter sequences and native biological sequences.

At its core, DeepChopper employs HyenaDNA11 as its primary feature extractor. HyenaDNA processes the input sequence using multiple attention-free linear layers with a receptive field, transforming the nucleotide tokens into rich 256-dimensional feature representations. The model handles variable-length sequences through a padding approach, maintaining consistent performance across different sequence lengths while efficiently capturing long-range dependencies.

These features are then fed through a quality block that incorporates standardized base quality scores. Prior to processing, the quality scores are normalized using z-score standardization (μ = 0, σ=1) to ensure numerical stability. The quality block, comprising two MLPs with residual connections (hidden dimensions: 256), processes this normalized quality information while preserving the original sequence features. Each MLP layer is followed by ReLU activation, enhancing the model’s ability to learn complex quality-sequence relationships.

The processed sequence features are subsequently fed into a classification head consisting of a two-layer neural network. This architecture transforms the feature representations into classification outputs at the nucleotide level. The classification module employs a softmax activation function to compute probability distributions across two classes: adapter and non-adapter. For a given nucleotide position with output logits z1 and z2 (corresponding to adapter and non-adapter classes), the softmax function computes class probabilities as:

P(yi=c) = ezc / Σj=1,2 ezj (1)

where P(yi=c) represents the probability that nucleotide position i belongs to class c. The final classification decision is based on the class with the higher probability score. In other words, a nucleotide is classified as an adapter if P(yi=adapter) > P(yi=non-adapter). Hence, a threshold of 0.5 is applied implicitly.

This nucleotide-level classification strategy allows DeepChopper to identify adapter boundaries with high precision, including both terminal and internal adapter sequences.

Model training
DeepChopper processes sequences up to 32,770 nucleotides in length, excluding any longer sequences from analysis. To ensure efficient batch processing, shorter sequences were padded to this maximum length. The model was trained using a supervised learning approach, utilizing sequences labeled with adapter annotations. Training was performed in a High Performance Computing (HPC) cluster using two A100 Graphics Processing Units (GPUs). The batch size was set to 64, and validation was performed every 20,000 steps. The model with the highest validation F1 score for the base prediction task was selected for subsequent analyses. Training was carried out over 60 epochs, with early stopping applied based on validation performance to mitigate overfitting risks.

The Adam optimizer was used for parameter optimization, with settings of β1 = 0.9 and β2 = 0.99933. A learning rate scheduler was used to reduce the learning rate when validation loss ceased improving, starting with an initial learning rate of 2 × 10−5. The cross-entropy loss function was used to update the model parameters, defined as follows:

LBCE = (1/N) Σi=1N [yi log(ŷi) + (1-yi) log(1-ŷi)] (2)

where LBCE is the binary cross-entropy loss, N is the total number of tokens in the input sequence, yi is the ground truth label for the i-th token, and ŷi is the predicted probability for the i-th token.

The average cross-entropy loss across the mini-batch is computed as:

LBatchBCE = (1/B) Σj=1B LBCE(yj, ŷj) (3)

where LBatchBCE is the average binary cross-entropy loss for the mini-batch, B is the batch size (number of sequences in the mini-batch), and yj and ŷj are the true labels and predicted probabilities for the j-th sequence in the batch.

The model evaluation metrics included accuracy, precision, recall and the F1 score, calculated using the following equations:

Precision = TP / (TP+FP) (4)

Recall = TP / (TP+FN) (5)

F1 = 2 × (Precision × Recall) / (Precision + Recall) (6)

The final selection of the model was based on the optimal performance in the validation set. The model is implemented by PyTorch (v2.5.0)34. To identify the best hyperparameter configuration, the Hydra (v1.3.2)35 framework was used.

Ablation study of quality block component
An ablation study was conducted to compare two model variants: one with the quality block component and one without. Both models were trained using the same dataset of 100,000 samples, following the training procedures described in Training data preparation and Model training. All hyperparameters, including learning rate, batch size, and optimization algorithm, were kept constant across both configurations. The only architectural difference was the inclusion or exclusion of the quality block. Evaluation was performed on a held-out test set using the F1 score as the primary metric.

RNA004 chemistry fine-tuning
To optimize DeepChopper’s performance for the current RNA004 sequencing chemistry, we fine-tuned the model using VCaP RNA004 data. Following the same data preparation methodology described in Training data preparation, we generated synthetic training data from VCaP RNA004 base-called reads. The fine-tuning dataset consisted of 300,000 reads, split into training (70%, 210,000 reads), validation (20%, 60,000 reads), and test (10%, 30,000 reads) sets, with positive examples containing adapter sequences (1:1 ratio of 3' end and internal adapters) and negative examples without adapters in a 9:1 ratio.

The fine-tuning process maintained the same model architecture and hyperparameters as the original RNA002 training but allowed the model to adapt to RNA004-specific error profiles and signal characteristics. Training was performed on two NVIDIA A100 GPUs using the optimization settings described in Model training, with early stopping based on validation F1 score. Fine-tuning required approximately 9 hours of total training time. Performance evaluation compared three conditions: (1) Dorado base calling with adapter trimming (baseline), (2) Dorado followed by the original RNA002-trained DeepChopper, and (3) Dorado followed by the RNA004-fine-tuned DeepChopper. Chimeric alignments were evaluated against matched cDNA sequencing data to distinguish biological events from artifacts.

Sliding window approach for prediction refinement
To improve prediction consistency and reduce local noise, a sliding window approach was implemented for post-processing of nucleotide-level classification outputs. This method extends predicted adapter regions and smooths isolated predictions, better reflecting the typical length distribution of adapter sequences in ONT dRNA-seq data. The approach enhances continuity in adapter-labeled regions and mitigates the occurrence of fragmented or spurious classifications.

The refinement operates on the initial (raw) predictions from the model rather than iteratively refining predictions at each step, a design choice driven by the requirement for precise adapter boundary detection at single-nucleotide resolution. For each base position i, we define a window Wi of size w (default: 21 bp) centered at position i. The final classification yi is determined by majority vote of all predictions within the window:

yi = 1 if Σj=i-k i+k pj > w/2 (7)
0 otherwise

where yi is the final prediction for the i-th nucleotide, W is the sliding window size, k is half the window size (k = W/2 - 1), and pj represent the initial predicted label for the j-th nucleotide within the window, where a value of 1 indicates that the nucleotide is part of an adapter sequence, and a value of 0 indicates that it is part of a non-adapter sequence.

The default window size is set to 21 nucleotides, and can be customized using the –smooth-window parameter in the DeepChopper implementation to accommodate dataset-specific characteristics.

Post-processing and filtering
After refining the adapter predictions, four filtering steps were applied to enhance the quality of the final results:
1. A predicted adapter sequence must be at least 13 nucleotides long. Sequences shorter than this length threshold are not considered valid adapters.
2. If a read contains more than four adapter sequences, the entire read sequence is retained without any adapter removal.
3. For reads containing four or fewer adapter sequences, the identified adapters are removed and the read is divided into smaller segments.
4. Any segments resulting from this process that are less than 20 nucleotides long are discarded.

Each remaining segment and its corresponding base quality scores are stored as a single read record in the final FASTQ file. This filtering process separates chimeric read artifacts containing internal adapters into multiple segments, while retaining reads with 3' end adapters as single shortened segments.

All filtering thresholds, including minimum segment length, and maximum adapter count per read, are configurable via command-line parameters in the DeepChopper implementation, allowing users to tailor these settings to dataset-specific requirements or experimental conditions.

Computational benchmarks
All benchmarks were conducted in triplicate using btop (https://github.com/aristocratos/btop, v1.4.0) and nvtop (https://github.com/Syllo/nvtop, v3.1.0) to monitor CPU and GPU memory usage, respectively. Evaluations were performed on high-performance computing infrastructure with 16 CPU cores, 60 GB RAM, and dual NVIDIA A100 GPUs (80GB memory each). Adapter prediction stage used a batch size of 64.

Transcript length distribution analysis
To assess the biological appropriateness of DeepChopper’s 32kb input limit, we analyzed transcript length distributions from two sources: theoretical annotations and empirical sequencing data. For theoretical analysis, all protein-coding transcripts were extracted from Ensembl human genome annotations (GRCh38.115, released July 11, 2025). We calculated median length, 95th percentile, 99th percentile, and the fraction of transcripts exceeding 32kb using Python. For empirical analysis, read length distributions were examined across seven dRNA-seq datasets: A549, MCF7, HCT116, K562, HepG2, VCaP RNA002, and VCaP RNA004. For each dataset, we calculated comprehensive statistics including minimum, maximum, mean, standard deviation, quartiles (Q1, Q2, Q3), and high percentiles (P90, P95, P99). The number and percentage of reads exceeding the 32kb threshold were specifically quantified to assess the practical impact of this limitation.

All read length statistics were computed from Dorado (v0.5.2) base-called reads with the trim option enabled.

Validation of chimera artifact reduction
Cross-platform validation of dRNA-seq chimera artifacts identified by DeepChopper was conducted leveraging ONT direct cDNA sequencing and additional cDNA-based sequencing platforms. Direct cDNA sequencing validation was performed using six cancer cell lines, including the VCaP dataset generated in this study and five published datasets (A549, K562, HepG2, MCF7, and HCT116) obtained from the SG-NEx project17. The direct cDNA data in FAST5 format were converted to POD5 format using the POD5 conversion tool (https://pod5-file-format.readthedocs.io). Subsequently, FASTQ files were generated using Dorado (v0.5.2)5 with adapter trimming enabled (–trim adapters) and the dna_r9.4.1_e8_hac@v3.3 model. The reads were then processed using Pychopper (https://github.com/epi2me-labs/pychopper, v2.7.9) and Cutadapt (v4.2)36 according to a published protocol37. The oriented reads were aligned to the human reference genome (GRCh38) using minimap2 (v2.24)20 with optimized parameters (-ax splice -uf -k14) for spliced alignment. The resulting SAM files were then converted to BAM format, indexed, and sorted using SAMtools (v1.19.2)32.

Additional cDNA-based long-read sequencing data from the WTC11 (human) and F121-9 (mouse) cell lines were used for further validation, incorporating five distinct platforms: ONT PCR-cDNA, ONT CapTrap, ONT R2C2, PacBio cDNA, and PacBio CapTrap. The raw FASTQ files (and FASTA files for ONT R2C2) from these datasets were provided by the LRGASP project23. For the PCR-cDNA data, the reads were processed using Pychopper (https://github.com/epi2me-labs/pychopper, v2.7.9) and Cutadapt (v4.2)36, following the protocol described in ref. 37. ONT reads were then aligned to the human reference genome (GRCh38) or mouse reference genome (GRCm39) using minimap2 (v2.24)20 with the parameters (-ax splice -uf -k14), while PacBio reads were aligned using the parameters (-ax splice:hq -uf). The ONT dRNA-seq data from A549, K562, HepG2, MCF7, HCT116, VCaP, WTC11 and F121-9 cell lines were processed using the same pipeline, except that the F121-9 cell line data was aligned to the mouse reference genome (GRCm39).

To validate the chimeric alignments derived from dRNA-seq, comparisons were made with chimeric alignments identified from cDNA-based data across the specified platforms. Chimeric alignments, defined by a primary alignment and one or more supplementary alignments, each containing the SA tag in the BAM file, were converted into lists of genomic intervals based on their corresponding alignments. The genomic interval lists were then compared between platforms, and overlapping intervals were considered concordant if the distance difference between them was less than 1000bp. Supporting rates were calculated as the proportion of dRNA-seq chimeric alignments corroborated by cDNA-based platforms, thereby providing cross-validation of chimera artifacts identified by DeepChopper.

To empirically assess the applicability of alignment-based artifact detection methods to dRNA-seq, we compared DeepChopper with Breakinator (v1.0)18 using VCaP RNA002 data. Three processing pipelines were evaluated: Dorado base calling with trim option (baseline), Dorado with trim followed by DeepChopper, and Dorado with trim followed by Breakinator. All pipelines started with identical Dorado-trimmed reads to enable direct comparison. Breakinator was applied using default parameters as specified in its documentation18. Chimeric alignments from each pipeline were classified as cDNA-supported (validated by matched direct cDNA sequencing) or unsupported (likely artifacts) using the validation framework described above. Support rates were calculated as the proportion of chimeric alignments corroborated by cDNA-seq data.

Internal adapter sequence characterization
To assess the quality and genomic origin of internal adapter sequences, we randomly subsampled 1 million reads from the VCaP RNA002 and VCaP RNA004 datasets. DeepChopper was applied to identify internal adapter sequences within these reads. For base quality analysis, we extracted quality scores for all nucleotide positions identified as internal adapters, yielding 146,580 sequences from RNA002 and 11,143 from RNA004. Base quality scores (Phred Q-scores) were obtained directly from FASTQ files. For genome alignment analysis, internal adapter sequences were aligned to the human reference genome (GRCh38) with default parameters. Sequences that failed to align to the genome were excluded from the identity distribution analysis. This resulted in 98,807 sequences from RNA002 and 6185 sequences from RNA004 being included in the genome identity analysis. Sequences that failed to align to the genome were excluded from the identity distribution analysis, as they would yield uninformative zero identity scores.

BLAT identity calculation
The accuracy of DeepChopper in detecting adapter sequences was evaluated by aligning the identified sequences to the human reference genome using BLAT21. A BLAT identity score was defined as the ratio of matched bases to the total sequence length:

BLAT Identity = MatchLength / SequenceLength (8)

In this context, match length refers to the number of bases in the query sequence that align with the reference genome, while sequence length denotes the total length of the query sequence. This score provides a quantitative measure of how closely each identified sequence aligns with the reference genome, serving as an indicator of detection accuracy. The alignments were performed using the PxBLAT (v1.2.1)38.

Gene expression analysis and transcript classification
Gene expression levels from dRNA-seq were quantified using IsoQuant (v3.1.2)25, with the parameters (–data_type nanopore –stranded forward –model_construction_strategy default_ont –sqanti_output). The –sqanti_output option enables IsoQuant to generate files containing transcript classification information, analogous to the output provided by SQANTI39.

Gene fusion identification and visualization
For ONT dRNA-seq data, gene fusions were identified using FusionSeeker (v1.0.1)26 with default settings. For short-read RNA-seq data, FASTQ files for the VCaP cell line were obtained from the Cancer Cell Line Encyclopedia (CCLE) project40 under SRA accession SRX5417211. Raw reads were mapped to the hg38 reference genome using STAR (v2.7.11)41, and gene fusion events were detected with Arriba (v2.4.0)27. The gene structure of the RPS29-COX8A fusion was visualized using GSDS (v2.0)42. Base quality scores were generated with a custom Python script, and ion current signals were visualized using Squigualiser (v0.6.3)43. The circos plot for gene fusion events was visualized using chimeraviz (v1.30.0)44.

GO enrichment analysis
GO enrichment analysis of biological processes for genes involved in chimera artifacts identified in dRNA-seq data was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) webserver45. Enrichment significance was assessed using a one-sided modified Fisher’s exact test (EASE score). P values were adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) method. Terms with FDR < 0.05 were considered significant.

Computing resource
All computations were performed on a HPC server equipped with a 64-core Intel(R) Xeon(R) Gold 6338 CPU and 256 GB of RAM. The server was also configured with two NVIDIA A100 GPUs, each with 80GB of memory, enabling efficient processing of both CPU-intensive tasks and GPU-accelerated deep learning workloads.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript introduces DeepChopper, a genomic language model specifically designed to mitigate chimera artifacts in\nnanopore direct RNA sequencing. The authors leverage HyenaDNA, an efficient genome language model utilizing long-\nrange implicit convolution, to generate output embeddings. This is coupled with a multi-layer perceptron (MLP) and a sliding\nwindow-based probability prediction mechanism, enhanced by majority voting for robust predictions. As the first genomic\nlanguage model tailored for long-read sequencing data, this work marks a significant advancement in transcriptomics. It\naddresses a critical challenge in the field, showcasing its utility in improving data quality for applications such as transcript\nannotation and gene fusion detection. The study is innovative, methodologically sound, and a valuable contribution to\ngenomic research. Below, I highlight the manuscript's strengths and suggest revisions to further improve its clarity and\nimpact.\nStrength:\n1. DeepChopper is the first genomic language model tailored for long-read sequencing data. It provides an end to end\npipeline for chimera affected DNA sequence task. Its ability to handle sequences up to 32K nucleotides, combined with its\nintegration of sequencing quality scores, ensures precise detection of adapter sequences at single-nucleotide resolution.\n2. The tool is rigorously validated across multiple platforms, including ONT and PacBio, and different chemistries (RNA002\nand RNA004). Its robustness and practical impact on reducing false positives in gene fusion detection are well-documented.\n3. The method is comparatively faster than traditional methods due to Hyena’s long range efficient capabilities and efficient\ncomputational complexity.\n4. Improvements in transcript annotation and artifact reduction have strong implications for advancing transcriptomics\nresearch.\nSuggestions for Revision:\n1. Sliding Window Rationale: While the 21-nucleotide sliding window approach achieves an effective balance between\nsmoothing and sensitivity, further explanation of how this parameter was optimized would strengthen the methodology\nsection. Clarifying whether alternative window sizes were tested and how their performance compared would provide\nadditional insights.\n2. Other baseline models: Given that other Genome based language models exist (such as DNABERT, DNABERT2, The\nNucleotide Transformers), authors might include results from these models as baselines or discussion of these models to\nbetter understand the capabilities of DeepChopper (in terms of accuracy).\n3. Handling of Long Reads: The manuscript notes that sequences exceeding 32K nucleotides are excluded from analysis.\nAdditional details on the prevalence of such sequences in datasets and the implications of this constraint would help users\nunderstand the tool's potential limitations.\n4: Performance with RNA004 Chemistry: The reduced efficacy of DeepChopper with RNA004 compared to RNA002 (21%\nvs. 91% reduction in chimeric alignments) is an important observation. Including a discussion on differences in adapter\ndesigns or sequencing chemistry and their potential impact on performance would add depth to the analysis.\n5. Computational Resource Documentation: Providing a table summarizing runtime, memory, and GPU/CPU requirements\nfor datasets of varying sizes would be highly beneficial for researchers planning large-scale analyses.\n6. Generalization Guidelines: Including recommendations for parameter tuning in diverse datasets or scenarios would\nenhance the tool’s applicability across different research contexts.\n(Remarks on code availability)\nThe reviewer did not run the code, but review the code and document provided in the GitHub. Github provide all the\nnecessary documents to run the experiments. Overall, the open-source implementation and availability on platforms such as\nPyPI and Hugging Face ensure broad accessibility for the research community.", "reviewer_2": "This manuscript presents DeepChopper, a deep-learning-based tool designed to identify and remove chimera artifacts in\nNanopore direct RNA sequencing (dRNA-seq). The authors propose that sequencing artifacts, particularly internal adapters,\nintroduce errors that affect downstream analyses such as transcript annotation and gene fusion detection. DeepChopper\nleverages HyenaDNA, a genomic language model originally trained on DNA sequences, and integrates sequencing quality\nscores to improve adapter detection. The study evaluates DeepChopper using synthetic training data and real dRNA-seq\ndatasets, claiming a significant reduction in chimeric reads compared to ONT’s Dorado basecaller. However, several\nconcerns arise regarding the justification, methodology, and evaluation of the proposed approach.\n1. The manuscript does not clearly explain why a DNA-trained genomic language model (HyenaDNA) is appropriate for\nRNA sequencing data. RNA has different sequence structures, error profiles, and biological contexts than DNA. Since\nHyenaDNA was trained specifically on human DNA, it likely requires fine-tuning to adapt to RNA sequencing artifacts. The\nauthors should provide a stronger theoretical justification for why this model is expected to work and discuss whether any\nmodifications were made to improve its performance on RNA data. In addition, if non-human dRNA-seq data were to be\nanalysed, DeepChopper, although not evaluated, will likely to be fail.\n2. DeepChopper is introduced as a novel method, but the manuscript does not compare it to existing adapter trimming and\nchimera detection tools such as Porechop, Pychopper, or ONT’s Dorado with adapter trimming enabled (rDNA-seq data was\nbase-called with “--no-trim” option). Without direct comparisons in terms of accuracy, precision, recall, F1-score, and runtime,\nit is unclear whether DeepChopper offers meaningful advantages. A benchmarking analysis is necessary to determine\nwhether DeepChopper outperforms or complements these existing methods.\n3. The training data were created by manually inserting adapters into reads and identifying adapter regions using minimap2\nsoft-clipping. This process assumes that all 3’ soft-clipped regions correspond to adapter sequences, which may not be true.\nFurthermore, chimeric reads were artificially generated, which does not necessarily reflect real ONT sequencing artifacts. The\nauthors should validate their training data approach by analyzing real dRNA-seq datasets to determine whether internal\nadapters appear at similar frequencies and distributions. In addition, the authors used minimap2 (a long-read aligner) to\ngenerate true training data. Why don’t use alignment-based method directly? How the genomic language model will be\nbetter than alignment-based methods? Basically, all the benchmarks (e.g. counting chimeric reads, etc.) were all based on\nalignment results. This needs to be justified.\n4. DeepChopper classifies bases as adapters if the probability score is greater than 0.5, but the manuscript does not justify\nthis threshold. If probability scores are clustered around 0.5, the classification may be unreliable. The authors should provide\na distribution of probability scores and assess whether different cutoffs affect performance. An ROC curve or precision-recall\nanalysis would help determine the optimal threshold for adapter detection.\n5. The manuscript introduces a Quality Block to incorporate sequencing base quality scores, but it does not clearly explain\nhow this improves model performance. Similarly, the sliding window refinement approach lacks detailed methodology,\nincluding how window size and majority voting criteria were selected. The authors should provide an ablation study testing\nthe impact of the Quality Block and a sensitivity analysis for the sliding window parameters.\n6. The manuscript states that DeepChopper has 4.6 million parameters, suggesting it is computationally efficient, but it does\nnot provide any runtime or memory benchmarks. There is no information on how long DeepChopper takes to process a\ndataset, what GPU/CPU resources are required, or how it scales with large sequencing datasets. A computational efficiency\nevaluation is necessary to determine whether DeepChopper is practical for large-scale sequencing projects.\n7. The validation dRNA-seq data were processed using Dorado with the “–no-trim” option, which preserves adapters and\nlikely increases the observed chimeric read count. This choice could artificially inflate the problem DeepChopper aims to\nsolve, making the model appear more effective than it actually is. The authors should repeat their analysis using Dorado with\nadapter trimming enabled to assess whether DeepChopper still provides a significant advantage.\n(Remarks on code availability)\nThe format looks fine. However, I have no data to test it."}, "author_response": "Point-by-point responses\nWe would like to thank the reviewers R1 and R2 for their thorough review of our manuscript.\nPlease find our point-by-point response below.\nR1.General This manuscript introduces DeepChopper, a genomic language model specifically\ndesigned to mitigate chimera artifacts in nanopore direct RNA sequencing. The authors lever-\nage HyenaDNA, an efficient genome language model utilizing long-range implicit convolution, to\ngenerate output embeddings. This is coupled with a multi-layer perceptron (MLP) and a sliding\nwindow-based probability prediction mechanism, enhanced by majority voting for robust predic-\ntions. As the first genomic language model tailored for long-read sequencing data, this work marks\na significant advancement in transcriptomics. It addresses a critical challenge in the field, show-\ncasing its utility in improving data quality for applications such as transcript annotation and gene\nfusion detection. The study is innovative, methodologically sound, and a valuable contribution to\ngenomic research. Below, I highlight the manuscript’s strengths and suggest revisions to further\nimprove its clarity and impact.\nStrength:\n1. DeepChopper is the first genomic language model tailored for long-read sequencing data. It\nprovides an end to end pipeline for chimera affected DNA sequence task. Its ability to handle\nsequences up to 32K nucleotides, combined with its integration of sequencing quality scores,\nensures precise detection of adapter sequences at single-nucleotide resolution.\n2. The tool is rigorously validated across multiple platforms, including ONT and PacBio, and\ndifferentchemistries(RNA002andRNA004). Itsrobustnessandpracticalimpactonreducing\nfalse positives in gene fusion detection are well-documented.\n3. The method is comparatively faster than traditional methods due to Hyena’s long range\nefficient capabilities and efficient computational complexity.\n4. Improvements in transcript annotation and artifact reduction have strong implications for\nadvancing transcriptomics research.\nResponse We sincerely thank the reviewer for their thoughtful and constructive feedback on our\nmanuscriptintroducingDeepChopper. Weappreciatetherecognitionofourwork’ssignificanceand\ninnovation in addressing chimera artifacts in nanopore direct RNA sequencing. Below, we provide\npoint-by-point responses to the reviewer’s suggestions.\nR1.Q1 Sliding Window Rationale: While the 21-nucleotide sliding window approach achieves an\neffective balance between smoothing and sensitivity, further explanation of how this parameter was\noptimized would strengthen the methodology section. Clarifying whether alternative window sizes\nwere tested and how their performance compared would provide additional insights.\nResponse In response to the reviewer’s comment, we conducted additional tests using multiple\nwindow sizes to optimize our approach. These results are presented in Response Figure 1 and\nhave been incorporated into the revised manuscript as a new Extended Data Fig. 3. We have\nalso added detailed explanations of these tests in the revised manuscript (highlighted sections). As\nshown in Response Figure 1, we systematically evaluated window sizes of 11, 21, 31, 41, and 51\nnucleotides to determine the optimal parameter. In Response Figure 1a, the red bars represent the\n1\na Supported by b\ncDNA No support 10 6\n5\n10\n4 10\n3\n10\n2\n10\n1\n10\n0\n10\n1 2 3 4 5 6 7\nThe number of segments per read after trimming\n)elacs\ngol(\ndaer\nfo\n.oN\nWindow size 11 bp\nWindow size 21 bp\n51\n41\n31\n21\n11\n0 20 40 60 80 100\nPercentage\n)pb(\nezis\nwodniW\n43.3%\n44.9%\n46.3%\n47.4%\n47.5%\nResponse Figure 1 (Manuscript Extended Data Fig. 4) Effect of window size on chimeric\nalignment detection and read fragmentation. (a) Analysis of different sliding window sizes\n(11, 21, 31, 41, and51nucleotides)showingthepercentageofcDNA-supportedchimericalignments\n(red bars) in VCaP. Higher percentages indicate better support. (b) Distribution of the number\nof segments per read after trimming (x-axis) for window sizes 11 (gray) and 21 (pink), shown on\na logarithmic scale (y-axis). Data represents sampling of 1M reads from VCaP dataset. Window\nsize 21 maintains similar detection sensitivity to window size 11 while producing significantly fewer\nfragmented reads.\nratio of supporting chimeric alignments by direct cDNA data, with higher percentages indicating\nbetter support. Window sizes 11 and 21 showed the highest supporting ratios (47.5% and 47.4%,\nrespectively), with larger windows showing progressively declining support (46.3%, 44.9%, and\n43.3% for window sizes 31, 41, and 51). However, Response Figure 1b demonstrates a critical\nadvantageofwindowsize21overwindowsize11. Whilebothdetectsimilarpercentagesofchimeric\nalignments, window size 21 produces significantly fewer segments per read after trimming. This\nindicates that window size 21 maintains more contiguous predicted sequences compared to window\nsize 11, which frequently fragments reads into 4-7 segments. This over-segmentation with window\nsize 11 can potentially introduce artifacts and complicate downstream analysis. Window size 21\ntherefore represents the optimal balance point in our testing - maintaining the high detection\nsensitivity of smaller windows while preserving read continuity. Importantly, we recognize that\noptimalparametersmayvarydependingonspecificdatasetcharacteristics. Forthisreason,wehave\nimplementedaflexibleapproachbyprovidingtheparameter–smooth-window inoursoftware,which\nallowsuserstoadjustthewindowsizeaccordingtotheirspecificdatarequirements. Additionally,we\nhave provided a detailed guide to change the –smooth-window and other command-line parameters\nin response to R1.Q6.\nR1.Q2 Other baseline models: Given that other Genome based language models exist (such as\nDNABERT, DNABERT2, The Nucleotide Transformers), authors might include results from these\nmodels as baselines or discussion of these models to better understand the capabilities of Deep-\nChopper (in terms of accuracy).\nResponse We appreciate the reviewer’s suggestion to include comparisons with existing genomic\n2\nlanguage models. In response, we have added a comprehensive discussion in the revised manuscript\n(highlighted) outlining the key differences between DeepChopper and general-purpose genomic\nlanguagemodels. Asclarified,DeepChopperisspecificallydesignedtoaddresstheuniquechallenges\noflong-readsequenceanalysisatsingle-nucleotideresolution. WhilemodelssuchasDNABERT[1],\nDNABERT2 [2], and Nucleotide Transformer [3] are valuable for a broad range of genomic tasks,\ntheircontextlengthlimitations—approximately512bpforDNABERT,10,000bpforDNABERT2,\nand 6,000 bp for Nucleotide Transformer—make them suboptimal for long-read applications. In\ncontrast, DeepChopperprocessessequencesupto32kilobases, acontextlengthspecificallytailored\nto accommodate most full-length mRNA transcripts.\nGiven these fundamental architectural differences, direct performance comparisons are not ap-\npropriate. DeepChopper’s design further enables single-nucleotide resolution, a critical feature for\nbase-level precision that general-purpose models are not optimized to deliver. The revised discus-\nsion now clearly delineates DeepChopper’s unique strengths in the context of long-read sequence\nanalysis, while acknowledging the broader utility of existing genomic language models.\nR1.Q3 Handling of Long Reads: The manuscript notes that sequences exceeding 32K nucleotides\nare excluded from analysis. Additional details on the prevalence of such sequences in datasets and\nthe implications of this constraint would help users understand the tool’s potential limitations.\nResponse Thanksforthisimportantcommentregardingthehandlingofultra-longreads. Wehave\nrevised the manuscript (highlighted) to provide a comprehensive explanation of the 32K context\nlength limitation and its practical implications.\nThe 32K limit was introduced based on both architectural and biological considerations. Archi-\ntecturally, it reflects the context window limitation of the underlying HyenaDNA model [4]. While\nextending this limit is technically possible, doing so would substantially increase computational\ndemands without clear benefits for the vast majority of use cases.\nBiologically, the manuscript now clarifies that human mRNA transcripts have a median length\nof1.5-2kbwithapeakaround2,065bp[5],andevencomplexgenesrarelyexceed10–15kb. Notably,\nthe longest known transcripts, such as titin (TTN), range from 20–25 kb—well within our 32K\nlimit. We also cite nanopore RNA-seq data showing that the maximum aligned read lengths are\ntypically 21 kb, with most reads falling well below this threshold[6].\nThese updates should provide readers with a clear understanding of the rationale and relevance\nof the 32K context limit for most long-read transcriptomic applications.\nR1.Q4 PerformancewithRNA004Chemistry: ThereducedefficacyofDeepChopperwithRNA004\ncompared to RNA002 (21% vs. 91% reduction in chimeric alignments) is an important observation.\nIncluding a discussion on differences in adapter designs or sequencing chemistry and their potential\nimpact on performance would add depth to the analysis.\nResponse We appreciate the reviewer highlighting this important difference in performance be-\ntween RNA002 and RNA004 chemistries. In response, we have expanded our manuscript (high-\nlighted) to address this important observation. Notably, DeepChopper was trained exclusively on\nRNA002 data, which was the predominant sequencing chemistry available during model devel-\nopment. Despite this training limitation, we deliberately included RNA004 evaluation to assess\nDeepChopper’s zero-shot transfer capability to newer chemistries without additional fine-tuning.\nThe observed difference in chimeric alignment reduction (91% for RNA002 vs. 21% for RNA004)\nreflects both the model’s training bias toward RNA002 patterns and fundamental differences in the\nsequencing chemistries themselves.\nsignedtoproducecleanerreadswithfewerartifacts, includingchimericsequences[7]. Thisinherent\nimprovement in the RNA004 protocol means that raw RNA004 data already contains substantially\nfewer chimeric artifacts than RNA002 data, giving DeepChopper less opportunity for further im-\nprovement. In other words, the smaller relative improvement in RNA004 should be contextualized\nbythealreadysuperiorqualityoftheinputdata. Wehaveaddedthisexplanationtothemanuscript\n(highlighted) to provide readers with a more comprehensive understanding of the performance dif-\nferences and to highlight DeepChopper’s zero-shot generalization ability across different sequencing\nchemistries.\nR1.Q5 Computational Resource Documentation: Providing a table summarizing runtime, mem-\nory, and GPU/CPU requirements for datasets of varying sizes would be highly beneficial for re-\nsearchers planning large-scale analyses.\nResponse We thank the reviewer for this valuable suggestion. In response, We have expanded the\ndocumentationofcomputationalresourceintherevisedmanuscriptbyincludinganew Extended\nData Fig. 4 (Response Figure 2) that illustrates the scaling of runtime and memory requirements\nacross different dataset sizes. As shown in Response Figure 2, we benchmarked DeepChopper’s\nperformance on four dataset sizes (0.1M, 0.5M, 1M, and the full 9M reads) to provide researchers\nwithclearexpectationsforplanningtheiranalyses. TheresultsshowthatDeepChoppercanprocess\na full 9-million-read dataset from the VCaP cell line in approximately 5 hours.\nWe have also included a detailed breakdown of resource usage for each processing step:\n1. FASTQ Conversion: This initial stage shows minimal resource requirements for smaller\ndatasets but scales to 25 minutes and 40 GB CPU memory for the full 9M read dataset.\n2. Adapter Prediction: This most computationally intensive stage benefits significantly from\nGPU acceleration, requiring 3 hours and 42 minutes on our full dataset with 33 GB GPU\nmemory and 71 GB CPU memory.\n3. Post-Processing: This stage scales linearly, requiring 55 minutes and 41 GB CPU memory\nfor the full dataset.\nThe benchmarks were conducted using high-performance infrastructure (16 CPU cores, dual\nNVIDIA A100 GPUs), and we have included these specifications in the Methods section (high-\nlighted) to provide complete transparency regarding the computing environment. These details\nhave been incorporated into the new “Computational Benchmarks” subsection under Methods.\nThese comprehensive benchmarks demonstrate that DeepChopper remains practical for large-scale\nsequencing projects, efficiently processing millions of reads within hours while maintaining reason-\nable memory requirements.\n4\n5\n4\n3\n2\n1\n0\n0.1M 0.5M 1M Full (9M)\nData Size\n)sruoh(\nemiT\nFASTQ Conversion 70\nPrediction\nPost-Processing 60\nTotal\n50\n40\n30\n20\n10\n0\n0.1M 0.5M 1M Full (9M)\nData Size\n)BG(\nyromeM\na b\nFASTQ Conversion (CPU)\nPrediction (CPU)\nPost-Processing (CPU)\nPrediction (GPU)\nResponseFigure2(ManuscriptExtendedDataFig. 3)Computationalperformancemetrics\nacross different data sizes from the VCaP cell line dRNA-seq. (a) Runtime analysis\nshowing processing time requirements for different pipeline stages (FASTQ Conversion, Prediction,\nPost-Processing) and total runtime across four dataset sizes : subsampled (0.1M, 0.5M, 1M) and\nfull (9M) reads derived from the VCaP dRNA-seq. As data size increases, prediction time becomes\nthe dominant component, with the full dataset requiring approximately 5 hours of total processing\ntime. (b)MemoryusagecomparisonbetweenCPUandGPUimplementationsacrossthesamedata\nsizes. The prediction stage shows consistently higher memory requirements, with CPU memory\nusage for prediction reaching approximately 70 GB for the larger datasets. All measurements\ninclude error bars representing standard deviation from three runs.\nR1.Q6 Generalization Guidelines: Including recommendations for parameter tuning in diverse\ndatasets or scenarios would enhance the tool’s applicability across different research contexts.\nResponse We thank the reviewer for this valuable suggestion. In response, We have created\na dedicated “Parameter Optimization Guidelines” (https://github.com/ylab-hi/DeepChopper/\nblob/main/documentation/parameters.md)sectioninourGitHubdocumentationtoclarifyDeep-\nChopper’s applicability across different research contexts. This documentation provides detailed\nrecommendations for optimizing four key command-line parameters used in the post-processing\nstep:\n1. Sliding Window Size (–smooth-window): We recommend the default value of 21 nucleotides\nfor most applications, as it optimally balances detection sensitivity and read continuity as\nshown in our comparative analysis (Response Figure 1).\n2. Minimum Interval Size (–min-interval-size): This post-smoothing parameter (default: 13)\ncan be adjusted based on adapter characteristics, with lower values increasing sensitivity to\nshort adapter fragments.\n3. Maximum Process Intervals (–max-process-intervals): This parameter (default: 4) limits\nadapterregionsprocessedperreadandcanbereducedtodecreasefragmentationorincreased\nfor complex libraries.\n5\n4. Minimum Read Length (–min-read-length): This final filtering parameter (default: 20) pre-\nvents the generation of extremely short fragments and can be adjusted based on the applica-\ntion’s requirements.\nThese revisions directly address the reviewer’s suggestions and contribute to improving the\noverall clarity and utility of the manuscript. We thank the reviewer again for their thoughtful\nfeedback, which has helped refine and strengthen the work.\nR2.General This manuscript presents DeepChopper, a deep-learning-based tool designed to iden-\ntify and remove chimera artifacts in Nanopore direct RNA sequencing (dRNA-seq). The authors\npropose that sequencing artifacts, particularly internal adapters, introduce errors that affect down-\nstream analyses such as transcript annotation and gene fusion detection. DeepChopper leverages\nHyenaDNA, a genomic language model originally trained on DNA sequences and integrates se-\nquencing quality scores to improve adapter detection. The study evaluates DeepChopper using\nsynthetic training data and real dRNA-seq datasets, claiming a significant reduction in chimeric\nreads compared to ONT’s Dorado basecaller.\nResponse We appreciate the reviewer’s thoughtful and constructive feedback on our manuscript\npresenting DeepChopper. Below, we provide detailed responses to each point raised.\nR2.Q1 The manuscript does not clearly explain why a DNA-trained genomic language model\n(HyenaDNA)isappropriateforRNAsequencingdata. RNAhasdifferentsequencestructures,error\nprofiles, and biological contexts than DNA. Since HyenaDNA was trained specifically on human\nDNA, it likely requires fine-tuning to adapt to RNA sequencing artifacts. The authors should\nprovide a stronger theoretical justification for why this model is expected to work and discuss\nwhether any modifications were made to improve its performance on RNA data. In addition, if\nnon-human dRNA-seq data were to be analysed, DeepChopper, although not evaluated, will likely\nto be fail.\nResponse We thank the reviewer for raising this important point regarding the use of a DNA-\ntrained genomic language model (HyenaDNA [4]) for RNA sequencing data. We provide the fol-\nlowing clarifications:\n• Adapter Sequences: The adapter sequences that we detect are synthetic DNA sequences\nligatedtoRNAmoleculesduringlibrarypreparation(asdiscussedinRef.3[8]ofthemanuscript).\nSince these adapters are DNA-based, a DNA-trained model is appropriate for this task.\n• Model Transferability: HyenaDNA is designed to learn nucleotide-level genomic patterns,\nwhich are transferable to RNA sequences due to the shared nucleotide composition between\nDNA and RNA. Genomic language models capture contextual relationships between nu-\ncleotides, making them effective for both DNA and RNA analysis (as detailed in Ref.8 [9] of\nthe original manuscript).\n• Fine-Tuning on RNA Data: To ensure optimal performance, we extensively fine-tuned\nHyenaDNA using RNA-specific data, allowing it to adapt to the unique error profiles and\nstructural characteristics of Nanopore dRNA-seq. This fine-tuning process enables the model\nto effectively learn RNA-specific patterns while preserving its core nucleotide understanding\n(see “Training Data Preparation” in the Methods section).\nChopper will likely fail.”\nWe thank the reviewer for raising this important point. While the reviewer expressed concern\nthat DeepChopper may not generalize to non-human dRNA-seq data, our original manuscript\nalready included an analysis demonstrating its applicability beyond human datasets.\nAs presented in Extended Data Fig. 2 of the original submission (now updated and relabeled\nas Extended Data Fig. 5 in the revised manuscript), we applied DeepChopper to dRNA-seq\ndata from the F121-9 mouse embryonic stem cell line provided by the LRGASP consortium.\nIn this analysis, DeepChopper effectively removed chimeric read artifacts that lacked support from\ncDNA-based long reads across multiple sequencing platforms, confirming its utility in non-human\nspecies.\nTo further strengthen this point and address the reviewer’s concern related to R2.Q7, we ex-\npanded the figure in the revised manuscript to include new benchmarking results using Dorado\nadapter trimming followed by DeepChopper, as shown in Extended Data Fig. 5 and\nResponse Figure 3 below. These results demonstrate that DeepChopper consistently removes un-\nsupported chimeric alignments in both Dorado trimmed and untrimmed read sets.\nTogether, these results support the generalizability of DeepChopper to non-human dRNA-seq\ndata without requiring species-specific retraining.\ntnemngila\nciremihc\nfo\n.oN\n000,1\ntnemngila\nciremihc\nfo\n.oN\n000,1\nUnsupported\nSupported\ntnemngila\nciremihc\nfo\n.oN\n000,1\ntnemngila\nciremihc\nfo\n.oN\n000,1\nONT PCR cDNA ONT R2C2 PacBio cDNA PacBio CapTrap\n30 30 30 30\n25 25 Unsupported 25 Unsupported 25 Unsupported\n20 20 Supported 20 Supported 20 Supported\n15 15 15 15\n10 10 10 10\n5 5 5 5\n0 0 0 0\nDorado with Tr D I D m o e r e a p d C o h w o i p th p e T r rim D D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D I D m o e r e a p d C o h w o i p th p e T r rim D D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D I D m o e r e a p d C o h w o i p th p e T r rim D D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D I D m o e r e a p d C o h w o i p th p e T r rim D D o e + r e a p d C o h w o / p o p T e r r im +\nResponse Figure 3 (Manuscript Extended Data Fig. 5) Chimeric alignments from direct\nRNA sequencing (dRNA-seq) of the F121-9 cell line (mouse), evaluated for support us-\ning additional Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio)\nsequencing data with different protocols. DeepChopper-involved methods reduce un-\nsupported chimeric alignments across all methods compared to Dorado with adapter\ntrimming. The bar colors indicate chimeric alignments supported by additional se-\nquencing data (red) and those lacking support (grey).\nR2.Q2 DeepChopper is introduced as a novel method, but the manuscript does not compare it\nto existing adapter trimming and chimera detection tools such as Porechop, Pycho Porechop, Pychopper, or ONT’s\nDorado with adapter trimming enabled (rDNA-seq data was base-called with “–no-trim” option).\nWithout direct comparisons in terms of accuracy, precision, recall, F1-score, and runtime, it is\nunclear whether DeepChopper offers meaningful advantages. A benchmarking analysis is necessary\nto determine whether DeepChopper outperforms or complements these existing methods.\nResponse We appreciate the reviewer’s comments regarding the need for benchmarking and com-\nparison of DeepChopper against existing adapter trimming and chimera detection tools. We would\nlike to respectfully clarify that DeepChopper addresses challenges specific to Oxford Nanopore\nTechnologies (ONT) direct RNA sequencing (dRNA-seq)—a protocol that is fundamentally\ndistinct from those targeted by the tools mentioned.\n7\n1. Protocol-Specific Relevance\nDeepChopperisspecificallydesignedtoidentifyandtrimadaptersequencesinONTdirect\nRNAsequencingdata. Incontrast,toolssuchasPychopper[10]areoptimizedforcDNA-\nbased protocols and use models trained on cDNA adapter sequences, which are not present\nin dRNA-seq datasets. Therefore, a direct comparison is not appropriate, as Pychopper is\nnot built to process or accurately identify dRNA-seq adapters.\n2. Limitations of Legacy Tools\nPorechop, another tool cited, is no longer actively maintained (last update over eight years\nago) and was developed prior to the introduction of ONT’s dRNA-seq technology [11]. Our\nanalysis demonstrates that Porechop is unable to detect adapters in dRNA-seq reads, making\na direct comparison with DeepChopper infeasible (see Response Figure 4).\n3. Benchmarking on Synthetic dRNA-seq Data\nTo address the concern, we benchmarked DeepChopper’s performance against a range of\nadapter trimming tools, including those explicitly mentioned by the reviewer (Pychopper,\nPorechop) as well as an additional, more recent extension tool, Porechop ABI [12]. While\nPorechop ABI represents an advancement over the original Porechop, none of these tools are\nspecifically designed for the characteristics of ONT direct RNA sequencing (dRNA-seq)\ndata.\nWe performed benchmarking using a synthetic dRNA-seq dataset generated in the orig-\ninal manuscript (N = 60,000 reads), which includes known adapter sequences introduced at\ndefined positions. This design allows for robust performance evaluation in terms of precision,\nrecall, and F1-score. As shown in Response Figure 4, all existing tools, including Pore-\nchop ABI, failed to accurately detect dRNA-seq adapter sequences. Their perfor-\nmance metrics were negligible, indicating a fundamental incompatibility with the dRNA-seq\nprotocol.\nIn contrast, DeepChopper accurately identified and trimmed both terminal and\ninternal adapters, highlighting its unique capability to handle the complexities of direct\nRNA reads. These findings reinforce that current tools are insufficient for adapter detection\nin dRNA-seq, and underscore the need for a purpose-built solution such as DeepChopper.\n4. Additional Benchmarking with Dorado\nWe have also included a detailed comparison with Dorado (with adapter trimming en-\nabled) in our response to R2.Q7, where we show that DeepChopper offers superior sensitiv-\nity and specificity for detecting dRNA-seq adapters, particularly in complex cases involving\nchimeric reads.\n8\n1.0\n0.8\n0.6\n0.4\n0.2\n0.0\nPychopper Porechop Porechop_ABI DeepChopper\neulaV\nRecall 0.9940.9920.993\nPrecision\nF1 Score\n0.021 0.0020.004 0.0000.0000.000 0.0010.0000.000\nResponse Figure 4 (Manuscript Extended Data Fig. 1) Performance evaluation in a held-\nout test (N = 60,000) dataset showing Recall, Precision, and F1 values for DeepChop-\nper, Pychopper, Porechop, and Porechop ABI.\nWe have updated the manuscript to include these benchmarking results as Extended Data\nFig. 1 (Response Figure 4) and added Extended Data Table 1 (Response Table 1), which\nprovides a comparative summary of the capabilities and limitations of current tools versus Deep-\nChopper. These results support the conclusion that existing tools are not well-suited for adapter\ntrimming in dRNA-seq, underscoring the need for a dedicated solution.\nResponse Table 1 (Manuscript Extended Data Table 1) Summary of Adapter Trimming Tools\nfor analyzing dRNA-seq data\ndRNA-seq dRNA-seq\nTrimming existing\nterminal internal\nAdapter trimming tool dRNA-seq datasets\nadapter adapter\n(post-basecalling)\ntrimming trimming\nPorechop [11] × × ×\nPorechop ABI [12] × × ×\nPychopper [10] × × ×\nDorado [13] ✓ × ×\nDeepChopper (This study) ✓ ✓ ✓\nNote: ✓indicatesthetoolsupportsthisfunctionality;×indicatesthetooldoesnotsupportthisfunctionality.\nWe hope these clarifications andnew data address the reviewer’s concerns regarding the novelty\nand benchmarking of DeepChopper.\nR2.Q3 The training data were created by manually inserting adapters into reads and identifying\nadapter regions using minimap2 soft-clipping. This process assumes that all 3’ soft-clipped regions\n9\ncorrespond to adapter sequences, which may not be true. Furthermore, chimeric reads were arti-\nficially generated, which does not necessarily reflect real ONT sequencing artifacts. The authors\nshould validate their training data approach by analyzing real dRNA-seq datasets to determine\nwhether internal adapters appear at similar frequencies and distributions. In addition, the authors\nusedminimap2(along-readaligner)togeneratetruetrainingdata. Whydon’tusealignment-based\nmethod directly? How the genomic language model will be better than alignment-based methods?\nBasically, all the benchmarks (e.g. counting chimeric reads, etc.) were all based on alignment\nresults. This needs to be justified.\nResponse We thank the reviewer for the detailed comments and the opportunity to clarify our\ntraining data generation methodology and the rationale behind our modeling approach.\n1. Refinement in Training Data Generation Using Biological Context\nThe reviewer notes that our training data were created by manually inserting adapters and\nidentifying adapter regions using minimap2 soft-clipping, which may not fully capture the\ncomplexity of real dRNA-seq artifacts. We appreciate this concern and would like to clarify\nthat we did not assume all 3’ soft-clipped regions to be adapter sequences. Instead,\nour method incorporates a biologically informed refinement:\nWe specifically anchored on polyA tails—a reliable indicator of transcript termination in\ndRNA-seq—to guide the identification of downstream adapter sequences. Only soft-clipped\nregions of low base-quality that followed a polyA stretch were considered candidate adapter\nsegments. This strategy substantially reduces the risk of falsely labeling endogenous soft-\nclipped sequences as adapter-derived, ensuring higher-quality training labels.\nTo support this strategy, we provide an example from real dRNA-seq data (Response Fig-\nure 5). In this example, a read corresponding to the GAS5 gene does not terminate at the\nexpected polyA region (highlighted in red) but continues with a sequence segment of notably\nlower base quality (labeled “Low base quality region”). These low-quality regions extend into\nthe soft-clipped part of the alignment, strongly indicating the presence of adapter-containing\nchimera artifacts. The figure clearly demonstrates how these artifacts display a consistent se-\nquence pattern—well-aligned regions followed by polyA stretches, which are then followed by\npoorly aligned, low-quality segments (shown in the gray shaded area in Response Figure 5)\nthat correspond to the adapter sequences and subsequent incorrectly captured fragments.\nThis pattern recognition approach significantly reduces the risk of misclassifying non-adapter\nregions as adapters and ensures our training data more accurately reflects the biological con-\ntext of direct RNA sequencing.\nJunction\nLow base\nquality region\n4 5\nReads\n5 5\nRefSeq\npolyA GAS5\nResponse Figure 5 Visualization of adapter-containing chimeric reads in dRNA-seq\nof the VCaP cell line. This alignment visualization shows characteristic features of chimeric\nartifacts in dRNA-seq data. Individual reads (horizontal lines) display a consistent pattern:\nwell-aligned sequences (colored bases) followed by internal polyA stretches (labeled “polyA”),\nwhich are immediately followed by regions of significantly lower base quality (labeled “Low base\nquality region”). These low-quality regions, extending into the soft-clipped portions (gray shaded\narea), represent adapter sequences and incorrectly captured fragments. This distinctive pattern—\ninternal polyA followed by low-quality regions—provides a reliable signature for discriminating\ntechnical artifacts from genuine biological chimeras.\n2. Justification for Using a Genomic Language Model Over Alignment-Based Meth-\nods\nThe reviewer also questions why alignment-based methods (e.g., minimap2) were not used\ndirectly for detecting chimeric artifacts, especially since alignment results were employed in\nbenchmarking.\nWe clarify that while alignment is useful for initial labeling and benchmarking, it is\nnot sufficient for accurate artifact detection. This is because:\n(a) Not all chimeric alignments are artifacts. Some represent biologically meaningful\nevents, such as trans-splicing or gene fusion events.\n(b) Alignment-based tools lack the context to distinguish artifact from biology.\nWithout modeling sequence composition, adapter motifs, or base quality patterns, tra-\nditional aligners cannot differentiate true gene fusions from chimera artifacts.\n(c) Ourgenomiclanguagemodelcapturesthelatentpatternsandstatisticalprop-\nerties of genuine transcripts versus synthetic chimeric sequences, including nucleotide\ndistribution, quality scores, and adapter motifs.\n(d) ThismodelingapproachenablesDeepChoppertoselectivelyfilterartificialchimeras\nwhile preserving biologically relevant reads, whichiscrucialinaccuratetranscript\nannotationandminimizingfalse-positivegenefusioncalls(seeFig. 3inthemanuscript).\nThus, while alignment was used in training and validation, our approach offers a learned,\nsequence-awarealternativethatiscapableofgeneralizingbeyondrule-basedalignmentheuris-\ntics.\nanchored strategy in the Methods: Training Data Preparation section.\nR2.Q4 DeepChopper classifies bases as adapters if the probability score is greater than 0.5, but\nthe manuscript does not justify this threshold. If probability scores are clustered around 0.5, the\nclassification may be unreliable. The authors should provide a distribution of probability scores\nand assess whether different cutoffs affect performance. An ROC curve or precision-recall analysis\nwould help determine the optimal threshold for adapter detection.\nResponse Wethankthereviewerforpointingouttheambiguityregardingtheclassificationthresh-\nold used by DeepChopper. We agree that the description in the original manuscript—specifically\nthe reference to a fixed threshold of 0.5—may have led to confusion.\nTo clarify, DeepChopper does not use a fixed threshold of 0.5 for classification.\nInstead, it employs a softmax-based multi-class classification approach, where the model outputs\nper-base probabilities for two classes: adapter and non-adapter. During inference, classification is\nperformed by selecting the class with the higher probability at each nucleotide position, i.e., a base\nis assigned to the adapter class if P(y = adapter) > P(y = non-adapter ), and vice versa. This\ni i\napproach inherently avoids reliance on a fixed threshold and ensures consistent interpretation of\nprobabilistic outputs, especially in the context of mutually exclusive class labels.\nWe have updated the manuscript to correct this discrepancy and provide a more accurate\ndescription of the classification head. Specifically, we have revised the relevant sentence in the\nLanguage Model Architecture subsection of the Methods to reflect the use of softmax rather than\nsigmoid, and to clarify the decision rule applied during inference.\nIn response to the reviewer’s suggestion, we have also performed additional analyses to char-\nacterize the model’s prediction confidence and assess the reliability of its classifications. We now\ninclude a new figure (Response Figure 6) that visualizes the distribution of predicted probabilities\nfor the adapter and non-adapter classes on a held-out test set (N = 60,000 reads). These analyses\nshow that:\n1. For true adapter bases (Response Figure 6a), the model assigns high adapter probabilities\n(mostly > 0.8), and correspondingly low non-adapter probabilities.\n2. For true non-adapter bases (Response Figure 6b), the model confidently predicts high non-\nadapter probabilities (> 0.8), with low adapter probabilities (< 0.2).\n3. The overall probability distributions are strongly bimodal, indicating decisive predictions and\nminimal ambiguity between classes.\nThis sharp separation in probability space demonstrates robust feature learning and general-\nization.\nWe sincerely appreciate the reviewer’s valuable feedback, which has helped us improve both the\nclarity and completeness of our manuscript.\n12\na b\n3\n2\n1\n0\n0.0 0.2 0.4 0.6 0.8 1.0\nProbability\ntnuoC\nGround Truth: Adapter\n1e6\nProb of Adapter\n6\n4\n2\n0\n0.0 0.2 0.4 0.6 0.8 1.0\nProbability\ntnuoC\nGround Truth: Non-Adapter\n1e7\nProb of Adapter\nProb of Non-Adapter\nResponse Figure 6 (ManuscriptExtendedDataFig. 2)Prediction probability distributions\nof DeepChopper for the held-out test dataset (N = 60,000). (a) Distribution of predic-\ntion probabilities for sequences with ground truth adapter classification. Red bars represent the\nprobability of adapter prediction, while gray bars show the probability of non-adapter prediction.\nThe count (y-axis) is shown in millions of sequences (106 scale). (b) Distribution of prediction\nprobabilities for sequences with ground truth non-adapter classification. Red bars indicate the\nprobability of adapter prediction, while gray bars show the probability of non-adapter prediction.\nThe count (y-axis) is shown in tens of millions of sequences (107 scale). Both distributions demon-\nstrate strong polarization toward correct classification probabilities, indicating the model’s high\nconfidence in distinguishing between adapter and non-adapter sequences.\nR2.Q5 The manuscript introduces a Quality Block to incorporate sequencing base quality scores\nbut it does not clearly explain how this improves model performance. Similarly, the sliding window\nrefinement approach lacks detailed methodology, including how window size and majority voting\ncriteria were selected. The authors should provide an ablation study testing the impact of the\nQuality Block and a sensitivity analysis for the sliding window parameters.\nResponse We appreciate the reviewer’s thoughtful comments and the opportunity to clarify the\nmethodology and impact of both the Quality Block and the sliding window refinement procedure.\nWe have revised the manuscript to include additional details and supporting analyses, which are\nnow highlighted in the updated version.\nQuality Block Contribution The Quality Block is designed to incorporate base-level sequenc-\ning quality scores into DeepChopper’s prediction pipeline, enhancing its robustness in noisy regions\nof the input sequence—particularly near ambiguous adapter boundaries, which exhibit low overall\nquality (See Response Figure 5).\nIn response to the reviewer’s suggestion, we performed an ablation study using an independent\ndataset (N = 100,000 reads). As shown in Response Table 2, incorporating the Quality Block\nimproved the F1 score from 0.97 to 0.99, indicating a notable improvement in classification per-\nformance attributable to this component. This enhancement reflects the ability of quality signals\nto disambiguate difficult predictions, particularly in low-confidence regions. We have added the\ntraining setup to the revised Methods section and included the results of this ablation study as\nExtended Data Table 2 in the revised manuscript.\nModel Configuration F1 Score\nWith Quality Block 0.99\nWithout Quality Block 0.97\nSliding Window Refinement To further improve prediction smoothness and reduce isolated\nmisclassifications, DeepChopper applies a post-hoc sliding window refinement step based on major-\nity voting. We have now expanded the Methods section to detail both the rationale and parameter\nselection strategy.\nWe evaluated multiple window sizes (ranging from 11 to 51 nucleotides) on VCaP, balancing\ndetection sensitivity with read continuity (See R1.Q1). As shown in Response Figure 1a, window\nsizes of 11 and 21 performed similarly in terms of raw prediction support (47.5% and 47.4%,\nrespectively). However, as illustrated in Response Figure 1b, a window size of 21 significantly\nreduced the number of read fragments introduced by trimming, thereby preserving better overall\nsequenceintegritywhilemaintainingsensitivity. Basedonthisanalysis,windowsize21wasselected\nas the default.\nTo ensure flexibility for different datasets and sequencing characteristics, we have implemented\nthe –smooth-window parameter in the command-line interface, allowing users to customize the\nwindow size as needed. Detailed documentation for this feature is available in DeepChopper’s\nonline user guide and in our response to Reviewer 1, Comment 6 (See R1.Q6).\nMajority Voting Mechanism The sliding window refinement applies a straightforward major-\nity voting strategy over a centered window of size W, defined as follows:\n(cid:40) 1 if (cid:80)i+k p > W\ny = j=i−k j 2\ni\n0 otherwise\nHere,y isthefinalrefinedpredictionatnucleotidepositioni,k = W−1 isthewindowradius,andp\ni 2 j\nrepresents the initial binary prediction at position at j, where 1 indicates adapter and 0 indicates\nnon-adapter. This approach smooths the basewise predictions by consolidating local prediction\ncontext, reducing isolated false positives and negatives.\nWe thank the reviewer for prompting these improvements, which have enhanced the clarity\nand rigor of our methodology. The relevant sections in the manuscript have been updated and\nhighlighted accordingly.\nR2.Q6 The manuscript states that DeepChopper has 4.6 million parameters, suggesting it is com-\nputationally efficient but it does not provide any runtime or memory benchmarks. There is no\ninformation on how long DeepChopper takes to process a dataset, what GPU/CPU resources are\nrequired, or how it scales with large sequencing datasets. A computational efficiency evaluation is\nnecessary to determine whether DeepChopper is practical for large-scale sequencing projects.\nResponse We thank the reviewer for this important suggestion. To address the concern regarding\ncomputationalefficiency, wehaveconductedacomprehensivebenchmarkinganalysisandexpanded\nthe relevant documentation in the revised manuscript.\ndatasetsrangingfrom0.1millionto9millionreads. Forthefull9million-readVCaPdataset,Deep-\nChopper completes processing in approximately 5 hours. We also provide a detailed breakdown\nof resource requirements for each stage of the pipeline:\n1. FASTQ Conversion: 25 minutes, peak 40 GB CPU memory\n2. Adapter Prediction: 3 hours and 42 minutes, peak 33 GB GPU memory and 71 GB\nCPU memory\n3. Post-processing and Sliding Window Refinement: 55 minutes, peak 41 GB CPU\nmemory\nThese benchmarks were performed on a system with 16 CPU cores and two NVIDIA A100\nGPUs. The hardware specifications and software environment have been added to the Methods\nsection (highlighted) for full transparency.\nThis analysis demonstrates that DeepChopper scales efficiently to large datasets, with runtime\nandmemoryusageremainingpracticalforlarge-scalesequencingapplications. Thefullbenchmark-\ning results, including scalability trends, are also discussed in our response to Reviewer 1, Comment\n5 (See R1.Q5).\nR2.Q7 The validation dRNA-seq data were processed using Dorado with the “–no-trim” option,\nwhich preserves adapters and likely increases the observed chimeric read count. This choice could\nartificially inflate the problem DeepChopper aims to solve, making the model appear more effective\nthan it actually is. The authors should repeat their analysis using Dorado with adapter trimming\nenabled to assess whether DeepChopper still provides a significant advantage.\nResponse We thank the reviewer for raising this important concern. The potential for inflated\nchimeric read counts due to the use of Dorado with the --no-trim option is well taken, and we\nappreciate the opportunity to clarify and further substantiate DeepChopper’s effectiveness.\nIn response to the reviewer’s suggestion, we extended our benchmarking and downstream anal-\nyses by incorporating results from Dorado with adapter trimming enabled, both alone and in com-\nbination with DeepChopper. In our analysis of VCaP dRNA-seq data, we observed that Dorado\nalone—regardless of whether trimming was applied—was insufficient to eliminate spurious chimeric\nalignments. In contrast, DeepChopper substantially reduced unsupported chimeric events, whether\napplied independently or following Dorado trimming, as validated by orthogonal cDNA sequencing\n(Response Figure 7).\n15\nNo support\n80\ncDNA\n60\n40\n20\no o o o\nra ra ra ra\nd d d d\no o o o\nw w D w D w\n/o\nT rim\nith\nT rim\ne\ne p C\nh\no\ni\np\nth\nT rim\n+\ne\ne p C\nh\no\n/\np\no\nT rim\n+\np p\ne e\nr r\ntnemngila\nciremihc\nfo\n.oN\n000,1\nResponse Figure 7 (Manuscript Fig. 2d) Chimeric alignments (in thousands) for VCaP\ndRNA-seq reads processed by Dorado with and without adapter trimming, Dorado\nwith adapter trimming followed by DeepChopper, and DeepChopper. DeepChopper-\ninvolved methods including Dorado with adapter trimming followed by DeepChopper\nand DeepChopper greatly reduce chimeric alignments not supported by direct cDNA\nsequencing.\nTo further generalize these findings, we performed an expanded analysis across six additional\nhuman cell lines (A549, HCT116, HepG2, K562, MCF7, and WTC11) as well as the F121-9 mouse\nembryonicstemcellline. AsshowninResponseFigure8,ResponseFigure9,andResponseFigure3,\nDeepChopper consistently reduced chimeric artifacts across all cases, including when applied after\nDorado’s adapter trimming. These results reinforce that DeepChopper offers distinct and additive\nbenefits beyond what is achieved through adapter trimming alone.\nD D o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im +\ntnemngila\nciremihc\nfo .oN\n000,1\nA549\n15\n12.5 10 7.5\n5\n2.5 0\nDorado with Trim\ntnemngila\nciremihc\nfo .oN\n000,1\nHCT116\n50\n40 30\n20\n10 0\ntnemngila\nciremihc\nfo .oN\n000,1\nHepG2\n20\n15 10\n5 0\ntnemngila\nciremihc\nfo .oN\n000,1\nK562\n25\n20 15\n10\n5 0\ntnemngila\nciremihc\nfo .oN\n000,1\nMCF7\n20\n15 10\n5 0\nDorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im +\nResponse Figure 8 (Manuscript Fig. 2g) The number of chimeric alignments (in thou-\nsands) for A549, HCT116, HepG2, K562, and MCF7 cell lines processed by Dorado\nwith adapter trimming, Dorado with adapter trimming followed by DeepChopper\nand DeepChopper. DeepChopper-involved methods including Dorado with adapter\ntrimming followed by DeepChopper and DeepChopper consistently reduce chimeric\nalignments not supported by cDNA sequencing across all cell lines.\nUnsupported Unsupported Unsupported Unsupported Unsupported\nSupported Supported Supported Supported Supported\nDorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im + Dorado with Tr D D im o e r e a p d C o h w o i p th p e T r ri D m D o e + r e a p d C o h w o / p o p T e r r im +\ntnemngila\nciremihc\nfo .oN\n000,1\nONT PCR cDNA\n15\n12.5\n10\n7.5\n5\n2.5\n0\ntnemngila\nciremihc\nfo .oN\n000,1\nONT CapTrap\n15\n12.5\n10\n7.5\n5\n2.5\n0\ntnemngila\nciremihc\nfo .oN\n000,1\nONT R2C2\n15\n12.5\n10\n7.5\n5\n2.5\n0\ntnemngila\nciremihc\nfo .oN\n000,1\nPacBio cDNA\n15\n12.5\n10\n7.5\n5\n2.5\n0\ntnemngila\nciremihc\nfo .oN\n000,1\nPacBio CapTrap\n15\n12.5\n10\n7.5\n5\n2.5\nWTC11 cell line, evaluated for support using additional ONT and PacBio sequencing\ndata with different protocols. DeepChopper-involved methods including Dorado with\nadapter trimming followed by DeepChopper and DeepChopper reduce unsupported\nchimeric alignments across all methods compared to Dorado with adapter trimming.\nWealsoevaluatedtheimpactofDeepChopperonisoform-leveltranscriptdetectionusingVCaP\ncells. AsillustratedinResponseFigure10,applyingDeepChopperfollowingDoradotrimmingledto\na greater number of detected transcripts across multiple isoform classification categories compared\nto Dorado trimming alone. This supports the complementary role of DeepChopper in enhancing\ndownstream transcriptomic interpretation, even when applied after standard trimming procedures.\nKnown\nNovel\nReference\nFull splice match (FSM)\nIncomplete splice match (ISM)\nNovel in catalog (NIC)\nNovel not in catalog (NNC)\nMonoexonic\nOther\nGenic\nstpircsnart\nfo\n.oN\n000,1\nAdatper trimmed by\nDorado with Trim + Deepchopper\nDorado w/o Trim + Deepchopper\n80\n60\n40\n20\nTranscript category\nResponse Figure 10 (Manuscript Fig. 3b) The bar plot shows the number of tran-\nscripts (in thousands) across different isoform classification categories. DeepChopper-\nprocessed reads result in a higher number of transcripts compared to Dorado-trimmed\nreads. The inset details the isoform classification scheme.\nFurthermore, we repeated our gene fusion analysis using Dorado-trimmed reads followed by\nDeepChopper. As shown in Response Figure 11, fusion detection was markedly improved in both\nDeepChopper-based pipelines—with or without prior trimming—demonstrating greater concor-\ndance with gene fusions identified from matched short-read RNA-seq data. This underscores Deep-\nChopper’s ability to eliminate spurious fusion calls while preserving true, biologically meaningful\nfusion events.\n18\n500\nNo support\n200\n100\no o o\nra ra ra\nd o D d o D d o\nw ith T rim e e p C h w o i p th p e T r rim + e e p C h w o / p o p T e r r im +\nstneve\nnoisuf\neneg\nfo\n.oN\nResponse Figure 11 (Manuscript Fig. 3c) Detected gene fusions from Dorado adapter-\ntrimmed reads and DeepChopper-processed reads using Dorado with adapter\ntrimming followed by DeepChopper and DeepChopper. Gene fusions identified from short-\nread RNA-seq were used to validate fusion events detected from dRNA-seq.\nCollectively, these findings confirm that DeepChopper’s effectiveness is not contingent upon\nthe inclusion of untrimmed adapter sequences. Its capacity to reduce chimeric artifacts, enhance\nisoform detection, and improve gene fusion calling remains robust across diverse datasets and\npreprocessing workflows. We have incorporated these updated analyses and figure references into\nthe revised manuscript to directly address this concern.\n[1] Yanrong Ji et al. “DNABERT: Pre-Trained Bidirectional Encoder Representations from\nTransformers Model for DNA-language in Genome”. In: Bioinformatics 37.15 (Aug. 9, 2021).\nEd.byJanetKelso,pp.2112–2120.issn:1367-4803,1367-4811.doi:10.1093/bioinformatics/\nbtab083.\n[2] Zhihan Zhou et al. DNABERT-2: Efficient Foundation Model and Benchmark for Multi-\nSpecies Genome.2023.doi:10.48550/arXiv.2306.15006.arXiv:2306.15006[cs, q-bio].\nPre-published.\n[3] Hugo Dalla-Torre et al. “Nucleotide Transformer: building and evaluating robust foundation\nmodels for human genomics”. In: Nature Methods (2024), pp. 1–11.\n[4] Eric Nguyen et al. “Hyenadna: Long-range genomic sequence modeling at single nucleotide\nresolution”. In: Advances in neural information processing systems 36 (2024).\n[5] Inˆes Lopes et al. “Gene size matters: an analysis of gene length in the human genome”. In:\nFrontiers in Genetics 12 (2021), p. 559998.\n[6] Rachael E Workman et al. “Nanopore native RNA sequencing of a human poly (A) tran-\nscriptome”. In: Nature methods 16.12 (2019), pp. 1297–1305.\n[7] Charlotte Hewel et al. “Direct RNA sequencing enables improved transcriptome assessment\nandtrackingofRNAmodificationsformedicalapplications”.In:bioRxiv (2024),pp.2024–07.\n[8] Martin A Smith et al. “Molecular barcoding of native RNAs using nanopore sequencing and\ndeep learning”. In: Genome research 30.9 (2020), pp. 1345–1353.\n[9] GonzaloBenegasetal.GenomicLanguageModels:OpportunitiesandChallenges.2024.arXiv:\n2407.11435 [q-bio.GN]. url: https://arxiv.org/abs/2407.11435.\n[10] epi2me-labs/pychopper: cDNA read preprocessing. Github. url: https://github.com/\nepi2me-labs/pychopper.\n[11] Ryan R. Wick et al. “Completing bacterial genome assemblies with multiplex MinION se-\nquencing”. In: Microbial Genomics 3.10 (Oct. 2017). issn: 2057-5858. doi: 10.1099/mgen.\n0.000132. url: http://dx.doi.org/10.1099/mgen.0.000132.\n[12] Quentin Bonenfant, Laurent No´e, and H´el`ene Touzet. “Porechop ABI: discovering unknown\nadapters in Oxford Nanopore Technology sequencing reads for downstream trimming”. In:\nBioinformatics Advances 3.1 (2023), vbac085.\n[13] Oxford Nanopore PLC. Dorado. https://github.com/nanoporetech/dorado. 2023.\n20"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all the comments and concerns I raised in the previous review. I recommend that the paper be\npublished in its current form.", "reviewer_3": "Summary:\nThe manuscript describes genomic language model based method for detecting chimeric reads in direct RNA sequencing.\nOverall, the manuscript is well written and thoughtfully organised, where the comments are also well addressed. I have a few\nquestions remain after reading the manuscript, which either arise from reading the manuscript or following up to previous\nquestion which I felt more can be done to address those questions.\nMajor comments (more general):\nThe manuscript focuses on direct RNA seq, I wonder how generalizable is the approach to cDNA seq data, would a lot effort\nbe needed? It would be good if authors could discuss for this application given there are a lot cases where Nanopore cDNA\nsamples are generated instead.\nThe novelty of this approach, is the application of genomic language models, I agree. However, the advantage of this model\nvs conventional non-large language model is not discussed enough for why this approach should be favored. One point\nrelated is the testing data being too easy, which is shown as clear bimodal distribution as presented in response figure 6,\nany challenging scenarios that could benefit from deepchopper but not other methods?\n(more specific)\nLine 059-064: The chimera artifacts in dRNA-seq resulted from ligation (biases? I assume) or software missing open pore\nsignal. The citation seems to describe more in a multiplexing context. I wonder is there any paper discussing this for regular\nONT dRNA-seq instead? Also, the part software missing open pore signals is from paper in 2020, specifically referring to\nMinKNOW, which has been outdated, have the authors checked whether this is still a problem with the recent dRNA-seq?\nRelated to the handling of long-reads, as questioned by Reviewer 1, 32kb misses a few genes, for example SMAD2, LTRT,\nTTN (TTN actually is longer than 32kb?), etc? How are these genes dealt with then? And what impact will happen to these\ngenes? In terms of expression levels? The read length referred to citation in the response is rather outdated, and the\nannotation with respect is also quite old.\nFollowing Review 1 Q4 regarding RNA004, for practicality, it would be good if the authors could show fine-tuned results of\ndeep-chopper for RNA004 in terms of performance to be more relevant in direct RNA sequencing context.\nFollowing Reviewer 1 Q5 response, the authors demonstrated it in different data size, in terms of practicality, an data of size\n20/25 million would also be interested to be included for this guidance.\nFigure 1 c). Sliding window worth more explanation for better understanding. For example, from step 3 to step 4, the bottom\n3rd base is refined in step 3 from 1 to 0, however, in step 4, the refined based is not used. That means this refinement\nstrategy is always using initial prediction for refinement, what is the advantage and disadvantage of this?\nFollowing R2.Q2 response, why comparison results from response figure differ so much between deepchopper and other\nmethods.\nFollowing R2.Q3 respons for the first part related to validating performance of internal adapter detection in real data, I like the\nexample shown in Figure 5, which quite clearly show that this is a internal adapter, I wonder the sequences before the low\nbase-quality, is a real artefact or fusion reads, and in general, what is the prevalence of the presence of such internal adapter\nin this real dataset to get an idea of how big the issue is in real data? Related to the second question, as to the justification of\nusing a genomic language model over alignment-based methods, a comparison of the genomic language model vs\nalignment-based methods (not necessarily minimap2, but some methods build upon minimap2 alignment), for example, the\nrecently online paper Breakinator tool (Heinz, Jakob M., Matthew Meyerson, and Heng Li. \"Detecting Foldback Artifacts in\nLong Reads.\" bioRxiv (2025): 2025-07.) and flair-fusion (which aims to identify fusion by removing chimeric reads). It would\nworth discussion to highlight the advantage of using genomic language models in this context.\nFollowing up to R2 Q4, the response does not explain how different thresholds of this classification decision. In the context\nof 2 labels, the sum of the 2 adapter class probability would always be 1, that is, the classification of 1 label probability being\ngreater than the other label is still 0.5 threshold. Please clarify and state scenarios where the 2 probabilities does not sum to\n1, cause it’s hard to understand why in the case of 2 labels, the sum would not be 1. Related to this, response figure 6 is very\nclean indeed, I wonder is there some kind overfitting or testing data is too simple? Please elaborate for challenging\nscenarios."}, "author_response": "Point-by-point responses\nWe would like to thank the reviewers R1 and R3 for their thorough review of our . Please find\nour point-by-point response below.\nR1.General The authors have addressed all the comments and concerns I raised in the previous\nreview. I recommend that the paper be published in its current form.\nResponse We sincerely thank R1 for continued support and recommendation for publication. We\nappreciate the recognition of our revisions and the thoroughness of the review process.\nR3.General The describes genomic language model based method for detecting chimeric reads\nin direct RNA sequencing. Overall, the is well written and thoughtfully organised, where the\ncomments are also well addressed. I have a few questions remain after reading the , which either\narise from reading the or following up to previous question which I felt more can be done to address\nthose questions.\nResponse We thank R3 for their thoughtful and constructive feedback on our revised . We ap-\npreciate the recognition that the is well-written and well-organized, and that we have addressed\nprevious comments comprehensively. Below, we provide detailed responses to the remaining ques-\ntions, which we hope will fully address the reviewer’s concerns.\nR3.Q1 The focuses on direct RNA seq, I wonder how generalizable is the approach to cDNA seq\ndata, would a lot effort be needed? It would be good if authors could discuss for this application\ngiven there are a lot cases where Nanopore cDNA samples are generated instead.\nResponse We thank the reviewer for raising this important point regarding generalizability to\ncDNA-seq. We agree that cDNA-seq is widely used, and we have revised the Discussion to clarify\nplatform-specific considerations and future extensions.\n1. DeepChopper’s Design and dRNA-seq Chimeras\nDeepChopper,initscurrentimplementation,istailoredtodRNA-seqbecausethedominantchimera\nformationmechanismindRNA-seqproducesinternaladaptersequences,whichserveasamolec-\nular signature of non-biological chimeras. Across six human cell lines, mouse cell, and two ONT\nchemistries(ManuscriptFigures2–3,ExtendedDataFigures8-11,R3.Q9),weconsistentlyobserve\nthat dRNA-seq chimeras contain these internal adapters. This enables two key capabilities: (i) re-\nliable artifact identification, since internal adapter presence cannot arise from biological processes;\nand (ii) accurate sub-read recovery by splitting at adapter boundaries, which preserves bona fide\ntranscriptomic rearrangements [1] and increases usable read yield.\n2. Mechanistic Difference: cDNA-seq and Template Switching\nNanopore cDNA-seq produces a fundamentally different class of chimeras. These arise mainly from\nreverse-transcriptase template switching, in which chimeric junctions are formed by direct\ntemplate-to-template joining and therefore lack internal adapter sequences [2–6]. Prior studies,\nincluding Schulz et al. (2021), have shown that such RT-mediated artifacts can closely mimic\nbiological events—for example, putative exitron splicing events detected only in cDNA-seq but\nabsent in dRNA-seq—underscoring that these artifacts have distinct molecular signatures that\nDeepChopper’s adapter-based strategy is not designed to detect.\n1\n3. Potential for Adaptation:\nWhile DeepChopper as a detection-and-recovery tool is specific to dRNA-seq, the Ge-\nnomic Language Model (GLM) that forms the foundation of DeepChopper is general\nand not tied to adapter features. This GLM-based framework can, in principle, be trained to\nrecognizeRT-mediatedcDNA-seqartifactsbylearningtheirsequence-contextpatterns(e.g., micro-\nhomology, local sequence motifs associated with RT template switching). We are actively exploring\nthese directions. However, distinguishing RT artifacts from genuine biological events in cDNA-\nseq remains challenging because, unlike the definitive internal adapter signal left by artifacts in\ndRNA-seq, RT template switching creates chimeric molecules through direct template-to-template\njoining, thus leaving no molecular tag (e.g., an internal adapter) for easy identification. Current\ncDNA-focusedmethodsthereforerelyprimarilyonalignment-basedrules[7], andwecomparethese\napproaches with DeepChopper’s adapter-based strategy in Response R3.Q10.\nWe appreciate the reviewer’s suggestion and have expanded the Discussion section to clarify (i)\nwhy DeepChopper currently targets dRNA-seq, (ii) how the foundational GLM is extensible to\ncDNA-seq artifact modeling, and (iii) what challenges and methodological considerations arise for\nfuture applications to cDNA-seq.\nR3.Q2 The novelty of this approach, is the application of genomic language models, I agree.\nHowever, the advantage of this model vs conventional non-large language model is not discussed\nenough for why this approach should be favored. One point related is the testing data being too\neasy, whichisshownasclearbimodaldistributionaspresentedinresponsefigure6, anychallenging\nscenarios that could benefit from deepchopper but not other methods?\nResponse We thank the reviewer for this insightful question regarding the advantages of GLM\nand the perceived simplicity of the test data. We have revised the manuscript to clarify why\nGLMoffercapabilitiesbeyondconventionalapproachesandtohighlightchallengingscenarioswhere\nDeepChopper provides clear benefits.\n1. Why Genomic Language Models Offer Substantive Advantages\nDeepChopper’s core novelty is not only applying an GLM, but enabling a new class of artifact de-\ntectionthatcannotbeaddressedbyrule-basedoralignment-basedmethods(R3.Q8andR3.Q10).\nThese advantages arise from three technical properties:\n(a) Error-tolerant pattern learning for unknown or corrupted adapter sequences In\ndRNA-seq, internal adapters are heavily corrupted because (i) the chemical adapters for dRNA-seq\nare not publicly documented, and (ii) DNA adapters are mis-called by RNA-trained basecallers.\nConventionalmethodsrelyonexactmatching,k-mercounting,oralignmentheuristicsandtherefore\nfail when adapters are error-rich, truncated, or shifted.\nIn contrast, the GLM learns probabilistic sequence patterns even under high error conditions.\nThis ability is what enables DeepChopper to identify > 90% of artifacts in some datasets—even\nwhen the true adapter sequence is never provided. We show that per-base quality integration\nfurther boosts F1 to 0.99 (Manuscript Extended Data Table 3), demonstrating the model’s ability\nto leverage multi-modal contextual signals.\n2\n(b) Alignment-free detection eliminates blind spots of alignment-dependent methods\nAlignment-based artifact detection inherently fails in three challenging but common scenarios:\n1. Artifacts that map as single alignments (no chimeric signature; invisible to fusion/structural\nfilters) (Response Figure 1)\n2. Reads that fail to map due to adapter contamination, thus never reaching downstream filters\n(Response Figure 1)\n3. Artifacts in repetitive or structurally complex regions, where aligners generate ambiguous\nmappings\nDeepChopper operates directly on raw sequences, circumventing these limitations entirely. This\nallows recovery of sub-reads from contamination-heavy sequences that all alignment-dependent\nfilters would simply discard (R3.Q10).\n(c) Preserving genuine biological rearrangements that conventional methods mistak-\nenly remove Becauseconventionalmethodsevaluatealignmentpatterns(breakpoints,distances,\norientations), they cannot distinguish artifacts from bona fide biological chimeras, which often pro-\nduceidenticalmappingsignatures(R3.Q10). Thisleadstovaluablebiologicalreadsbeingremoved.\nEmpirically, this distinction is decisive:\n• Breakinator decreases cDNA-supported rearrangements (4.8%, Response R3.Q10)\n• DeepChopper increases support nearly ten-fold (5% → 47−49%) (Manuscript Figure 2)\n• Gene-fusion false positives fall by 89% while novel transcripts nearly double (Manuscript\nFigure 3)\nThese gains reflect a unique conceptual advantage: DeepChopper removes artifacts be-\ncause they contain adapter-like sequence patterns, not because they merely “look\nchimeric” after alignment.\n2. Addressing the Reviewer’s Concern: Is the Test Data Too Easy?\nThe bimodal distribution shown in Response Figure 6 reflects that some artifacts contain long\nadapter fragments, making them easily separable. However, our analyses also include multiple\nchallenging scenarios where conventional tools fail but DeepChopper succeeds:\n(a) Short, fragmented, or partially mis-called adapters Many internal adapters are only\n10–18 bp long and heavily corrupted (Manuscript Figure 2a, 2e, and 2f). Rule-based filters cannot\ndetect these; DeepChopper reliably does (R3.Q8 and R3.Q10).\n(b) Non-chimeric alignments with internal adapters Up to 33–45% of adapter-containing\nreads map as single alignments (Response Figure 1). No alignment-based tool can flag these, yet\nthey are effectively removed by DeepChopper (R3.Q10).\n3\n40\n30\n20\n10\nH\nC\nT116 K562\nHep\nG2\nM C\nF7\nsdaer\ngniniatnoc-retpada\nlanretni\nfo\ntnecreP\n)%(\nciremihc-non\nera\ntaht\nResponse Figure 1 (Manuscript Extended Data Figure 4) Percent of internal adapter-\ncontaining reads that are non-chimeric Percentage of internal adapter-containing reads that\ndo not produce chimeric alignments across five human cell lines (RNA002) processed by Dorado\nwith trim followed by DeepChopper. Between 33–45% of adapter-containing reads map as single\nalignments or fail to map, making them invisible to chimeric alignment-based artifact detection.\n(c) Complex transcripts with true rearrangements In genes with extensive alternative\nsplicing or rearranged exons, conventional methods over-filter and remove biologically valid events.\nDeepChopper preserves these while removing artifacts, providing a net increase in usable biological\nsignal. (R3.Q10)\n(d)High-error-rateorlow-qualitychemistrydatasets Weincludedtwosequencingchemistries\nwith different error profiles. DeepChopper maintains performance in high-error regimes where\nexact-matching methods fail entirely. (R3.Q8)\nThesescenariosillustratethattheGLM’advantagesaremostpronouncedinthehardestregions\nof the distribution, not the easy ones.\n3. Summary of Why This Approach Should Be Favored\nDeepChopper’s GLM foundation enables three capabilities that no conventional method can repli-\ncate:\n1. Detect error-corrupted internal adapters without prior knowledge of sequences\n2. Operate completely alignment-free, eliminating blind spots where alignment-based filters fail\n3. Distinguish artifacts from true biological rearrangements, increasing biological signal instead\nof diminishing it\na fundamentally different and more powerful solution to adapter-bridged chimera removal.\nR3.Q3 (more specific) Line 059-064: The chimera artifacts in dRNA-seq resulted from ligation\n(biases? I assume) or software missing open pore signal. The citation seems to describe more\nin a multiplexing context. I wonder is there any paper discussing this for regular ONT dRNA-\nseq instead? Also, the part software missing open pore signals is from paper in 2020, specifically\nreferring to MinKNOW, which has been outdated, have the authors checked whether this is still a\nproblem with the recent dRNA-seq?\nResponse We thank the reviewer for these important observations about citation currency and\ntechnology advances. The reviewer has identified a significant literature gap that our work ad-\ndresses.\n1. Are adapter-bridged chimeras documented for regular (non-multiplexed)\nONT dRNA-seq?\nThe reviewer is correct that prior publications describing ligation- or pore-related chimeras pri-\nmarily focus on multiplexing settings or earlier chemistries. To our knowledge, no peer-reviewed\nstudyhascharacterizedinternal-adapterchimerasinstandard, non-multiplexeddRNA-\nseq libraries. This gap motivated our work.\nOur analyses—performed on non-multiplexed samples, using current Dorado basecalling and\nboth RNA002 and RNA004 chemistries—show that internal-adapter artifacts are systematic and\nreproducible (Response Table 2):\n• RNA002: 0.67–1.62% of total reads contain internal adapters across six human cell lines\n• RNA002: 63–92% of chimeric reads contain internal adapters across six human cell lines\n• RNA004: prevalence decreases but persists (0.33% of total reads and 24% of chimeric reads\nin VCaP), still affecting tens of thousands of reads per run\nThus,eventhoughtheliteraturehasnotpreviouslyreportedthisphenomenoninnon-multiplexed\ndRNA-seq, ourstudyprovidesquantitativeevidencethatthesechimerasremainanintrinsicfeature\nof current ONT dRNA workflows.\n2. Are older citations still relevant, and do “open pore signal” artifacts persist?\nThe reviewer is correct that the cited MinKNOW-related discussion originated from 2020 and\npredates Dorado. We agree this required clarification, and we have revised the manuscript to avoid\nimplying that MinKNOW-specific behavior is responsible for the artifacts we observe today.\nImportantly, our results indicate:\n• The internal-adapter chimeras we detect persist with Dorado (v0.5.2) and modern RNA004\nchemistry.\n• These artifacts are not restricted to older Guppy/MinKNOW pipelines.\nOur study does not rely on the presence of “open pore signal” errors in current software. Instead,\nwe empirically identify internal-adapter junctions directly in the read sequences, independent of\nthe mechanism by which they arise.\nBecause no current ONT documentation provides a definitive molecular explanation for these\nchimeras, we frame the mechanistic statement more cautiously. We now state that:\n5\n• internal-adapter chimeras are empirically observed,\n• occur independently of multiplexing,\n• and persist even with the latest chemistry and basecalling.\nWe have revised the manuscript Introduction and Discussion sections to clarify the literature\ngap, emphasize validation with current technology (Dorado, RNA004), and detail the comprehen-\nsive validation framework.\nR3.Q4 Related to the handling of long-reads, as questioned by Reviewer 1, 32kb misses a few\ngenes, for example SMAD2, LTRT, TTN (TTN actually is longer than 32kb?), etc? How are these\ngenes dealt with then? And what impact will happen to these genes? In terms of expression levels?\nThe read length referred to citation in the response is rather outdated, and the annotation with\nrespect is also quite old.\nResponse We thank the reviewer for raising this important question regarding ultra-long tran-\nscripts and the 32 kb context window. We have expanded the manuscript to clarify transcriptome-\nwide coverage using current annotations and to quantify the practical impact on real dRNA-seq\ndata.\n1. Empirical Finding: reads > 32 kb are extremely rare in modern dRNA-seq\nAcross seven datasets spanning RNA002 and RNA004 chemistries, reads exceeding 32 kb represent\nonly 0-0.0032% of all reads (Response Table 1). Even VCaP RNA004—the largest dataset\nwith improved chemistry enabling longer reads—had only 379 reads out of 11.72 million (0.0032%)\nexceeding 32 kb. Mean read lengths range 683-1,148 bp; 99th percentile reaches only 2.6-4.7 kb\nacross all datasets.\nResponse Table 1 (Manuscript Extended Data Table 2) Read Length Statistics\nSample Reads (M) Mean (bp) P95 (bp) P99 (bp) Max (bp) Reads ≥32kb %\nA549 1.70 907 2,440 3,829 16,246 0 0\nMCF7 3.04 715 1,863 3,052 28,802 0 0\nHCT116 4.70 889 2,431 3,793 21,656 0 0\nK562 3.06 683 1,736 2,619 58,395 2 0\nHepG2 1.80 1,148 3,025 4,665 46,077 1 0\nVCaPRNA002 9.18 994 2,826 4,399 77,474 1 0\nVCaPRNA004 11.72 995 2,784 4,474 225,798 379 0.0032\nFor the remaining 99.9968% of reads within 32 kb, DeepChopper provides substantial benefits:\n62-91% artifact reduction, nearly two-fold transcript discovery, and 89% fewer false gene fusions\n(Manuscript Figures 2-3). Reads longer than 32 kb are passed through unchanged and remain\navailable for downstream analyses.\n2. Updatedtranscriptomeanalysisusingcurrentannotation(EnsemblGRCh38.115,\nJuly 2025)\nTo address the reviewer’s concern about outdated transcript length references, we reanalyzed the\nfull set of protein-coding transcripts in Ensembl GRCh38.115 (released July 2025):\n• Median: ∼2.7 kb\n• 95th percentile: ∼8.7 kb\n6\n• 99th percentile: ∼14 kb\n• Transcripts >32 kb: <0.03% of all protein-coding transcripts\nResponse Figure 2 (Manuscript Extended Data Figure 1) shows this distribution with 32 kb\nthreshold marked. Over 99.97% of transcripts fall below this limit.\n80\n60\n40\n20\n0\n0 10 20 30 40 50 60 70 80 90 100 110\nTranscript Length (kb)\n)0001\nx(\ntnuoC\n32kb\nResponse Figure 2 (Manuscript Extended Data Figure 1) Distribution of transcript length\nfor protein-coding genes. Analysis of all protein-coding transcripts from Ensembl GRCh38.115\n(releasedJuly2025) showsthat>99.97% oftranscriptsarebelow the32kbthreshold(markedwith\nvertical dashed line). The distribution is highly skewed toward shorter transcripts, with median\nlength of ∼2.7 kb.\n3. Handling of ultra-long genes (e.g., TTN, SMAD2)\nFor the very small number of transcripts exceeding 32 kb:\n• DeepChopper does not modify these reads to avoid truncation of biologically meaningful\nregions\n• Expression quantification remains unaffected, as these reads pass through intact and are\ncounted normally downstream.\n• Biological impact is negligible, given that such reads constitute < 0.003% of real data and\n< 0.03% of annotated transcripts.\n• Artifact considerations remain minimal, as ultra-long reads rarely carry adapter contamina-\ntion and do not appreciably influence transcript-level summaries.\n4. Future Directions\nWhile the current 32 kb window is computationally optimal for typical dRNA-seq workflows, the\nunderlying HyenaDNA architecture in DeepChopper can, in principle, scale to ultra-long contexts\n7\napproaching 1M tokens [8]. For specialized applications where complete modeling of exceptionally\nlong transcripts is critical—such as full-length analysis of genes like TTN—we plan to develop and\nrelease extended-context versions of DeepChopper. These future models will support substantially\nlarger context windows while retaining the algorithmic efficiencies required for transcriptome-scale\nprocessing.\nWe have revised the manuscript Results and Discussion sections to acknowledge this limitation\nusing current annotations (Ensembl GRCh38.115, July 2025) and added Extended Data Figure 1\nand Table 2.\nR3.Q5 Following Review 1 Q4 regarding RNA004, for practicality, it would be good if the authors\ncould show fine-tuned results of deep-chopper for RNA004 in terms of performance to be more\nrelevant in direct RNA sequencing context.\nResponse We thank the reviewer for this practical suggestion. We demonstrate strong RNA004\nperformance with both original and fine-tuned models.\n1. Performance with Current RNA004 Chemistry\nResponseFigure3(ManuscriptExtendedDataFigure10)comparessixconditionsusingcDNA-seq\nvalidation. Key findings:\nStrongzero-shotcross-chemistrygeneralization: TheoriginalRNA002-trainedmodelachieved\n21-27% artifact reduction on RNA004 data while preserving all ∼7,000 cDNA-supported biological\nrearrangements. This demonstrates robust generalization despite different chemistry-specific error\nprofiles—adapter-bridged chimera formation mechanisms remain consistent across chemistries.\nFine-tuning provides marginal improvement: RNA004-optimized fine-tuning achieved 24-\n32%artifactreduction—anadditional3-4%improvementoverzero-shotperformance. Bothmodels\npreserve identical cDNA-supported alignments (∼7,000), confirming selective removal of adapter-\nbridged artifacts while maintaining genuine biological events.\n8\n30\n25\n20\n15\n10\n5\no r a d o\nw\no r a d o\nw\nD e e o p r\nC\na d o\nw\nD e e o p r\nC\na d o\nw\nF in e o -\nt\nr\nu\na d o\nw\nF in e o -\nt\nr\nu\na d o\nw\nith\nT r im\n/o\nT r im\nh o\np p\ni\ne\nth\nr T r im\n+\nh o\np p\n/\ne\no\nr T r im\n+\nn e\nd D\nit\ne\nh\ne\nT\np\nr\nC\nim\nn\n+\nn e\nd D\n/o\ne\ne\nT\np\nr i\nC\nm\n+\nh h\no o\np p\np p\ne e\nr r\n)000,1\nx(\ntnuoC\nSupported by\nNo support\ncDNA\nResponse Figure 3 (Manuscript Extended Data Figure 10) RNA004 performance: original\nvs fine-tuned DeepChopper. ChimericalignmentsinVCaPRNA004acrosssixconditions. Red:\ncDNA-supported biological events; Grey: unsupported artifacts. Baseline (Dorado only): ∼29-30K\ntotal alignments. Original DeepChopper: 21-27% artifact reduction. Fine-tuned DeepChopper:\n24-32% artifact reduction. Both models preserve all cDNA-supported biological rearrangements.\nComplementary with Dorado trimming: Performance remains nearly identical regardless of\nDorado trim order, confirming complementary functions: Dorado removes 3’ end adapters during\nbasecalling; DeepChopper detects internal adapters in chimeric reads (R3.Q8 and R3.Q11).\n2. Practical Recommendations\nOriginal model (recommended for most users): Strong zero-shot performance (21-27% ar-\ntifact reduction) with no additional setup. Robust across both RNA002 and RNA004 chemistries.\nFine-tuned RNA004 model (specialized applications): Marginal benefit (3-4% additional\nimprovement). RecommendedforRNA004-exclusivelarge-scalestudiesrequiringmaximumartifact\nreduction.\nin documentation. Fine-tuning used 300,000 VCaP RNA004 reads (70/20/10 train/validation/test\nsplit). Same architecture and hyperparameters as RNA002 training, adapting to RNA004-specific\nerrorprofiles. Processingtime: ∼9hoursontwoNVIDIAA100GPUs. Wehaveaddedthisanalysis\nto Results section and Extended Data Figure 10, providing optimized models for both RNA002\nand RNA004 platforms.\nR3.Q6 Following Reviewer 1 Q5 response, the authors demonstrated it in different data size, in\nterms of practicality, an data of size 20/25 million would also be interested to be included for this\nguidance.\nResponse We thank the reviewer for this practical suggestion. We benchmarked DeepChopper\non 23 million reads—a merged dataset combining five cell lines (A549, HCT116, HepG2, K562,\nMCF7) from our validation framework (R3.Q3).\n1. Performance at 23M Scale\nResponse Figure 4 (Manuscript Extended Data Figure 6) shows benchmarks across five data sizes\n(0.1M, 0.5M, 1M, 9M, 23M reads) with triplicate measurements.\nProcessing time (23M reads, two NVIDIA A100 GPUs):\n• Total: 10.6 hours\n• FASTQ conversion: 23 min\n• Prediction: 8.5 hours\n• Post-processing: 1.7 hours\nNear-linear scaling with dataset size confirms efficient computational performance without al-\ngorithmicbottlenecks. Predictiondominatesatscale(80%), expectedforsequentialneuralnetwork\nprocessing of long reads.\nMemory usage (stable across all sizes):\n• CPU: 70-93 GB (prediction stage peak)\n• GPU: 34-56 GB (stage-dependent)\nStablememoryfootprintindicatesnofundamentalbarrierstoprocessingbeyond23Mreads—memory\ndetermined by batch size, not dataset size.\n10\n10\n8\n6\n4\n2\n0\n0.1M 0.5M 1M 9M 23M\nData Size\n)sruoh(\nemiT\nFASTQ Conversion\nPrediction\nPost-Processing 80\nTotal\n60\n40\n20\n0\n0.1M 0.5M 1M 9M 23M\nData Size\n)BG(\nyromeM\nFASTQ Conversion (CPU)\nPrediction (CPU)\nPost-Processing (CPU)\nPrediction (GPU)\nResponse Figure 4 (Manuscript Extended Data Figure 6) Computational performance\nmetrics across different data sizes. (a) Runtime analysis showing processing time require-\nments for different pipeline stages (FASTQ Conversion, Prediction, Post-Processing) and total\nruntime across five data sizes: subsampled VCaP datasets (0.1M, 0.5M, 1M reads), full VCaP\ndirect RNA sequencing (dRNA-seq)dataset(9Mreads),andmergedlarge-scaledataset(23Mreads\ncombining A549, HCT116, HepG2, K562, and MCF7). Runtime scales near-linearly with data size.\nAs data size increases, prediction time becomes the dominant component, requiring approximately\n5 hours for the 9M dataset and 10.6 hours for the 23M dataset. (b) Memory usage comparison\nbetween CPU and GPU implementations across the same data sizes. The prediction stage shows\nconsistently higher memory requirements. CPU memory usage ranges from 70-93 GB and GPU\nmemory from 34-56 GB across larger datasets, with stable memory footprint indicating no funda-\nmental barriers to processing substantially larger datasets. All measurements include error bars\nrepresenting standard deviation from three technical replicates.\nThe biological benefits—62-91% artifact reduction enabling accurate transcript annotation and\ngene fusion detection ( R3.Q2)—justify this computational investment for high-quality transcrip-\ntome profiling.\nWe have added this 23M benchmark to the Results section and updated Extended Data Figure\n6, providing concrete performance expectations for large-scale studies.\nR3.Q7 Figure 1 c). Sliding window worth more explanation for better understanding. For exam-\nple, from step 3 to step 4, the bottom 3rd base is refined in step 3 from 1 to 0, however, in step 4,\nthe refined based is not used. That means this refinement strategy is always using initial prediction\nfor refinement, what is the advantage and disadvantage of this?\nResponse We thank the reviewer for carefully examining the refinement strategy in Figure 1c.\nTherevieweriscorrectthattherefinementprocedurealwaysusestheinitial predictionsratherthan\nthe refined values from previous steps. This behavior is intentional and reflects both biological and\ncomputational design requirements.\n1. Rationale: Biological Constraints Require Non-Iterative Refinement\nDeepChopper’s core task is not simply to classify reads as chimera artifact or non-artifact, but\nto precisely localize internal adapter boundaries so that sub-reads can be recovered with\n11\nsingle-nucleotide accuracy. Adapter boundary precision is essential because:\n• A 1–2 nt boundary shift disrupts reading frames\n• Mislocalized boundaries truncate or distort biological exons\n• Downstream transcript reconstruction becomes invalid\nTo satisfy these constraints, we designed a refinement strategy that evaluates each position inde-\npendently using a sliding window of the original model predictions, not sequentially refined\nvalues. This avoids the risk that early refinement errors accumulate and propagate across the read.\n2. How the Sliding Window Operates\nFor each base at position i, the refinement step uses:\np ,...,p ,...,p\ni−k i i+k\nwhere p are the initial model probabilities (default window size = 21 bp).\nj\nAs the window slides from step 3 to step 4:\n• The window shifts by one nucleotide\n• The refined value from step 3 is not carried forward\n• Position i+1 is re-evaluated solely from the initial predictions\nThus, each refined prediction is computed from a fixed reference distribution, not influenced by\nprevious refinement decisions.\n3. Advantages of This Non-Iterative Refinement\nPrevents error cascades. If one position is mis-refined, that error does not contaminate sub-\nsequent positions. This isolation is essential for accurate boundary detection.\nPreserves single-nucleotide accuracy. Independent evaluation ensures that each base reflects\nlocal evidence only—critical for adapter boundary calls where even single-nucleotide shifts compro-\nmise recovered sub-reads.\nEfficient and parallelizable. Because positions are independent, the refinement layer can be\ncomputed in parallel across all bases, enabling DeepChopper to scale to tens of millions of reads\n(see R3.Q6).\n4. Trade-offs and Mitigation\nTrade-off: Independent refinement may leave more variability in noisy regions compared with\niterative smoothing approaches.\nMitigation strategies used in DeepChopper:\n• Quality-aware predictions (Quality Block; R3.Q2) increase robustness and raise F1 from\n0.97 to 0.99.\n• Length-filteringremovesimplausiblyshortfragments(defaultminimum=20bp;R3.Q11).\n• User-adjustable window size allows fine-tuning for different datasets or error profiles.\n12\n5. Summary\nUsing only the initial predictions during refinement is a deliberate design choice that ensures (i)\nerror isolation, (ii) single-nucleotide precision critical for adapter boundary detection, and (iii)\nscalable, parallel computation. We have clarified this strategy in the revised Methods section.\nR3.Q8 Following R2.Q2 response, why comparison results from response figure differ so much\nbetween deepchopper and other methods.\nResponse We thank the reviewer for this important question. The dramatic performance differ-\nences reflect fundamental capability gaps—existing tools were designed for different problems and\nlack internal adapter detection functionality.\n1. The Unsolved Problem: Internal Adapter Detection\nOur validation confirms that internal adapter detection in dRNA-seq was an unsolved computa-\ntionalproblempriortoDeepChopper. Onheld-outtestdata(60,000reads: 90%containingadapters\nsplit equally between 3’ end and internal positions, 10% negative examples without adapters, eval-\nuated at single-nucleotide resolution), existing tools achieved negligible adapter detection perfor-\nmance (Manuscript Extended Data Table 1, Extended Data Figure 2) while DeepChopper achieved\nF1 >0.99. This reflects capability gaps, not tool quality—each tool excels at its intended ap-\nplication, but none were designed for internal adapter detection in dRNA-seq.\nReal-world validation across six human cell lines, mouse cells, and two chemistries ( R3.Q3)\nconfirms this capability gap: adapter-bridged chimeras persist when processed with existing tools,\nrequiring DeepChopper’s specialized approach for detection and recovery.\n2. Why Existing Tools Cannot Detect Internal Adapters\nDorado (ONT basecaller): Excels at 3’ end adapter trimming during basecalling but can-\nnot detect internal adapters. Basecalling proceeds sequentially 5’→3’; 3’ end detection identifies\nend-of-molecule (open pore signal), but internal adapters appear as continuous sequence without\ndistinguishing signals. Our validation (R3.Q3, R3.Q5, and R3.Q11) confirms adapter-bridged\nchimeras persist in RNA002 and RNA004 when processed with Dorado—3’ end trimming does not\naddress internal adapters.\nPychopper: Designed exclusively for cDNA-seq strand-switching primers (SSP, VNP) absent\nin dRNA-seq [9]. Detects only 3’ end primers for orientation—no internal detection capability.\nPerforms excellently for intended cDNA applications.\nPorechop: Developed2017,abandoned2018[10]. Attemptsinternaldetectionviaexactsequence\nmatching against predefined database. Fails because: (1) database lacks dRNA adapter sequences;\n(2)exactmatchingcannottoleratesequencingerrors; (3)DNAadaptersmis-calledbyRNA-trained\nbasecallers corrupt exact matches.\nPorechop ABI: Usesk-merfrequencyanalysistoinferadapters[11]. Failsbecause: (1)sequenc-\ningerrorscorruptk-merpatterns; (2)internaladaptersappearatlowfrequency—insufficientsignal\nfor k-mer detection.\n13\n3. Why DeepChopper Succeeds: Genomic Language Model Advantages\nAs detailed in R3.Q2, DeepChopper’s success is rooted in the unique capabilities of the Genomic\nLanguage Model (GLM) architecture, which specifically addresses the failure points listed above:\n• Error-Tolerant Pattern Learning: Thepre-trainedGLMlearnsprobabilistic sequence\ncharacteristics that distinguish biological RNA from adapter artifacts. This capability\ntolerates sequencing errors and basecaller miscalls that cripple exact-matching methods.\n• Alignment-Free Operation: DeepChopperworksdirectlyontherawbase-calledsequence,\neliminating systematic biases. This is critical because not all internal artifacts produce\nchimeric alignments (some map as single alignments or fail to map entirely) and are thus\ninvisible to alignment-based detection methods.\n• Long-Range Context: The DeepChopper’s 32K token window allows it to scan complete\ntranscripts (up to 32 kb) to detect adapters at any variable internal position, unlike local\nheuristics.\n• Preservation of Genuine Biology: By using the adapter’s definitive presence as the\nmarker, DeepChopper can selectively remove only artifacts while preserving genuine biologi-\ncal rearrangements (e.g., trans-splicing), a distinction rule-based alignment methods cannot\nmake. This distinction led to DeepChopper increasing cDNA support from 5% to 47–49%\nacross datasets.\nIn summary, the performance disparity reflects the fact that DeepChopper is the first tool\ndesigned with the advanced computational capabilities necessary to solve the unique technical\nchallenges of internal adapter detection in dRNA-seq. We have expanded the Discussion section to\nclarify these points.\nR3.Q9 Following R2.Q3 respons for the first part related to validating performance of internal\nadapter detection in real data, I like the example shown in Figure 5, which quite clearly show that\nthis is a internal adapter, I wonder the sequences before the low base-quality, is a real artefact or\nfusion reads, and in general, what is the prevalence of the presence of such internal adapter in this\nreal dataset to get an idea of how big the issue is in real data?\nResponse Thank you for this insightful question regarding (1) the prevalence of internal adapters\nin real datasets and (2) whether the sequences flanking the low–base-quality region in Figure 5\nrepresent true biological events or artifacts. We provide a consolidated explanation below.\n1. Internal Adapters Are a Systematic and High-Impact Artifact in Real dRNA-\nseq Data\nDeepChopper detected internal adapters in 0.67-1.62% of all reads across RNA002 datasets and\n0.33% in RNA004 (Response Table 2), representing 15,690-148,452 affected reads per experiment.\nImportantly, when restricting analysis to reads that align as chimeras, internal adapters account\nfor the majority of chimeric reads:\n1. 63–92% of all chimeric reads in RNA002 datasets\n2. 24% in RNA004 (reduced but still substantial)\nThus, in a typical 10-million–read experiment, tens to hundreds of thousands of reads contain\ninternal adapters even after current best-practice Dorado trimming. This shows that internal\nadapters remain a systematic, not sporadic, artifact in modern ONT dRNA-seq workflows.\nDatasets\nAll Reads Chimeric Reads Only\nWith Internal % With Internal %\nSample Total Total\nAdapters (A/B) Adapters (C/D)\n(A) (B) (C) (D)\nA549 15,690 1,703,697 0.92 10,553 12,803 82.43\nMCF7 20,340 3,039,468 0.67 11,115 17,646 63.00\nHCT116 57,122 4,697,299 1.22 37,823 46,800 80.81\nK562 29,436 3,061,722 0.96 19,289 23,214 83.09\nHepG2 22,530 1,797,922 1.25 14,331 16,921 84.69\nVCaP RNA002 148,452 9,177,422 1.62 98,878 107,265 92.18\nVCaP RNA004 38,878 11,714,520 0.33 6,891 29,144 23.65\nNote: TotalreadsfromDorado(v0.5.2)withtrim. InternaladaptersdetectedbyDeepChopperafterDoradoprocessing.\nNote: VCaP RNA002 and RNA004 demonstrate that adapter-bridged chimeras persist across chemistries (discussed\ninR3.Q3andR3.Q5),thoughRNA004showsreducedprevalence.\n2. Are the Sequences Flanking the Low-Quality Region True Biological Events?\nWe re-examined the flanking sequences associated with the low-quality region shown in Figure\n5 (Previous response; now Manuscript Figure 3e). As illustrated in Response Figure 5 (now\nManuscript Extended Data Figure 13), read 3b2292e9-43e5-4e40-87d9-ccc23897377c (the read dis-\nplayed in Figure 5) contains two high-confidence flanking regions. BLAT alignment confirms that\ntheupstream(5′)segment(positions1–154)mapstochr14withintheRPS29genewith98% iden-\ntity, and the downstream (3′) segment (positions 230–722) maps to chr11 within the COX8A gene\nwith 96% identity (Response Figure 5). These results demonstrate that both flanking sequences\ncorrespond to genuine biological RNA derived from distinct loci.\nIncontrast, theinterveningsegment(positions155–229)doesnotaligntothereferencegenome\\andexhibitshallmarksofaninternaladapterartifact(ResponseFigure5highlightedinred). Across\nthe dataset, regions identified as adapters by DeepChopper consistently show:\n• Low base quality: Median Q ∼10 throughout the adapter region (Response Figure 5;\nManuscript Figure 2e)\n• Poor genome alignment: Median BLAT identity ∼0.3 (Response Figure 5; Manuscript\nFigure 2f)\n• Poly-A context: Poly-A sequences immediately upstream of the adapter (Response Fig-\nure 5; highlighted in green)\nchr14: 49,583,70049,583,80049,583,90049,584,00049,584,10049,584,20049,584,30049,584,40049,584,50049,584,60049,584,70049,584,80049,584,90049,585,00049,585,10049,585,20049,585,30049,585,40049,585,50049,585,60049,585,70049,585,80049,585,900\nUpstream Seq blat on YourSeq\nGENCODE V49 (2 items filtered out)\nRPS29\nRefSeq genes from NCBI\nRefSeq Curated\n40\n1 150\nAAAATTACTCTCCCATCCCCTACCATACCAACCCATTTTCATTTACGCCCTCCATCACAACCCTATCTTTCCCCCCTTTAGGGCTACGGCTGACCGTTTTTTGTGGTGTACTCCGTGCCATCATGTCCGTCCTGACGCCGCTGCTGCTGC\n151 300 30\nGGGGCTTGACAGGCTCAGCCCGGCGGCTCCAGTGCCGCGCGCCAAGATCCATTCGTTACTGCCGCCGGAGGGGAAGCTTGGGATCATGGAATTGGCCGTTGGGCTTACCTCCTGCTTCGTGACCTTCCTGCCTGTAGGCTGGATCCTGTC\n301 450 20\nACACCTGGAGACCTACAGGAGGCCAGAGTGAAGGGGTCCGTTCTGTCCCTCACCATCACTGTGACCTGACCAGCCCACCAGCCCATCCTGGTCATGTTCTTGTTACTTGTGGCCGGCCTCCCCTGGATCATGTCATTCATAATTCCAGTC\n451 600\n601 722\nScale 500 bases hg38\nchr11: 63,974,800 63,974,900 63,975,000 63,975,100 63,975,200 63,975,300 63,975,400 63,975,500 63,975,600 63,975,700 63,975,800 63,975,900 63,976,000 63,976,100 63,976,200 63,976,300 63,976,400 63,976,500\nblat on YourSeq\nDownstream Seq\nGENCODE V49\nENSG00000256100\nCOX8A\nRefSeq genes from NCBI\nRefSeq Curated\n)erocs\nQ(\nytilauq\nesaB\nAlignment of Upstream (5') Sequence\nAlignment of Downstream (3') Sequence\nResponse Figure 5 (Manuscript Extended Data Fig. 13) Representative internal adapter\ndetection with base quality visualization. RepresentativereadfromVCaPRNA002processed\nwith Dorado trim followed by DeepChopper, demonstrating internal adapter detection (highlighted\nin red, positions 155-229) and sub-reads recovery (blue box and purple box). (upper panel) BLAT\nalignment of upstream sequence (blue box, positions 1–154) to chr14 (RPS29 gene) with 0.98\nidentity,confirminggenuinebiologicalRNA.(middlepanel)Fullreadvisualizationshowinginternal\nadapter and sub-reads. Base quality scores (Q scores) shown as color-coded bars: yellow indicates\nhigh quality (Q > 40), dark blue indicates low quality (Q < 10). The adapter region shows\ncharacteristic poly-A sequences upstream (highlighted in green) and lower base quality compared\ntoflankingbiologicalsequences. Theadapterregion(75bp)showsnomatchesfoundtothereference\ngenome. Sequences before and after the adapter represent genuine biological RNA from different\ntranscripts artificially joined during library preparation or basecalling. Read ID: 3b2292e9-43e5-\n4e40-87d9-ccc23897377c. (bottom panel) BLAT alignment of downstream sequence (purple box,\npositions 230–722) to chr11 (COX8A gene) with 0.96 identity, confirming genuine biological RNA\nfrom a different transcript.\nTogether, these features confirm the internal sequence detected by DeepChopper is an adapter,\nwhile both flanks represent true biological RNA. This read therefore reflects an adapter-bridged\nchimera, where transcripts from RPS29 (chr14) and COX8A (chr11) were artificially joined during\nlibrary preparation or basecalling.\nWithout DeepChopper, such reads are often discarded entirely—losing valid biological signal\nfrom both transcripts—or incorrectly interpreted as a bona fide RPS29–COX8A gene fusion. By\ndetecting internal adapters and splitting reads at precise boundaries, DeepChopper recovers both\ntranscript fragments as independent sub-reads rather than removing the molecule altogether.\nAs established in R3.Q8, no existing tool detects internal adapters—Dorado’s 3’ end trimming\ndoes not reduce internal adapter-bridged artifacts (Manuscript Figures 2-3, Extended Data Figures\n8-11). These systematic errors remain undetected in current dRNA-seq workflows, propagating\nthroughtranscriptannotation,genefusiondetection,andexpressionquantification. Wehaveadded\nExtended Data Table 4 and Extended Data Figure 13 to the Results and Discussion section.\nover alignment-based methods, a comparison of the genomic language model vs alignment-based\nmethods (not necessarily minimap2, but some methods build upon minimap2 alignment), for ex-\nample, the recently online paper Breakinator tool (Heinz, Jakob M., Matthew Meyerson, and Heng\nLi. “Detecting Foldback Artifacts in Long Reads.” bioRxiv (2025): 2025-07.) and flair-fusion\n(which aims to identify fusion by removing chimeric reads). It would worth discussion to highlight\nthe advantage of using genomic language models in this context.\nResponse Wethankthereviewerforthisinsightfulquestion,whichencouragesadirectcomparison\nbetween genomic language models and alignment-based artifact detection methods.\n1. Direct Comparison With Alignment-Based Tools\nTo address this question, we benchmarked DeepChopper against Breakinator [7] using the VCaP\nRNA002 dataset, with cDNA sequencing serving as ground truth validation. The result (Response\nFigure 6; now Manuscript Extended Data Figure 3) shows the following:\n• DeepChopperremoves95.1%ofunsupportedchimericalignmentswhileincreasingthefraction\nof cDNA-supported biological chimeras from 5.8% (baseline) to 48.7%.\n• Breakinator reduces total chimeric alignments by 61.6%, but the number of cDNA-supported\nbiological chimeras drops to 4.8%, which is lower than the baseline. This indicates indiscrim-\ninate removal of both artifacts and genuine biological events.\n• BreakinatordetectszeroRT-mediatedfoldbacks,asexpected,becausedirectRNAsequencing\ndoes not use reverse transcription.\nThese results clearly demonstrate that DeepChopper selectively removes adapter-bridged arti-\nfacts while preserving biologically meaningful rearrangements.\n80\ncDNA\n60\n40\n20\nDeepChopper Breakinator\n)000,1\nx(\ntnuoC\nNo support 101,038 4,912 38,844\ncDNA-supported 6,228 4,666 1,972\nSupport Rate 5.8% 48.7% 4.8%\nResponseFigure6(ExtendedDataFigure3)Comparisonofchimericalignmentreduction\nstrategies in VCaP RNA002 dRNA-seq data. Stacked bar plot showing chimeric alignments\n(in thousands) for three processing pipelines: Dorado with adapter trimming (baseline), Dorado\nwith adapter trimming followed by DeepChopper, and Dorado with adapter trimming followed by\nBreakinator. Graybarsrepresentunsupportedchimericalignments(likelyartifacts); pinkbarsrep-\nresent cDNA-supported chimeric alignments (biological events). DeepChopper achieves substantial\nreduction in unsupported chimeric alignments (95.1%) while preserving cDNA-supported biologi-\ncal chimeras (support rate: 48.7%). In contrast, Breakinator shows moderate reduction in total\nchimeric alignments (61.6%) but decreases the cDNA support rate to 4.8%, lower than the baseline\n(5.8%), indicating indiscriminate removal of both artifacts and biological events.\n2. Why Alignment-Based Alignment Methods Cannot Distinguish Artifacts from\nBiology\nFundamental paradigm difference: DeepChopper uses adapter-based detection—internal\nadapterpresencedefinitivelyindicateslibrarypreparation/basecallingartifacts,notbiologicalevents.\nThis enables unambiguous classification and selective removal.\nAlignment-based methods use rule-based filtering—hand-crafted thresholds on alignment\npatterns (distance > 1 Mb, interchromosomal, opposite strand within 200 bp) [7]. These patterns\noccur in both adapter-bridged artifacts and genuine biological rearrangements, making discrimina-\ntion impossible.\nBreakinator limitations for dRNA-seq: Breakinator [7] detects two types of artifacts based\non alignment patterns and distance thresholds: (1) RT foldback inversions (opposite strand break-\npoints within 200 bp), targeting RT template-switching artifacts absent in dRNA-seq, precisely\nthe RT chimeras discussed in R3.Q1 that lack internal adapters; and (2) distance-based chimera\nfiltering (intrachromosomal breakpoints > 1Mb, interchromosomal events) that cannot distinguish\n18\nadapter-bridged artifacts from genuine biological RNA rearrangements. Neither detection strat-\negy addresses internal adapters, and Breakinator removes entire reads without sub-read recovery,\ndiscarding genuine biological sequences flanking adapters.\nFLAIR-fusion limitations: Task-specific gene fusion caller [12], not general QC tool. Filtering\nrules leverage gene annotation and splice sites—information unavailable for other analyses like iso-\nform quantification. No adapter detection capability. We encountered execution failures; issues re-\nportedonGitHub(https://github.com/cafelton/FLAIR-fusion/issues/6)remainunresolved.\n3. Advantages of DeepChopper: Genomic Language Models Over Alignment-\nBased Methods\nAs established in R3.Q2 and R3.Q8, DeepChopper’s genomic language model framework delivers\ncritical capabilities beyond the reach of traditional approaches. Detection via internal adapter\nsignatures provides unambiguous artifact identification: adapterpresencewithinreadscon-\nclusively marks library preparation or basecalling failures rather than biological processes, allowing\nDeepChopper to retain authentic biological rearrangements that distance-based rules eliminate in-\ndiscriminately. Reference-independent processing avoids mapping biases and alignment ambi-\nguities (R3.Q9, Response Table 2). Key genomic language model features supporting this strategy\ninclude: (1) foundation model training on genomic data establishes intrinsic patterns of au-\nthentic biological sequences[8, 13, 14], supporting discrimination between genuine transcripts and\nadapter contaminants through learned representations without requiring known adapter motifs;\n(2) robust pattern recognition achieving reliable detection despite degraded sequences and un-\ncharacterizedadaptersequences; (3)base-quality integrationincorporatingper-nucleotideconfi-\ndence scores ( Extended Data Table 3); (4) extended receptive fields through HyenaDNA’s 32K\nnucleotide window [8] enabling adapter identification at any internal location; and (5) nucleotide-\nlevel boundary accuracy via independent positional assessment ( R3.Q7), critical for successful\nsub-read extraction.\n4. Summary\nThese tools address fundamentally different problems. DeepChopper performs pre-alignment qual-\nity control with internal adapter detection and sub-read recovery in dRNA-seq, while Breakinator\ntargets post-alignment artifact detection based on distance rules in cDNA-seq and FLAIR-fusion\nprovides specialized fusion calling with artifact-aware filtering. For dRNA-seq, DeepChopper fills a\ncritical gap that alignment-based methods cannot address: detecting internal adapters and recov-\nering genuine biological sequences, as established in R3.Q1, R3.Q2, andR3.Q8. We have added\nthis comparison to the Discussion section and Extended Data Figure 3, clarifying DeepChopper’s\nuniquepositionindRNA-seqworkflowsandthecomplementarynatureofdifferentartifactdetection\napproaches.\nR3.Q11 Following up to R2 Q4, the response does not explain how different thresholds of this\nclassification decision. In the context of 2 labels, the sum of the 2 adapter class probability would\nalways be 1, that is, the classification of 1 label probability being greater than the other label is\nstill 0.5 threshold. Please clarify and state scenarios where the 2 probabilities does not sum to\n1, cause it’s hard to understand why in the case of 2 labels, the sum would not be 1. Related to this,\nresponse figure 6 is very clean indeed, I wonder is there some kind overfitting or testing data is too\nsimple? Please elaborate for challenging scenarios.\nity formulation and expand our discussion of challenging scenarios to address concerns regarding\noverfitting.\n1. Clarifying the Probability Threshold\nThe reviewer is absolutely correct: for a binary softmax classifier, the two output probabilities\nalways sum to 1. Thus:\nP(adapter)+P(non-adapter) = 1\nOur decision rule is therefore equivalent to applying a 0.5 threshold on P(adapter).\nWe indeed use a standard softmax formulation—primarily for calibrated confidence scores and\nextensibility to multi-class settings—but the implicit 0.5 threshold should have been stated ex-\nplicitly. We have clarified this in the revision.\n2. Why the Distribution in Response Figure 6 Looks “Clean”\nThe clean bimodal distribution is not due to test set simplicity or overfitting. Rather, it reflects\na task where the sequence signal at adapter boundaries is strong: internal adapter regions have\npronounced base-quality drops and characteristic non-biological sequence patterns that the model\nreliably learns.\nWe emphasize that the model’s generalization is supported by extensive real-data validation\nacrossmultiplechemistries(RNA002andRNA004)andmultiplecelltypes(A549,MCF7,HCT116,\nK562, HepG2, and VCaP), as described in our response to R3.Q2 and R3.Q2.\nThe task is not trivial. Below we highlight concrete challenging scenarios observed in real\ndatasets that demonstrate where the model encounters difficulty.\n3. Challenging Real-World Scenarios and Model Limitations\nScenario 1: Incomplete detection of 3’ end adapters in multi-adapter reads In reads\ncontaining both an internal adapter and a 3’ end adapter, DeepChopper consistently detects the\ninternaladapterbutmaypartiallymissthe3’endadapter. Theflankingbiologicalsequenceremains\nintact and is not misclassified. This reflects the model’s prioritization of internal signals, which is\nappropriate for its intended purpose.\nResponseFigure7(ExtendedDataFigure14)showsreadc16c6ade-135b-4073-a1d6-5a9c6900bfb2\n(VCaP RNA002, Dorado without trim): internal adapter positions 1026-1105 correctly detected,\n3’ end adapter partially missed. This occurs when multiple adapters present at different positions.\n20\n1 150\nATCTTGACTTCCAGATGGAACAGTACATCTATAAAGGAAAAGTGATGGCATCTATATCAAATCCTAGAGGACCTGGGAGAAGCTTCTGCTGGCAGCTCGTGCAATTGTTGCCATTGAAAACCCTGCTGATGTCAGTGTTATATCCCAGGA\n151 300\nATACTGGCCAGAGGGCTGCTGAAGTTTGCTGCTGCCACTAGAGCCACTCCAATTGCTGGCCGCTCACTCCTGGAACCTTCACTAACCAGATCCCAGGCCTTCCGGGAGCCACGGCTTCTTGTGGTTACTGACCCCAGGGCTGACCACCAG\n40\n301 450\nCCTCTCACGGAGGCATCTTATGTTAACCTACCTACCATTGCGCTGTGTAACACAGATTCTTCCTCTGCGCCATGTGGACATTGCCATCCCATGCATCAACAAGGGAAATCACTCAGTGGGGTTTGATGTGGTGGATGCTGGCTCGGGAAG\n451 600\n601 750\nGCTCCCGCTCCTGAGTTCACTGCTACTCAGCCTGAGGTTTGCAGACTGGTCTGAAGGTGTACAGGTGCCCTCTGTGCCTATTCAGCAATCTCCCTACTGAAGACTGGAGCGCTCAGCCTCCTGCCACGGAAGACTGGTCGCAATACCCAC 30\n751 900\nTGCTCAGGCCACTGAATAGGGTAGGAGCAACCACTGACTGGTCTTAAGCTGTTCTTGCATAGGCTCTTAAGCAGCATGGAAAAATGGTTGATGGAAAATAAACATCAGTTTCTATTAAAAAAAAATCCCTCCCTCCCCTACCCCCACTTC\n901 1050\nTGCCACCCACATCTTATCTCATATCATCCCACGACACATCTCCCTAACCCCCCACGGCTGCCGAAGATGGCGGAGGTGCAGGTCTTCCTGGTGCTTGATGGTCGAGGCCATTTCCGCCGCCTGGCGGCCATCGTGGCTAAACAGGTCTGC\n1051 1200\nTGGGCCGGAAGGTGGTGGTCGTACGCTGTGAAGGTATCAACATTTCTGGCAATATTTCTACAGAAACAAGTTGAAGTACCTGGCTTCCGCAAGCGGATGAACACCAACCCTCCCGAGGCCCCCTACCACTTCCGGGCCCCAGCCGCATCT 20\n1201 1350\nTCTGGCGGACCGTGCGAGGTATGCTGCCCACAAAACCAAGCGAGGCCAGGCCGCTCTGGACCGTCTCAAGGTGTTTGACGGCATCCCACCGCCCCTACGACAAGAAAAAGCGGATGGTGGTTCCTGCTGCCCTCAAGGTCGTGCGTCTGA\n1351 1500\nAGCCTACAAGAAAGTTTGCCTATTTGGGGCGCCTGGCTCACGAGGTCGGCTCGGAAGTACCAGGCAGTGACAGCCACCCTGGAGGAGAAGAGGAAAGAGAAAGCCAAGATCCACTACCGGAAGAAGAAACAGCTCTCGAGGCTACGGAAA\n1501 1650\nCAGGCCGAGAAGAACGTGGAGAAGAAAATTGGAACAAATACACAGAGGTCTCAAGACCCACGGACTCCTGGTCTGAGCCCAATAAAGACAATAATTCCTAATAAAAAAAAAATCCCCACTCCCATCATCATCCATATCAACCTGCCCTCC 10\n1651 1800\nCCATCCTTATTCCC\n1801 1814\n)erocs\nQ(\nytilauq\nesaB\nResponse Figure 7 ( Extended Data Figure 14) Challenge Scenario 1: Incomplete 3’ ter-\nminal adapter detection in multi-adapter read. Representative read from VCaP RNA002\nprocessedwithDoradowithouttrimfollowedbyDeepChopper(withrefinementapplied). Theread\ncontains both an internal adapter (positions 1026-1105, highlighted in red, correctly detected) and\na 3’ end adapter that DeepChopper failed to completely detect. Base quality scores (Q scores)\nshown as color-coded bars: yellow indicates high quality (Q > 40), dark blue indicates low quality (Q < 10).\nThe internal adapter shows characteristic low quality compared to flanking biological sequences. This\nscenario demonstrates that when multiple adapters are present, DeepChopper reliably detectsinternal\nadapters (its primary function) but may incompletely detect terminal adapters.\nRead ID: c16c6ade-135b-4073-a1d6-5a9c6900bfb2.\nSolution: Combined Dorado-DeepChopper workflow (Response Figure 8) ( Extended Data\nFigure 16). Dorado removes 3’ end adapters; DeepChopper detects internal adapters. Complemen-\ntary functions confirmed in R3.Q5 and R3.Q8.\n21\n1 150\nATCTTGACTTCCAGTGGAACAGTACATCTATAAAGGAAAAGTGATGGCATCTATATCAAATCCTAGAGGACCTGGGAGAAGCTTCTGCTGGCAGCTCGTGCAATTGTTGCCATTGAAAACCCTGCTGATGTCAGTGTTATATCCCAGGAA\n151 300\n301 450\nCCTCTCACGGAGGCATCTTATGTTAACCTACCTACCATTGCGCTGTGTAACACAGATTCTTCCTCTGCGCCATGTGGACATTGCCATCCCATGCATCAACAAGGGAGCTCACTCAGTGGGTTTGATGTGGTGGATGCTGGCTCGGGAAGT\n451 600\nTCTGCGCATGCGTGGCACCATCTCCCGTGAACACCCATGGGAGGTCATGCCTGATCTGTACTTCTACAGAGATCCTGAAGAGATTGAAAAAGAAGAGCAGGCTGCTGCTGAGAAGGCAGTGACCAAGGAGGAATTCAGGGTGAATGGACT\n601 750 30\nGCTCCCGCTCCTGAGTTCACTGCTACTCAGCCTGAGGTTTGCAGATTGGTCTGAAGGTGTACAGGTGCCCTCTGTGCCTATTCAGCAATCTCCCTACTGAAGACTGGAGCGCTCGTAGCTCCTGCCACGGAAGACTGGTCGTAAGCTCCC\n751 900\nACTGCTCAGGCCACTGAATAGGGTAGGAGCAACCACTGACTGGTCTTAAGCTGTTCTTGCATAGGCTCTTAAGCAGCATGGAAAAATGGTTGATGGAAAATAAACATCAGTTTCTAAAAAATCCCTCCCTCCCCTACCCCCACTTCTATC\n901 1050\nCCACCATCATCCATTTCACATTCCCCATCCCTAAACCCCCCATCGCCGAAGATGGCGGAGGTGCAGGTCTCCTGGTGCTTGATGGTCGAGGCCATCTTTGCCGCCTGGCGGCCATCGTGGCTAAACAGGTCTGCTGGGCCGGAAGGTGGT\n20\n1051 1200\nGGTCGTACGCTGTGAAGGTATCAACATTTCTGGCAATATTTCTACAGAAACAAGTTGAAGTACCTGGCTTCCGCAAGCGGATGAACACCAACCCTTCCCGAGGCCCCCTACCACTTCCGGGCCCCAGCCGCATCTTCTGGCGGACCGTGC\n1201 1350\nGAGGTATGCTGCCCACAAAACCAAGCGAGGCCAGGCCGCTCTGGACCGTCTCAAGGTGTTTGACGGCATCCCACCGCCCTACGACAAGAAAAAGCGGATGGTGGTTCCTGCTGCCCTCAAGGTCGTGCGTCTGAAGCCTACAAGAAAGTT\n1351 1500\nTGCCTATTTGGGGCGCCTGGCTCACGAGGTCGGCATGGAAGTACCAGGCAGTGACAGCCACCCTGGAGGAGAAGAGGAAAGAGAAAGCCAAGATCCACTACCGGAAGAAGAAACAGCTCTCGAGGCTACGGAAACAGGCCGAGAAGAACG 10\n1501 1650\nTGGAGAAGAAAATTGACAATACACAGAGGTCTCAAGACCCACGGACTCCTGGTCTGAGCCCAATAAAGCATTGTTAATTCCTCAAAAAAAAATAAAAAAAAAA\n1651 1753\n)erocs\nQ(\nytilauq\nesaB\nResponse Figure 8 ( Extended Data Figure 16) Solution for Challenge Scenario 1: Com-\nbined Dorado-DeepChopper workflow. The same representative read (ID: c16c6ade-135b-\n4073-a1d6-5a9c6900bfb2) processed with Dorado with trim followed by DeepChopper. Dorado\nsuccessfully removed the 3’ end adapter, while DeepChopper detected the internal adapter (high-\nlighted in red, positions adjusted after Dorado trimming). Base quality scores (Q scores) shown as\ncolor-coded bars: yellow indicates high quality (Q > 40), dark blue indicates low quality (Q < 10).\nThe internal adapter shows characteristic low quality. This demonstrates that combining Dorado\n(3’ end adapters) with DeepChopper (internal adapters) addresses complementary problems and\nresolves the incomplete detection issue shown in Challenge Scenario 1.\nScenario 2: Partial internal detection Inreadscontaininganinternaladapter(3’endadapter\nis trimmed by Dorado), DeepChopper detects part of the internal adapter. Response Figure 9 (\nExtended Data Figure 15) shows read 0955d980-9c79-48c2-a474-08c8c39cb00f (VCaP RNA002,\nDorado with trim): partial adapter detection in highly degraded sequences. Portions of adapter\nmay be missed when error-rich.\n22\n1 150\nAGACGGATTTTTATGTTGCCCAGGCTGGGTACCAGGCTCCTGGGCTCAGATGGTCCCTCCTGCCTCAGCTTCCAAAGGCACAGGCCAAGTTGTAGCTTTGTCCCTCTGCCATATGCCCAACAAGAGGTCCTATACCTTTTAATGAATTGA\n40\n151 300\nCTTTCAAATTGGTTATGTTGGTGGGCAAGTTTCTTTAAGCTGGAAATTGTAAATTCCTGAAATGCTTTTCCATGCAGTTACCATGAACTGTTACTACAATAAAGGATGGTTTGGGTGCACAAAAAATCCCCCTACCCCCACATATCCCCT\n301 450\nTTATCCTGCTATATTGCAAACCCATAACAATTTCACCCATAAACTGAATTTTCCATCCTGTCCCATTAGTGGTAGCTATAAAATCCTGCCCACCGAGAAAACCACGTCTTCTTCCGTCTTCATGCTGCTTGCCTCTGGCATTTGCACTTC\n451 600 30\nATCCAACGATACCTGAGGTGGTGTCTCCATCATCCGGGCCTGTGCCCCAATGAGCCCAGCGCCAGCTCCAATCCCTCCTCCCTGCAAGGGTCCGGGCTCCGGCCTCGCCTCCTCTTCCAGCCAAGCAGAACGAGATCAACAAGCTGCAGC\n601 750\nAGTGAGCAGGATGGCTCCCTGAAAGACCATTGCGCCGGCCCCTCCGCCTCCGGGGCTGTAGTGGCTGAAAAGAGCGACCGCTGCTCGGCCTGCTGACGCCTCTCCAGGCAACCTCCCCCCTTCCCCGTCCTGCCCTGGGCGAGCCCCTCC\n751 900\n901 1050\nCATGCCCCACCTGTTGCCTGCCTCCCTCCCCTTCCCTCTCCCACCTTGGGACCACTGGGTACAAGAGATGGGATGCCCAATGCGTCCAATATTATGAAACTAATCCTTAACCCCTGCGCCTAATGATACCTGTTCAGCAGTCCTCAGTGT\n1051 1200\nAGAGGGATGTGGGTCAGAGAAAGGTGCTGTGGACATGTGGGTGGAGAAGGGAGGGTGGCCAGCACTTAGTTGGGAGGAATGGTGCTCTGCTGGCCACAAGGAAAAGGAGGAGGTGTCCTGGGGTGAGAGTTAGGGAGAGAGAAGCAGGCA 10\n1201 1350\nGATAAGTTTGGAGCAGGGTTGGTCAAGGCCACCTGCCTCTAGTCCCCAAGGCCCTCTCTCTGCCTGAAATGTTACACATTAAACAGGATTTTCAGTAAAAAAAAAAATC\n1351 1459\n)erocs\nQ(\nytilauq\nesaB\nResponse Figure 9 ( Extended Data Figure 15) Challenge Scenario 2: Partial internal\nadapter detection. Representative read from VCaP RNA002 processed with Dorado trim option\nfollowed by DeepChopper (with refinement applied). The read shows partial adapter detection\n(highlighted in red), demonstrating challenges with degraded or error-rich adapter sequences where\nDeepChopper may detect only portions of the complete adapter sequence. Base quality scores (Q\nscores) shown as color-coded bars: yellow indicates high quality (Q > 40), dark blue indicates low\nquality(Q < 10). Suchpartialdetectionscanleadtospuriousshortfragments, whichareaddressed\nthrough post-processing length filtering. Read ID: 0955d980-9c79-48c2-a474-08c8c39cb00f.\nSolution: Post-processinglengthfiltering(default20bpminimum,configurablevia--min-read-length).\nRemoves spurious short fragments while preserving biological sequences. Combined with sliding\nwindow refinement (R3.Q7), provides quality control ensuring biologically meaningful sub-reads\nretained.\n4. Summary\nThese challenging scenarios have minimal impact on overall performance:\n• Primary objective achieved: Internal adapter detection—DeepChopper’s core purpose\n(R3.Q1)\n• Complementary tools: Dorado (3’ end) + DeepChopper (internal) address both artifact\ntypes effectively\n• Biological discovery enabled: 62-91% artifact reduction, nearly two-fold transcript dis-\ncovery, 89% fewer false fusions, preserved biological rearrangements ( Figures 2-3)\n• Robust generalization: Consistent performance across cell lines, species, chemistries\nCritically, as emphasized in R3.Q1 and R3.Q2, DeepChopper solves a previously unsolved\n23\nproblem—no existing tool detects internal adapters in dRNA-seq. Strong performance across di-\nverse validation confirms genuine capability. We have added Extended Data Figures 14-16 (chal-\nlenging scenarios and solutions) to the Discussion section, demonstrating transparency about lim-\nitations alongside overall strong performance.\n[1] Cong Ma, Mingfu Shao, and Carl Kingsford. “SQUID: Transcriptomic Structural Variation\nDetection from RNA-seq”. In: Genome Biology 19 (Apr. 12, 2018), p. 52. issn: 1474-7596.\ndoi: 10.1186/s13059-018-1421-5. PMID: 29650026.\n[2] ZsoltBal´azs etal. “Template-SwitchingArtifactsResemble AlternativePolyadenylation”.In:\nBMC Genomics 20.1 (Nov. 8, 2019), p. 824. issn: 1471-2164. doi: 10.1186/s12864-019-\n6199-7.\n[3] Laura Schulz et al. “Direct Long-Read RNA Sequencing Identifies a Subset of Question-\nable Exitrons Likely Arising from Reverse Transcription Artifacts”. In: Genome Biology 22.1\n(June 28, 2021), p. 190. issn: 1474-760X. doi: 10.1186/s13059-021-02411-1.\n[4] Daniel R Garalde et al. “Highly parallel direct RNA sequencing on an array of nanopores”.\nIn: Nature methods 15.3 (2018), pp. 201–206.\n[5] Miten Jain et al. “Advances in nanopore direct RNA sequencing”. In: Nature methods 19.10\n(2022), pp. 1160–1164.\n[6] YongjiZouetal.“AComparativeEvaluationofComputationalModelsforRNAModification\nDetection Using Nanopore Sequencing with RNA004 Chemistry”. In: Briefings in Bioinfor-\nmatics 26.4 (July 1, 2025), bbaf404. issn: 1477-4054. doi: 10.1093/bib/bbaf404.\n[7] Jakob M. Heinz, Matthew Meyerson, and Heng Li. Detecting Foldback Artifacts in Long-\nReads. Sept. 23, 2025. doi: 10.1101/2025.07.15.664946. Pre-published.\n[8] Eric Nguyen et al. “Hyenadna: Long-range genomic sequence modeling at single nucleotide\nresolution”. In: Advances in neural information processing systems 36 (2024).\n[9] CamilleSessegoloetal.“TranscriptomeProfilingofMouseSamplesUsingNanoporeSequenc-\ning of cDNA and RNA Molecules”. In: Scientific Reports 9.1 (Oct. 17, 2019), p. 14908. issn:\n2045-2322. doi: 10.1038/s41598-019-51470-9.\n[10] Ryan R. Wick et al. “Completing bacterial genome assemblies with multiplex MinION se-\nquencing”. In: Microbial Genomics 3.10 (Oct. 2017). issn: 2057-5858. doi: 10.1099/mgen.\n0.000132. url: http://dx.doi.org/10.1099/mgen.0.000132.\n[11] Quentin Bonenfant, Laurent No´e, and H´el`ene Touzet. “Porechop ABI: discovering unknown\nadapters in Oxford Nanopore Technology sequencing reads for downstream trimming”. In:\nBioinformatics Advances 3.1 (2023), vbac085.\n[12] ColetteFeltonetal.Detection of Alternative Isoforms of Gene Fusions from Long-Read RNA-\nseq with FLAIR-fusion. July 18, 2023. doi: 10.1101/2022.08.01.502364. Pre-published.\n[13] Hugo Dalla-Torre et al. “Nucleotide Transformer: building and evaluating robust foundation\nmodels for human genomics”. In: Nature Methods (2024), pp. 1–11.\n[14] Zhihan Zhou et al. DNABERT-2: Efficient Foundation Model and Benchmark for Multi-\nSpecies Genome.2023.doi:10.48550/arXiv.2306.15006.arXiv:2306.15006[cs, q-bio].\nPre-published.\n25"}, {"round": 3, "reviewer_comments": {"reviewer_3": "The authors have thoroughly and satisfactorily addressed all of the comments and concerns I raised. I sincerely appreciate\ntheir hard work and patience."}, "author_response": ""}]

Decision letter