Supplementary tables

Computational tools for RNA modification detection and analysis using Oxford DRS.

This table summarizes currently available computational methods developed for detecting, quantifying, or modeling RNA modifications from Oxford Nanopore Technologies (ONT) direct RNA sequencing data. For each tool, we list the functional description, GitHub repository, targeted RNA modification(s), supported pore chemistry (RNA002 and/or RNA004), and year of publication or release.

Comprehensive list of RNA modification–related tools for nanopore DRS

Tool	Functional description	GitHub	Modification	Pore chemistry	Year
Tombo	Signal-level statistical framework for detecting RNA and DNA modifications from nanopore reads.	https://github.com/nanoporetech/tombo	m6A, m5C	RNA002	2017
EpiNano	Machine learning–based framework for detecting RNA modifications (e.g., m6A) from basecalling error features in nanopore data.	https://github.com/enovoa/EpiNano	m6A	RNA002	2019
DiffErr	Differential error-based method for detecting RNA modifications by comparing mismatch profiles between conditions.	https://github.com/bartongroup/differr_nanopore_DRS	m6A	RNA002	2020
MINES	Supervised learning tool for m6A detection from nanopore signal features using motif constraints.	https://github.com/YeoLab/MINES	m6A	RNA002	2020
Nano-ID	A method that detects newly synthesized RNA isoforms and monitors isoform metabolism. Combines metabolic RNA labeling, long-read nanopore sequencing of native RNA molecules and machine learning.	https://github.com/birdumbrella/nano-ID	5EU	RNA002	2020
ELIGOS	Statistical model for identifying RNA modifications based on systematic basecalling errors.	https://gitlab.com/piroonj/eligos2	m6A, m1A, 5moU, Ψ, m7G, A-to-I, hm5C, f5C, m5C	RNA002	2021
Nanocompore	Statistical framework for detecting differential RNA modifications by comparing signal distributions across conditions.	https://github.com/tleonardi/nanocompore	m6A	RNA002	2021
NanoPsu	Supervised machine learning tool for pseudouridine (Ψ) detection from nanopore signals.	https://github.com/sihaohuanguc/Nanopore_psU	Ψ	RNA002	2021
nanoRMS	Single-molecule RNA modification stoichiometry estimation framework using signal clustering.	https://github.com/novoalab/nanoRMS	Ψ	RNA002	2021
Sequoia	Visualization and signal exploration tool for nanopore current data.	https://github.com/dnonatar/Sequoia	m6A, m5C	RNA002	2021
xPore	Probabilistic mixture model for estimating RNA modification stoichiometry and differential modification from nanopore signals.	https://github.com/GoekeLab/xpore	m6A	RNA002	2021
Yanocomp	Tool for identifying RNA modification sites using nanopore signal clustering and statistical modeling.	https://github.com/bartongroup/yanocomp	m6A	RNA002	2021
nanom6A	Machine learning–based m6A detection tool using signal-derived features from nanopore DRS data.	https://github.com/gaoyubang/nanom6A	m6A	RNA002	2021
DENA	Recurrent neural network–based framework for m6A detection from nanopore signals.	https://github.com/weir12/DENA	m6A	RNA002	2022
Dinopore	CNN-based model for detecting RNA editing directly from ionic current traces.	https://github.com/darelab2014/Dinopore	A-to-I	RNA002	2022
IndoC	Machine learning framework for pseudouridine detection from nanopore signal and alignment features.	https://github.com/geno-verse/indoC	Ψ	RNA002	2022
JACUSA2	Variant and RNA modification detection framework using statistical comparison of aligned read features.	https://github.com/dieterich-lab/JACUSA2	m6A	RNA002	2022
ModPhred	Modification-aware post-processing tool for annotating basecalled reads with modification tags.	https://github.com/novoalab/modPhred	m6A, m5C	RNA002	2022
Penguin	Supervised ML-based tool for Ψ detection from nanopore data.	https://github.com/Janga-Lab/Penguin	Ψ	RNA002	2022
DRUMMER	Differential RNA modification detection tool based on basecalling error comparison.	https://github.com/DepledgeLab/DRUMMER	rRNA modifications	RNA002	2022
nanoSHAPE	Signal-based RNA structural probing tool (SHAPE-like) using nanopore data.	https://github.com/physnano/rRNA_nanoSHAPE	rRNA modifications	RNA002	2022
m6Anet	Deep learning (MIL-based) framework for m6A site detection and stoichiometry estimation from nanopore reads.	https://github.com/GoekeLab/m6anet	m6A	RNA002, RNA004	2022
DeepEdit	Deep learning tool for RNA editing or modification detection (exact scope unclear).	https://github.com/weir12/DeepEdit	A-to-I	RNA002	2023
Dorado	ONT production basecaller with integrated RNA modification detection models.	https://github.com/nanoporetech/dorado	m6A, m5C, Ψ, A-to-I	RNA004	2023
PsiNanopore	This package provides computational tools for detecting pseudouridine modification sites in nanopore sequencing data by comparing direct RNA vs IVT samples, calculating p-values to identify candidate positions, and visualizing raw ionic signals, with a complete step-by-step pipeline covering basecalling, alignment, and BAM file processing.	https://github.com/RouhanifardLab/PsiNanopore	Ψ	RNA002	2023
CHEUI	Deep learning–based m6A and m5C detection framework from nanopore DRS data.	https://github.com/comprna/CHEUI	m6A, m5C	RNA002	2024
IL-AD	Integrated learning-based RNA modification detection framework (exact scope unclear).	https://github.com/wangziyuan66/IL-AD	m1A, m6A	RNA002	2024
iForest	Isolation Forest–based anomaly detection approach for identifying RNA editing events.	https://github.com/F0nz0/C_to_U_classifier	C-to-U	RNA002	2024
ModQuant	Quantification framework for RNA modification stoichiometry from nanopore reads.	https://github.com/wanunulab/ModQuant	Ψ	RNA002	2024
m6ATM	m6A detection tool based on feature engineering and supervised learning.	https://github.com/poigit/m6ATM	m6A	RNA002	2024
m1a-prediction	Tool for m1A modification prediction from nanopore DRS data.	https://github.com/BernieeeX/m1a-prediction	m1A	RNA002	2024
mAFiA	Modification-aware basecalling framework integrating sequence decoding and RNA modification detection.	https://github.com/dieterich-lab/mAFiA	m6A	RNA002	2024
NanoML-5moU	Machine learning tool for detecting 5-methoxyuridine (5moU) modifications.	https://github.com/JiayiLi21/NanoML-5moU	5moU	RNA002	2024
NanoMUD	Multi-mark deep learning framework for RNA modification detection.	https://github.com/ABOMSBI/NanoMUD	Ψ, m1Ψ	RNA002	2024
NanoPsiPy	Tool for pseudouridine detection using nanopore signal and basecalling features.	http://github.com/vetmohit89/NanoPsiPy.git	Ψ	RNA002	2024
NanoSPA	Deep learning–based pseudouridine detection framework from nanopore signals.	https://github.com/sihaohuanguc/NanoSPA	m6A, Ψ	RNA002	2024
Nm-Nano	Supervised learning framework for detecting 2′-O-methylation (Nm) from nanopore data.	https://github.com/Janga-Lab/Nm-Nano	Nm	RNA002	2024
RNAkinet	RNA kinetic modeling tool from nanopore DRS signal data.	https://github.com/maragkakislab/rnakinet	5EU	RNA002	2024
RedNano	Deep residual neural network integrating signal and basecalling features for m6A detection.	https://github.com/Derryxu/RedNano	m6A	RNA002	2024
TandemMod	Multi-label deep learning framework for simultaneous detection of multiple RNA modifications.	https://github.com/yulab2021/TandemMod	m6A, m5C, hm5C, m7G, Ψ, A-to-I	RNA002	2024
DEMINERS	Deep learning framework for nanopore signal decoding and RNA modification detection.	https://github.com/LuChenLab/DEMINERS	m6A	RNA002	2025
DirecRM	Deep learning–based direct RNA modification detection framework.	https://github.com/yuxinPenny/DirectRM	ac4C, m1A, m5C, m6A, m7G, Ψ	RNA002, RNA004	2025
DRAP3R	Custom nanopore direct RNA sequencing method and analysis framework for capturing nascent Pol III-derived RNAs and a small subset of Pol II-derived ncRNAs.	https://github.com/DepledgeLab/DRAP3R	tRNA modifications	RNA004	2025
MoDorado	Light-weight algorithm that detects modification by off-label use of pre-trained modification-specific models in nanopore direct RNA sequencing (SQK-RNA004).	https://github.com/KleistLab/MoDorado	tRNA modifications	RNA004	2025
ModiDeC	Personalized two-input neural network designed to identify RNA modifications from direct RNA sequencing using RNA002 or RNA004 Oxford Nanopore technology kits.	https://github.com/mem3nto0/ModiDeC-RNA-modification-classifier	m6A, Gm, A-to-I, Ψ	RNA002, RNA004	2025
NanoNM	Ensemble machine learning–based tool for transcriptome-wide Nm detection.	https://github.com/YanqiangLi/NanoNm	Nm	RNA002	2025
NanoRL	Reinforcement learning–based RNA modification detection framework.	https://github.com/wangziyuan66/NanoRL	m1A, m6A, ac4C, m5C, hm5C, m5U, Ψ, m1Ψ	RNA002	2025
ORCA	Multi-modification deep learning detection framework for nanopore RNA.	https://github.com/bioinfo-biols/ORCA	m1A, m6A, Ψ, m5C, hm5C, f5C	RNA002, RNA004	2025
RMNet	Neural network model for RNA modification detection from nanopore signals.	https://github.com/liqingwen98/RMNet	m6A	RNA002	2025
RNANO	Deep learning tool for RNA modification detection from Nanopore direct RNA sequencing data.	https://github.com/abhhba999/RNANO	m6A, m5C, m1A, Ψ, Nm, ac4C, m7G	RNA002	2025
SingleMod	Attention-based deep learning model for single-molecule RNA modification detection.	https://github.com/xieyy46/SingleMod-v1	m6A	RNA002, RNA004	2025
Uncalled4	Signal-level nanopore analysis framework for modification detection and event alignment.	https://github.com/skovaka/uncalled4	m6A	RNA002, RNA004	2025
Xron	Neural network–based methylation-aware basecaller for simultaneous sequence and m6A detection.	https://github.com/haotianteng/Xron	m6A	RNA002, RNA004	2025
modCnet	Neural network–based multi-modification detection model for nanopore data.	https://github.com/yulab2021/modCnet	m5C, ac4C	RNA002	2025
m6Aiso	Isoform-level m6A detection and quantification tool for long-read RNA sequencing.	https://github.com/SYSU-Wang-LAB/m6Aiso	m6A	RNA002	2025
m6ABasecaller	Basecaller-integrated m6A detection tool for real-time nanopore modification calling.	https://github.com/novoalab/m6ABasecaller	m6A	RNA002	2025
pum6A	Positive-unlabeled learning framework for m6A identification from nanopore reads.	https://github.com/liuchuwei/pum6a	m6A	RNA002	2025
Ψ-co-mAFiA	Joint Ψ and m6A detection framework integrating signal features.	https://github.com/dieterich-lab/psi-co-mAFiA	m6A, Ψ	RNA002	2025
CircRM	Circular RNA modification detection framework from nanopore data.	https://github.com/jiayiAnnie17/CircRM	m1A, m5C, m6A	RNA002	2026

Note

• Modification abbreviations: m6A (N6-methyladenosine), m5C (5-methylcytosine), Ψ (pseudouridine), m1A (1-methyladenosine), hm5C (5-hydroxymethylcytosine), f5C (5-formylcytosine), ac4C (N4-acetylcytidine), m7G (7-methylguanosine), Nm (2’-O-methylation), 5EU (5-ethynyluridine), 5moU (5-methoxyuridine), A-to-I (adenosine-to-inosine editing), C-to-U (cytidine-to-uridine editing), m1Ψ (1-methylpseudouridine), m5U (5-methyluridine), Gm (2’-O-methylguanosine)

• Pore chemistry: RNA002 refers to the SQK-RNA002 kit, RNA004 refers to the SQK-RNA004 kit

• Tools are listed chronologically by year of publication, with multiple entries for the same year sorted alphabetically

Computational tools for Oxford Nanopore DRS data analysis beyond RNA modification detection.

This table summarizes representative computational tools and pipelines for analyzing ONT DRS and related long-read transcriptome data. Tools are organized by functional categories, including basecalling, isoform identification, isoform quantification, poly(A) tail analysis, allele-specific expression analysis, data simulation, fusion gene detection, and integrated data analysis pipelines. For each tool, we provide a brief functional description, GitHub repository, whether it is specifically designed for DRS (DRS-specific), and year of release. While many tools were originally developed for general long-read sequencing, they are widely applied to DRS data.

Comprehensive list of RNA analysis tools for nanopore DRS

Category	Tool	Functional description	GitHub	DRS-specific	Year
Basecalling	Albacore	ONT’s early offline basecaller for nanopore data, converting raw ionic current signals into nucleotide sequences using HMM-based models; now deprecated and replaced by Guppy/Dorado.		No	2017
Basecalling	Guppy	A C++-based closed-source ONT basecaller that replaced Albacore, supporting GPU-accelerated high-accuracy basecalling for DNA and RNA (RNA002 chemistry for DRS).		No	2018
Basecalling	Bonito	An open-source research basecaller developed by ONT based on PyTorch, enabling customizable neural network architectures and training for experimental nanopore basecalling models.	https://github.com/nanoporetech/bonito	No	2020
Basecalling	RODAN	A deep learning–based nanopore basecaller employing advanced neural network architectures to improve raw signal decoding accuracy.	https://github.com/biodlab/RODAN	No	2022
Basecalling	Dorado	ONT’s current production basecaller with GPU acceleration, supporting high-accuracy RNA and DNA basecalling, duplex calling, and integrated modification-aware models for simultaneous sequence and RNA modification detection.	https://github.com/nanoporetech/dorado	No	2023
Basecalling	GCRTcall	A neural network–based nanopore basecaller optimized for improved accuracy and robustness in decoding ionic current signals from ONT sequencing data.	https://github.com/liqingwen98/GCRTcall	No	2024
Basecalling	DEMINERS	A deep learning framework for nanopore signal decoding that enhances basecalling performance and supports modification-aware sequence interpretation.	https://github.com/LuChenLab/DEMINERS	No	2025
Basecalling	Coral	A neural network–based nanopore basecaller designed to improve signal-to-sequence translation accuracy, particularly for challenging signal regions such as homopolymers.	https://github.com/BioinfoSZU/Coral	No	2026
Isoform Identification	StringTie2	Transcript assembly from long-read alignments based on reference genome and annotation (DRS may rely more heavily on high-quality alignment and annotation due to 3′ bias).	https://github.com/gpertea/stringtie	No	2019
Isoform Identification	rnaSPAdes	De novo transcriptome assembly (DRS-compatible but often combined with other read types or strategies) to recover transcripts absent from reference annotations.	https://github.com/ablab/spades	No	2019
Isoform Identification	FLAIR	Post-alignment splice site correction and transcript assembly for ONT long reads (including DRS), mitigating the impact of ONT sequencing errors on splice junction detection.	https://github.com/BrooksLabUCSC/flair	No	2020
Isoform Identification	TALON	Classification and annotation summarization of known and novel transcripts from long reads (including DRS), generating transcript sets suitable for downstream quantification.	https://github.com/dewyman/TALON	No	2020
Isoform Identification	TAMA	Merging, filtering, and annotation refinement tools for long-read transcript structures, supporting the standardization and curation of DRS-derived transcript models.	https://github.com/GenomeRIK/tama	No	2020
Isoform Identification	Pinfish	Nanopore transcriptome toolkits (clustering, error correction, modeling) applicable to DRS reads for transcript candidate generation and structural organization.	https://github.com/nanoporetech/pinfish	No	2020
Isoform Identification	FLAMES	Isoform identification and organization for long-read transcriptomes (including DRS), commonly used to standardize long-read transcript sets for comparative analysis.	https://github.com/LuyiTian/FLAMES	No	2021
Isoform Identification	IsoSplitter	Partitioning and classification of long reads by splicing and structural features, facilitating the dissection of complex isoforms and alternative splicing patterns in DRS data.	https://github.com/Hengfu-Yin/IsoSplitter	No	2021
Isoform Identification	LyRic	Transcript identification and expression analysis tools or pipelines tailored to ONT DRS characteristics (directionality, 3′ signal, poly(A)-related features).	https://github.com/guigolab/LyRic	Yes	2021
Isoform Identification	RATTLE	Reference-free clustering of DRS/ONT reads to generate transcript consensus sequences, enabling isoform reconstruction without reliable reference genomes.	https://github.com/comprna/RATTLE	No	2022
Isoform Identification	Bambu	Reconstruction and merging of transcript structures from long reads (applicable to DRS), with identification of novel isoforms to accommodate uneven full-length coverage in DRS data.	https://github.com/GoekeLab/bambu	No	2023
Isoform Identification	IsoTools	Analytical framework for transcript classification, novel isoform discovery, and splicing event summarization in long-read transcriptomes (including DRS).	https://github.com/MatthiasLienhard/isotools	No	2023
Isoform Identification	Mandalorion	Identification of splice isoforms and generation of transcript models from ONT long reads (including DRS), suitable for rapid isoform catalog generation within the ONT ecosystem.	https://github.com/mandalorion-evidence/mandalorion	No	2023
Isoform Identification	ESPRESSO	Splice site error correction in long reads prior to isoform identification, reducing false splice junctions caused by higher DRS error rates.	https://github.com/Xinglab/espresso	No	2023
Isoform Identification	Freddie	Clustering long reads (including DRS) by splicing structure and inferring isoforms under weak or absent annotation, suitable for exploratory DRS datasets.	https://github.com/vpc-ccg/freddie	No	2023
Isoform Identification	RNA-Bloom2	De novo or weak-reference transcript assembly tools that support DRS, useful for constructing transcript sequence sets when annotation is incomplete or reference genomes are limited.	https://github.com/bcgsc/RNA-Bloom	No	2023
Isoform Identification	isONform	Reference-free clustering and reconstruction of ONT transcripts (including DRS), improving structural consistency under high sequencing error rates.	https://github.com/ksahlin/isONform	No	2023
Isoform Identification	rMATS-long	Differential isoform analysis between sample groups, as well as classification and visualization of isoform structure and abundance.	https://github.com/Xinglab/rMATS-long	No	2023
Isoform Identification	Isosceles	ONT-oriented isoform clustering and integration approaches, suitable for consolidating DRS reads into high-confidence transcript structure sets.	https://github.com/Genentech/Isosceles	No	2024
Isoform Identification	isoSeQL	Database tool for comparing across many Iso-Seq runs analyzed through SQANTI3.	https://github.com/christine-liu/isoSeQL	No	2025
Isoform Quantification	Salmon	Rapid transcript expression estimation (for long reads/DRS, requiring attention to 3′ bias and alignment/indexing strategies).	https://github.com/COMBINE-lab/salmon	No	2015
Isoform Quantification	FLAIR	Isoform-level counting of DRS reads based on corrected splice sites and transcript sets, reducing the impact of mismatches and mis-splicing on quantification.	https://github.com/BrooksLabUCSC/flair	No	2020
Isoform Quantification	TALON	Classification and counting of DRS reads within a known/novel transcript framework, producing consistent transcript expression matrices.	https://github.com/dewyman/TALON	No	2020
Isoform Quantification	tappAS	Functional interpretation based on isoform-level expression (DRS-derived transcript expression can inform differences in protein domains, UTRs, etc.).	https://github.com/ConesaLab/tappAS	No	2020
Isoform Quantification	HBA-DEALS	Joint modeling of gene expression and isoform usage changes, suitable for differential analysis using DRS-derived transcript counts.	https://github.com/TheJacksonLaboratory/HBA-DEALS	No	2020
Isoform Quantification	NanoCount	Direct assignment of ONT/DRS reads to transcripts or genes based on alignment results for baseline long-read quantification.	https://github.com/a-slide/NanoCount	No	2021
Isoform Quantification	LIQA	Estimation of isoform expression and detection of differential isoform usage, leveraging DRS long reads to distinguish closely related isoforms.	https://github.com/WGLab/LIQA	No	2021
Isoform Quantification	ScisorseqR	Isoform usage analysis frameworks for single-cell long-read data (adaptable if DRS is extended to single-cell or low-input scenarios).	https://github.com/noush-joglekar/scisorseqr	No	2021
Isoform Quantification	HTSeq	Feature-based counting from DRS alignments to generate count matrices for differential expression or differential usage analysis.	https://github.com/htseq/htseq	No	2022
Isoform Quantification	TAGET	Integration of long-read (including DRS) transcript annotation and quantification outputs for isoform-level expression and splicing analysis.	https://github.com/XiDsLab/TAGET	No	2022
Isoform Quantification	Bambu	Simultaneous transcript construction/integration and isoform quantification from DRS, suitable when unannotated transcripts are present.	https://github.com/GoekeLab/bambu	No	2023
Isoform Quantification	IsoQuant	Transcript identification and quantification from long reads (including DRS) with consistency assessment, suitable for robust estimation under uneven full-length coverage.	https://github.com/ablab/IsoQuant	No	2023
Isoform Quantification	IsoTools	Quantification of DRS/long-read transcripts with support for splicing and isoform usage change summarization.	https://github.com/MatthiasLienhard/isotools	No	2023
Isoform Quantification	Mandalorion	Integrated ONT workflow outputs of isoform counts and expression estimates, serving as unified solutions for DRS isoform quantification.	https://github.com/christopher-vollmers/Mandalorion	No	2023
Isoform Quantification	DELongSeq	Long-read–oriented differential transcript and differential splicing analysis methods, using DRS isoform counts as input.	https://github.com/WGLab/DELongSeq	No	2023
Isoform Quantification	Isosceles	Isoform-level expression estimation within the ONT ecosystem, consolidating DRS reads at the transcript level.	https://github.com/Genentech/Isosceles	No	2024
Isoform Quantification	miniQuant	Rapid transcript- or gene-level counting from long reads (including DRS), aligned with the “read-as-molecule” direct counting paradigm of DRS.	https://github.com/Augroup/miniQuant	No	2025
Isoform Quantification	lr-kallisto	Long-read–oriented transcript quantification applicable to DRS, though sensitive to read errors and coverage bias; often used as a comparison method.	https://github.com/pachterlab/kallisto	No	2025
Poly(A) Tail Analysis	Nanopolish	Signal-level analysis of Oxford Nanopore sequencing data. Can calculate improved consensus sequence, detect base modifications, call SNPs and indels with respect to a reference genome.	https://github.com/jts/nanopolish	No	2019
Poly(A) Tail Analysis	tailfindr	R package for estimating poly(A)-tail lengths in Oxford Nanopore reads.	https://github.com/adnaniazi/tailfindr	No	2019
Poly(A) Tail Analysis	PolyAcaller	Predicate potential poly(A) regions from Nanopore fast5 file which has been basecalled by guppy with flipflop mode.	https://github.com/zhailab/polyACaller	No	2020
Poly(A) Tail Analysis	PolyaID	Convolutional neural network models that predict the classification and cleavage profile surrounding a putative polyA site.	https://github.com/zhejilab/PolyaModelsHuman	No	2023
Poly(A) Tail Analysis	modr	Estimate known and novel transcript expression, predict poly-A tail lengths, and detect RNA modifications in Oxford Nanopore direct RNA-sequencing data like no pipeline before!	https://github.com/OpenOmics/modr	No	2023
Poly(A) Tail Analysis	PT_identify_polyA_sites	Python-based tool designed to identify and analyze poly(A) sites in RNA-seq data.	https://github.com/bartongroup/PT_identify_polyA_sites	No	2024
Poly(A) Tail Analysis	NanoTail	A set of functions to manipulate and analyze data coming from polyA lengths estimations done using Oxford Nanopore Direct RNA sequencing and Nanopolish software.	https://github.com/smaegol/nanotail	No	2024
Poly(A) Tail Analysis	TAILcaller	R package for analyzing polyA tails after dorado basecalling.	https://github.com/Mordziarz/TAILcaller	No	2025
Poly(A) Tail Analysis	BoostNano	A tool for preprocessing ONT-Nanopore RNA sequencing reads (before basecalling), it segments/trims the adapter, polyA stall, and the transcription from the raw signal before basecalling.	https://github.com/haotianteng/BoostNano	No	2025
Poly(A) Tail Analysis	Ninetails	An R package for finding non-adenosine poly(A) residues in Oxford Nanopore direct RNA sequencing reads.	https://github.com/LRB-IIMCB/ninetails	No	2025
Allele-Specific Expression Analysis	phASER	Estimates haplotype-level expression and allele-specific expression (ASE) based on phased variants (DRS long reads improve connectivity between phased sites and ASE resolution).	https://github.com/secastel/phaser	No	2016
Allele-Specific Expression Analysis	PEPPER	Variant detection component designed for ONT reads (applicable to DRS but requiring consideration of RNA-specific biases), often integrated with phasing and genotyping workflows.	https://github.com/kishwarshafin/pepper	No	2021
Allele-Specific Expression Analysis	LORALS	Performs allele-specific expression and allele-specific splicing analysis using long reads, enabling direct association of haplotypes with transcripts through DRS data.	https://github.com/LappalainenLab/lorals	No	2022
Allele-Specific Expression Analysis	WhatsHap	Uses long reads (including DRS) spanning multiple heterozygous sites to perform phasing inference, providing the foundation for haplotype-specific and allele-specific expression analysis in DRS.	https://github.com/whatshap/whatshap	No	2023
Allele-Specific Expression Analysis	RPVG	Aligns RNA reads to variation graphs or pangenomes and estimates allele-specific expression, suitable for reducing reference bias in highly polymorphic regions when using DRS.	https://github.com/jonassibbesen/rpvg	No	2023
Allele-Specific Expression Analysis	IsoLaser	Integrates long-read splicing structures with variant information to infer allele-specific expression and splicing (the long span of DRS facilitates linking variants to isoforms).	https://github.com/gxiaolab/isoLASER	No	2025
Allele-Specific Expression Analysis	Clair3-RNA	A small variant caller for long-read RNA sequencing (lrRNA-seq) data.	https://github.com/HKU-BAL/Clair3-RNA	No	2025
Data Simulation	NanoSim	A fast and scalable read simulator that captures the technology-specific features of ONT data, and allows for adjustments upon improvement of nanopore sequencing technology.	https://github.com/bcgsc/NanoSim	No	2017
Data Simulation	DeepSimulator	The first deep learning based Nanopore simulator which can simulate the process of Nanopore sequencing.	https://github.com/liyu95/DeepSimulator	No	2018
Data Simulation	PBSIM3	A simulator for all types of Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) long reads.	https://github.com/yukiteruono/pbsim3	No	2022
Data Simulation	Squigulator	A tool for simulating nanopore raw signal data. Uses traditional pore models and gaussian noise for simulation.	https://github.com/hasindu2008/squigulator	No	2024
Data Simulation	TKSM	A modular software for simulating long-read sequencing.	https://github.com/vpc-ccg/tksm	No	2024
Data Simulation	Seq2squiggle	A deep learning-based tool for generating artifical nanopore signals from DNA sequence data.	https://github.com/ZKI-PH-ImageAnalysis/seq2squiggle	No	2025
Data Simulation	AsaruSim	An automated Nextflow workflow designed for simulating 10x single-cell long read data from the count matrix level to the sequence level. Creates a gold standard dataset for assessment and optimization of single-cell long-read methods.	https://github.com/GenomiqueENS/AsaruSim	No	2025
Fusion Gene Detection	LongGF	A computational algorithm and software tool for fast and accurate detection of gene fusion by long-read transcriptome sequencing.	https://github.com/WGLab/LongGF	No	2020
Fusion Gene Detection	JAFFAL	A multi-step pipeline that takes either raw RNA-Seq reads, or pre-assembled transcripts, then searches for gene fusions.	https://github.com/Oshlack/JAFFA	No	2021
Fusion Gene Detection	FusionSeeker	A gene fusion caller for long-read single-molecular sequencing data.	https://github.com/Maggi-Chen/FusionSeeker	No	2023
Fusion Gene Detection	CTAT-LR-fusion	Find fusion transcripts using minimap2 and FusionInspector for long (and optionally additional short) RNA-seq reads.	https://github.com/TrinityCTAT/CTAT-LR-fusion	No	2023
Data Analysis Pipelines	NanoPipe	A web-based software for analyzing Oxford Nanopore sequencing reads, providing alignments, statistics, and polymorphism information, available online or as a downloadable script for local use.	https://github.com/IOB-Muenster/nanopipe2	No	2019
Data Analysis Pipelines	MasterOfPores	A pipeline written in Nextflow DSL2 to analyze Nanopore data. It can handle reads from direct RNAseq, cDNAseq, DNAseq etc.	https://github.com/biocorecrg/master_of_pores	No	2020
Data Analysis Pipelines	L-RAPiT	A Cloud-Based Computing Pipeline for the Analysis of Long-Read RNA Sequencing Data.	https://github.com/Theo-Nelson/long-read-sequencing-pipeline	No	2022
Data Analysis Pipelines	FASTdRNA	FastdRNA is a pipeline written in snakemake to handle ONT direct RNA seq database.	https://github.com/Tomcxf/FASTdRNA	Yes	2023
Data Analysis Pipelines	NanoTrans	An integrated computational framework for comprehensive transcriptome analysis with Nanopore direct-RNA sequencing.	https://github.com/yjx1217/NanoTrans	Yes	2024

Note

• DRS-specific: Indicates whether the tool was specifically designed for or particularly suited to Direct RNA Sequencing data. “No” means the tool can be applied to DRS but was not specifically designed for it.

• Basecallers: Tools that convert raw electrical signals from nanopore sequencing into nucleotide sequences.

• Isoform identification: Tools for reconstructing and identifying transcript isoforms from long-read sequencing data.

• Isoform quantification: Tools for estimating expression levels of transcript isoforms.

• Poly(A) tail analysis: Tools for analyzing poly(A) tail lengths and characteristics from nanopore RNA data.

• Allele-specific expression analysis: Tools for detecting and quantifying expression from specific alleles or haplotypes.

• Data simulation: Tools for generating simulated nanopore sequencing data for benchmarking and development.

• Fusion gene detection: Tools for identifying gene fusion events from long-read transcriptome data.

• Data analysis pipelines: Integrated workflows that combine multiple analysis steps for comprehensive data processing.