nanornaqtl 1.0.2

A comprehensive toolkit for multi-molecular phenotyping and QTL analysis based on Nanopore direct RNA sequencing data

nanornaqtl

A comprehensive toolkit for multi-molecular phenotyping and QTL analysis based on Nanopore direct RNA sequencing data

Introduction

nanornaqtl is a specialized analysis tool designed for Nanopore direct RNA sequencing data, used to identify various RNA molecular phenotypes and perform population-level QTL (Quantitative Trait Loci) analysis. This tool is particularly suitable for analyzing pooling samples (mixed sequencing of multiple individuals), with all analyses conducted at the read level.

Key Features

• 7 Molecular Phenotype Identification 📊:

  • RNA modification sites: m6A, m5C, pseudouridine (pseU), inosine
  • polyA tail length
  • Intron retention rate
  • Alternative polyA sites (APA)
  • Transcript isoforms

• 8 QTL Analyses 🧪:

  • Modification QTLs (m6A, m5C, pseU, inosine)
  • APA usage pattern QTL
  • Isoform usage pattern QTL
  • polyA tail length QTL
  • Intron retention rate QTL

Features ✨

• Read-level analysis: Suitable for population-level analysis of pooling samples
• Bayesian statistical methods: More robust for unevenly covered Nanopore data
• Parallel processing: Supports multi-threading acceleration, processing by chromosome in parallel
• Flexible parameter settings: Customizable quality thresholds, coverage requirements, etc.

---

Installation

Install via pip 📦

pip install nanornaqtl

Install from source

git clone https://github.com/xinranxu0930/nanornaqtl.git
cd nanornaqtl
poetry install

Dependencies

External tools:

• bedtools (v2.30.0+)
• samtools (v1.15+)
• IsoQuant (for isoform analysis): https://github.com/ablab/IsoQuant

Python packages:

• pysam
• pymc
• arviz
• statsmodels
• pandas
• numpy
• scipy
• logomaker
• biopython

---

Data Preprocessing

Before using nanornaqtl, Nanopore sequencing data needs to be preprocessed.

Step 1: Data Format Conversion

If the sequencing data is in FAST5 format, it needs to be converted to POD5 format first (skip this step if already in POD5):

# Convert using pod5 tool
pod5 convert fast5 input.fast5 --output output.pod5

Step 2: Basecalling and Alignment 🔧

Use Dorado for basecalling and alignment:

Basic alignment (required)

dorado basecaller \
  model_path \
  pod5_directory/ \
  --mm2-opts "-x splice -k 14" \
  > output.bam

Parameter description:

• --mm2-opts "-x splice -k 14": RNA splice alignment parameters

Optional features

If modification site analysis is needed:

dorado basecaller \
  model_path \
  pod5_directory/ \
  --mm2-opts "-x splice -k 14" \
  --modified-bases-models model_name \
  > output.bam

Download modification recognition models:

# Download all models
dorado download --model all

# Or download specific model
dorado download --model dna_r10.4.1_e8.2_400bps_hac@v4.1.0

If polyA tail length analysis is needed:

dorado basecaller \
  model_path \
  pod5_directory/ \
  --mm2-opts "-x splice -k 14" \
  --estimate-poly-a \
  > output.bam

---

Quick Start

Here is a complete analysis workflow example:

# 1. Data preprocessing
nanornaqtl prep \
  -b output.bam \
  -p output_prefix \
  -t 4 \
  -q 0

# 2. Identify m6A modification sites
nanornaqtl pheno m6A \
  -b output_prefix_calls_sorted_map.bam \
  -o result_prefix \
  -t 20 \
  --motif

# 3. Perform m6A QTL analysis
nanornaqtl qtl m6A \
  -b output_prefix_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o qtl_result_prefix \
  -csv result_prefix_m6A_sites_result.csv \
  -pkl result_prefix_m6A_reads_final.pkl \
  --geno_size hg19.chrom.sizes \
  -m m6A \
  --threads 20

---

Detailed Usage Instructions

1. prep - Data Preprocessing

Function: Filter unmapped reads from raw BAM file, generate FASTQ of mapped reads and strand-separated BAM files.

Input:

• Raw BAM file output by Dorado (no preprocessing needed)

Command:

nanornaqtl prep \
  -b <basecall_bam> \
  -p <output_prefix> \
  -t <threads> \
  -q <min_mapq>

Parameter description:

Parameter	Description	Default
-b, --basecall_bam	Input raw BAM file path	Required
-p, --dir_pre	Output file prefix	Required
-t, --threads	Number of threads	4
-q, --min_mapq	Minimum MAPQ threshold	0

Output files:

• <prefix>_calls_sorted_map.bam: Merged BAM of mapped reads
• <prefix>_calls_sorted_map0.bam: Plus strand BAM (flag=0)
• <prefix>_calls_sorted_map16.bam: Minus strand BAM (flag=16)
• <prefix>_calls_sorted_map.fastq: FASTQ of mapped reads
• Corresponding .bai index files

Example:

nanornaqtl prep \
  -b raw_output.bam \
  -p sample01 \
  -t 8 \
  -q 10

---

2. pheno - Molecular Phenotype Identification

2.1 m6A Modification Site Identification

Function: Identify m6A (N6-methyladenosine) modification sites.

Command:

nanornaqtl pheno m6A \
  -b <map_bam> \
  -o <output_prefix> \
  -t <threads> \
  [--motif] \
  [--metaPlotR]

Parameter description:

Parameter	Description	Default
-b, --bam	Input BAM file(*_map.bam)	Required
-o, --output_prefix	Output file prefix	Required
-t, --threads	Number of threads (max effective value 22)	4
-f, --mod_threshold	Modification probability threshold	0.75
-q, --min_qscore	Minimum base quality	10
--min_mapq	Minimum MAPQ	0
-r, --min_rate	Minimum modification rate	0.1
-c, --min_cov	Minimum coverage	5
--motif	Enable motif filtering (DRACH: [GAT][GA]AC[ATC])	False
--metaPlotR	Generate bed file for metaPlotR	False

Output files:

• <prefix>_m6A_sites_result.csv: m6A modification site information

Output column description:

Column	Description
chrom	Chromosome
pos_1base	Position (1-based)
strand	Strand (+/-)
mod_num	Number of modified reads
cov	Total coverage
mod_rate	Modification rate
motif	Motif sequence (if --motif enabled)

Example:

# Using motif filtering
nanornaqtl pheno m6A \
  -b sample01_calls_sorted_map.bam \
  -o sample01_m6A \
  -t 20 \
  --motif \
  --metaPlotR

# Without motif filtering
nanornaqtl pheno m6A \
  -b sample01_calls_sorted_map.bam \
  -o sample01_m6A \
  -t 20

---

2.2 m5C Modification Site Identification

Function: Identify m5C (5-methylcytosine) modification sites.

Command:

nanornaqtl pheno m5C \
  -b <map_bam> \
  -o <output_prefix> \
  -t <threads> \
  [--motif] \
  [--metaPlotR]

Parameters: Same as m6A

Motif classification (if --motif enabled):

• CG: CpG sites
• CHG: CHG sites
• CHH: CHH sites

Output files:

• <prefix>_m5C_sites_result.csv: m5C modification site information

Output column description: Same as m6A, with additional motif_classification column (CG/CHG/CHH)

Example:

nanornaqtl pheno m5C \
  -b sample01_calls_sorted_map.bam \
  -o sample01_m5C \
  -t 20 \
  --motif

---

2.3 Pseudouridine (pseU) Modification Site Identification

Function: Identify pseudouridine modification sites.

Command:

nanornaqtl pheno pseU \
  -b <map_bam> \
  -o <output_prefix> \
  -t <threads> \
  [--motif] \
  [--metaPlotR]

Parameters: Same as m6A

Motif classification (if --motif enabled):

• pus1: [ACT][AG]T
• pus4: GTTC[ATCG]A
• pus7: TGTA[AG]

Output files:

• <prefix>_pseU_sites_result.csv: pseU modification site information

Output column description: Same as m5C, includes motif classification (pus1/pus4/pus7)

Example:

nanornaqtl pheno pseU \
  -b sample01_calls_sorted_map.bam \
  -o sample01_pseU \
  -t 20 \
  --motif

---

2.4 Inosine Modification Site Identification

Function: Identify inosine (A-to-I editing) sites.

Command:

nanornaqtl pheno inosine \
  -b <map_bam> \
  -o <output_prefix> \
  -t <threads> \
  [--motif] \
  [--metaPlotR]

Parameters: Same as m6A

Motif (if --motif enabled): TA[GT]

Output files:

• <prefix>_inosine_sites_result.csv: Inosine site information

Example:

nanornaqtl pheno inosine \
  -b sample01_calls_sorted_map.bam \
  -o sample01_inosine \
  -t 20 \
  --motif

---

2.5 PolyA Tail Length Identification

Function: Extract polyA tail length for each read.

Prerequisites: BAM file must contain pt tag (generated by Dorado basecall with --estimate-poly-a)

Command:

nanornaqtl pheno polyA_tail \
  -b <map_bam> \
  -o <output_prefix> \
  -t <threads>

Parameter description:

Parameter	Description	Default
-b, --bamfile	Input BAM file	Required
-o, --output_prefix	Output prefix	Required
-t, --threads	Number of threads (max effective value 22)	4

Output files:

• <prefix>_polyAlen_result.csv: PolyA tail length information

Output column description:

Column	Description
readID	Read identifier
polyA_length	PolyA tail length

Example:

nanornaqtl pheno polyA_tail \
  -b sample01_calls_sorted_map.bam \
  -o sample01_polyA \
  -t 20

---

2.6 Intron Retention Rate Identification

Function: Calculate intron retention rate for each read.

Command:

nanornaqtl pheno intron_retention \
  -g <gtf_file> \
  -b <map_bam> \
  -o <output_dir> \
  -p <output_prefix>

Parameter description:

Parameter	Description
-g, --gtf	GTF annotation file path
-b, --bam	Input BAM file
-o, --output	Output directory
-p, --output_prefix	Output file prefix

Output files:

• <prefix>_intronRetention_result.csv: Intron retention rate information

Output column description:

Column	Description
readID	Read identifier
intron_retention_rate	Intron retention rate

Example:

nanornaqtl pheno intron_retention \
  -g gencode.v38.annotation.gtf \
  -b sample01_calls_sorted_map.bam \
  -o output_dir \
  -p sample01_IR

---

2.7 Alternative PolyA Site (APA) Identification

Function: Identify alternative polyA sites and classify read usage patterns.

Command:

nanornaqtl pheno APA \
  -g <gtf_file> \
  -b <map_bam> \
  -o <output_dir> \
  -p <output_prefix>

Parameter description:

Parameter	Description
-g, --gtf	GTF annotation file path
-b, --bam	Input BAM file
-o, --output	Output directory
-p, --output_prefix	Output file prefix

Output files:

• <prefix>_APA_result.csv: APA site information

Output column description:

Column	Description
readID	Read identifier
APA_type	APA type classification

Example:

nanornaqtl pheno APA \
  -g gencode.v38.annotation.gtf \
  -b sample01_calls_sorted_map.bam \
  -o output_dir \
  -p sample01_APA

---

3. qtl - QTL Analysis

Prerequisite: All QTL analyses require a SNP information file.

SNP Information File Format

The SNP information file must be a tab-separated text file containing the following columns:

Column	Description	Example
chrom	Chromosome	chr1
pos	Position (1-based)	12345
SNP	SNP identifier	rs123456
A1	Allele 1	A
A2	Allele 2	G

Example:

chrom	pos	SNP	A1	A2
chr1	12345	rs123456	A	G
chr1	67890	rs789012	C	T

---

3.1 m6A QTL Analysis

Function: Identify genetic variants associated with m6A modification.

Command:

nanornaqtl qtl m6A \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -csv <m6A_sites_csv> \
  -pkl <m6A_reads_pkl> \
  --geno_size <genome_size_file> \
  -m m6A \
  --threads <threads>

Parameter description:

Parameter	Description	Default
-b, --bam	Input BAM file	Required
--snp_info	Variant site file	Required
-o, --output_prefix	Output prefix	Required
-csv, --m6A_sites	m6A site result file (*_sites_result.csv)	Required
-pkl, --m6A_reads	m6A read information file (*_reads_final.pkl)	Required
--geno_size	Genome size file	Required
-m, --molecular_type	Molecular phenotype type (m6A)	Required
-q, --min_qscore	Minimum base quality	10
--min_coverage	Minimum coverage	8
--mcmc_samples	MCMC sampling number	1000
--threads	Number of threads	4
--keep_tmp	Keep temporary files	False

Output files:

• <prefix>_m6A_QTLs_result.csv: m6A QTL results

Output column description:

Column	Description
chrom	Chromosome
SNP	Variant ID
snp_pos_1base	SNP position
mod_site_pos_1base	Modification site position
A1, A2	Alleles
MAF	Minor allele frequency
A1_mod, A1_unmod	A1 allele modification counts
A2_mod, A2_unmod	A2 allele modification counts
bayes_factor	Bayes Factor
posterior_prob	Posterior probability
p_fisher	Fisher's exact test p-value

Statistical method description:

1. Bayesian method (recommended):

   • Bayes Factor (BF): Quantifies evidence for association
   • Posterior probability: Probability that null hypothesis is true

2. Frequentist method (reference):

   • Fisher's exact test: More aggressive, higher false positive rate

Note: Output includes all statistical results (significant and non-significant), users can filter based on their own thresholds (e.g., BF > 3).

Example:

nanornaqtl qtl m6A \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_m6A_qtl \
  -csv sample01_m6A_sites_result.csv \
  -pkl sample01_m6A_reads_final.pkl \
  --geno_size hg19.chrom.sizes \
  -m m6A \
  --threads 20 \
  --mcmc_samples 2000

---

3.2 m5C QTL Analysis

Function: Identify genetic variants associated with m5C modification.

Command:

nanornaqtl qtl m5C \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -csv <m5C_sites_csv> \
  -pkl <m5C_reads_pkl> \
  --geno_size <genome_size_file> \
  -m m5C \
  --threads <threads>

Parameters: Same as m6A QTL, change -m parameter to m5C

Output files:

• <prefix>_m5C_QTLs_result.csv: m5C QTL results

Output columns: Same as m6A QTL

Example:

nanornaqtl qtl m5C \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_m5C_qtl \
  -csv sample01_m5C_sites_result.csv \
  -pkl sample01_m5C_reads_final.pkl \
  --geno_size hg19.chrom.sizes \
  -m m5C \
  --threads 20

---

3.3 Pseudouridine (pseU) QTL Analysis

Function: Identify genetic variants associated with pseudouridine modification.

Command:

nanornaqtl qtl pseU \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -csv <pseU_sites_csv> \
  -pkl <pseU_reads_pkl> \
  --geno_size <genome_size_file> \
  -m pseU \
  --threads <threads>

Parameters: Same as m6A QTL, change -m parameter to pseU

Output files:

• <prefix>_pseU_QTLs_result.csv: pseU QTL results

Example:

nanornaqtl qtl pseU \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_pseU_qtl \
  -csv sample01_pseU_sites_result.csv \
  -pkl sample01_pseU_reads_final.pkl \
  --geno_size hg19.chrom.sizes \
  -m pseU \
  --threads 20

---

3.4 Inosine QTL Analysis

Function: Identify genetic variants associated with inosine modification.

Command:

nanornaqtl qtl inosine \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -csv <inosine_sites_csv> \
  -pkl <inosine_reads_pkl> \
  --geno_size <genome_size_file> \
  -m inosine \
  --threads <threads>

Parameters: Same as m6A QTL, change -m parameter to inosine

Output files:

• <prefix>_inosine_QTLs_result.csv: Inosine QTL results

Example:

nanornaqtl qtl inosine \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_inosine_qtl \
  -csv sample01_inosine_sites_result.csv \
  -pkl sample01_inosine_reads_final.pkl \
  --geno_size hg19.chrom.sizes \
  -m inosine \
  --threads 20

---

3.5 APA QTL Analysis

Function: Identify genetic variants affecting alternative polyA site usage.

Command:

nanornaqtl qtl APA \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -f <apa_result_csv> \
  --geno_size <genome_size_file> \
  -m APA \
  -t <threads>

Parameter description:

Parameter	Description	Default
-b, --bam	Input BAM file	Required
--snp_info	Variant site file	Required
-o, --output_prefix	Output prefix	Required
-f, --read_overlap_file	APA result file (*_APA_result.csv)	Required
--geno_size	Genome size file	Required
-m, --molecular_type	Molecular phenotype type (APA)	Required
-q, --min_qscore	Minimum base quality	10
--min_coverage	Minimum coverage	8
--mcmc_samples	MCMC sampling number	1000
-t, --threads	Number of threads	4
--keep_tmp	Keep temporary files	False

Output files:

• <prefix>_APA_QTLs_result.csv: APA QTL results

Output column description:

Column	Description
chrom	Chromosome
SNP	Variant ID
snp_pos_1base	SNP position
A1, A2	Alleles
MAF	Minor allele frequency
BF	Bayes Factor
posterior_prob	Posterior probability
chi2_pvalue	Chi-square test p-value
TVD	Total Variation Distance (range 0-1)
dominant_shift	Direction and magnitude of major APA type usage change

Statistical method description:

1. Bayesian method (recommended): BF and posterior probability
2. Frequentist method (reference): Chi-square test

Effect size metric description:

• TVD (Total Variation Distance): Measures the degree of difference in APA usage distribution between A1 and A2 alleles

  • 0: Two distributions are identical
  • 1: Two distributions are completely different

• dominant_shift: Describes the change in usage proportion of each APA type between A1 and A2

  • Example format: type1:A1↓(-0.56) indicates reads carrying A1 allele use this APA type less frequently (relative decrease of 56%)

Example:

nanornaqtl qtl APA \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_APA_qtl \
  -f sample01_APA_result.csv \
  --geno_size hg19.chrom.sizes \
  -m APA \
  -t 20

---

3.6 Isoform QTL Analysis

Function: Identify genetic variants affecting transcript isoform usage.

Command:

nanornaqtl qtl isoform \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -f <isoquant_output> \
  --geno_size <genome_size_file> \
  -m isoform \
  -t <threads>

Parameter description:

Parameter	Description
-f, --read_overlap_file	IsoQuant output file (OUT.transcript_model_reads.tsv.gz)
Other parameters	Same as APA QTL

Output files:

• <prefix>_isoform_QTLs_result.csv: Isoform QTL results

Output columns: Same as APA QTL, where:

• chi2_pvalue: Chi-square test p-value
• TVD and dominant_shift describe isoform usage pattern differences

Example:

nanornaqtl qtl isoform \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_isoform_qtl \
  -f OUT.transcript_model_reads.tsv.gz \
  --geno_size hg19.chrom.sizes \
  -m isoform \
  -t 20

---

3.7 PolyA Tail Length QTL Analysis

Function: Identify genetic variants affecting polyA tail length.

Command:

nanornaqtl qtl polyA_tail \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -csv <polyA_csv> \
  --geno_size <genome_size_file> \
  --threads <threads>

Parameter description:

Parameter	Description	Default
-b, --bam	Input BAM file	Required
--snp_info	Variant site file	Required
-o, --output_prefix	Output prefix	Required
-csv, --polya_csv	PolyA result file (*_polyAlen_result.csv)	Required
--geno_size	Genome size file	Required
-q, --min_qscore	Minimum base quality	10
--min_coverage	Minimum coverage	8
--mcmc_samples	MCMC sampling number	2000
--threads	Number of threads	4
--keep_tmp	Keep temporary files	False

Output files:

• <prefix>_polyA_tail_length_QTLs_result.csv: PolyA tail length QTL results

Output column description:

Column	Description
chrom	Chromosome
SNP	Variant ID
snp_pos_1base	SNP position
A1, A2	Alleles
MAF	Minor allele frequency
A1_len	PolyA tail length list for A1 allele
A2_len	PolyA tail length list for A2 allele
beta	Effect size (log ratio of mean lengths)
SE	Standard error
KS_stat	Kolmogorov-Smirnov statistic
bayes_factor	Bayes Factor
posterior_prob	Posterior probability
p_welch	Welch's t-test p-value
p_mw	Mann-Whitney U test p-value
p_ks	Kolmogorov-Smirnov test p-value

Frequentist method description (three p-values):

• p_welch: Welch's t-test, suitable for unequal variances
• p_mw: Mann-Whitney U test, non-parametric test, robust to outliers
• p_ks: Kolmogorov-Smirnov test, tests distribution differences

Example:

nanornaqtl qtl polyA_tail \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_polyA_qtl \
  -csv sample01_polyAlen_result.csv \
  --geno_size hg19.chrom.sizes \
  --threads 20 \
  --mcmc_samples 2000

---

3.8 Intron Retention Rate QTL Analysis

Function: Identify genetic variants affecting intron retention rate.

Command:

nanornaqtl qtl intron_retention \
  -b <map_bam> \
  --snp_info <snp_file> \
  -o <output_prefix> \
  -csv <ir_csv> \
  --geno_size <genome_size_file> \
  --threads <threads>

Parameter description:

Parameter	Description	Default
-csv, --ir_csv	Intron retention rate result file (*_intronRetention_result.csv)	Required
Other parameters	Same as polyA tail length QTL

Output files:

• <prefix>_intron_retention_QTLs_result.csv: Intron retention rate QTL results

Output column description:

Column	Description
chrom	Chromosome
SNP	Variant ID
snp_pos_1base	SNP position
A1, A2	Alleles
MAF	Minor allele frequency
A1_IR	Intron retention rate list for A1 allele
A2_IR	Intron retention rate list for A2 allele
beta	Effect size (logit difference)
SE	Standard error
KS_stat	Kolmogorov-Smirnov statistic
bayes_factor	Bayes Factor
posterior_prob	Posterior probability
p_welch	Welch's t-test p-value
p_mw	Mann-Whitney U test p-value
p_ks	Kolmogorov-Smirnov test p-value

Note: Automatically filters low-quality data (e.g., >90% reads with IR=0)

Example:

nanornaqtl qtl intron_retention \
  -b sample01_calls_sorted_map.bam \
  --snp_info snp_info.txt \
  -o sample01_IR_qtl \
  -csv sample01_intronRetention_result.csv \
  --geno_size hg19.chrom.sizes \
  --threads 20

---

Output File Description

File Naming Rules

• prep module: <prefix>_calls_sorted_map[0/16].bam, <prefix>_calls_sorted_map.fastq
• pheno module: <prefix>_<phenotype>_result.csv
• qtl module: <prefix>_<phenotype>_QTLs_result.csv

General Output Format

All CSV output files are standard tab-separated or comma-separated files that can be opened and analyzed using Excel, R, Python, and other tools.

Key Statistical Metrics Interpretation 📈

Bayesian Methods

• BF (Bayes Factor):

  • BF > 3: Substantial evidence supporting association
  • BF > 10: Strong evidence
  • BF > 30: Very strong evidence

• Posterior probability: Posterior probability that null hypothesis is true, smaller values indicate stronger evidence for association

Frequentist Methods

• p-value: Traditional significance test p-value, typically p < 0.05 considered significant
• Note: Frequentist methods may produce more false positives with uneven coverage, Bayesian methods recommended as primary approach

---

Citation

If you use nanornaqtl in your research, please cite our GitHub repository:

nanornaqtl: A comprehensive toolkit for molecular phenotyping and QTL analysis using Nanopore direct RNA sequencing
https://github.com/xinranxu0930/nanornaqtl

---

Contact 📧

• Issue Reporting: GitHub Issues
• Email: [e-mail]
• GitHub: https://github.com/xinranxu0930/nanornaqtl

---

License

This project is licensed under the MIT License. See LICENSE file for details.

---

Changelog

v1.0.0 (2026-01-18)

• Initial release
• Support for 7 molecular phenotype identification methods
• Support for 8 QTL analysis types
• Implementation of Bayesian statistical methods
• Support for multi-threaded parallel processing