rigel-rnaseq 0.3.3

Bayesian RNA-seq transcript quantification tool

Bayesian RNA-seq quantification with joint mRNA, nascent RNA, and genomic DNA modeling

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Overview

Rigel quantifies RNA-seq alignments while explicitly modeling three sources of signal in the same library:

• Mature RNA (mRNA)
• Nascent RNA (nRNA)
• Genomic DNA contamination (gDNA)

The implementation is built around a single-pass native BAM scan plus a locus-level EM solver. A key architectural change in the current codebase is that nRNA is no longer represented as one shadow per transcript. Instead, Rigel builds a global table of unique nRNA spans keyed by (ref, strand, start, end) and shares each nRNA component across transcripts with the same genomic span. This reduces redundant nRNA states in loci with many isoforms that start and end at the same coordinates.

Key features

• Joint mRNA, nRNA, and gDNA quantification in one locus-level model
• Shared-span nRNA architecture with one component per unique genomic span (ref, strand, start, end)
• Single-pass C++ BAM scanner using htslib, with memory-bounded buffering and spill-to-disk support
• Automatic strand-model training from annotated spliced fragments; protocol auto-detection (R1-sense / R1-antisense)
• Aggregate-first gDNA calibration using density, strand balance (Beta-Binomial), and fragment-length signals
• Empirical Bayes priors for nRNA fractions and gDNA rates; calibrated per-locus gDNA initialization
• MAP-EM and Variational Bayes EM (VBEM, default) solver modes with SQUAREM acceleration
• Discrete fragment assignment: fractional, map, or sample (default) post-EM assignment modes
• Parallel BAM scanning and parallel locus EM controlled through one --threads setting
• Feather and TSV outputs plus optional annotated BAM output with per-fragment assignment tags

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Installation

Bioconda

conda install -c conda-forge -c bioconda rigel

PyPI

pip install rigel-rnaseq

The PyPI package name is rigel-rnaseq because rigel is already taken on PyPI. The import name and CLI stay rigel.

From source

git clone https://github.com/mkiyer/rigel.git
cd rigel

mamba env create -f mamba_env.yaml
conda activate rigel

pip install --no-build-isolation -e .

Requirements

• Python 3.12+
• C++17-capable compiler
• Runtime dependencies: numpy, pandas, pyarrow, pysam, pyyaml

On macOS, install Xcode Command Line Tools first:

xcode-select --install

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Quick start

1. Build an index

rigel index \
    --fasta genome.fa \
    --gtf annotation.gtf \
    -o index/

The FASTA must have a .fai index. If needed:

samtools faidx genome.fa

2. Quantify a BAM

rigel quant \
    --bam sample.bam \
    --index index/ \
    -o results/

Input BAM requirements:

• Name-sorted or collated
• NH tag present for multimapper handling
• Splice-junction strand tag available for best strand-model training (XS or ts, or let Rigel auto-detect)

3. Inspect outputs

head results/quant.tsv
head results/gene_quant.tsv
head results/nrna_quant.tsv
head results/loci.tsv
cat results/summary.json

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Output files

File	Description
quant.feather / quant.tsv	Transcript-level abundance table with mrna, nrna, rna_total, tpm, and QC columns
gene_quant.feather / gene_quant.tsv	Gene-level aggregates derived from transcript estimates
nrna_quant.feather / nrna_quant.tsv	nRNA-span-level abundance estimates (one row per unique genomic nRNA span)
loci.feather / loci.tsv	Per-locus EM summary with mrna, nrna, gdna, and gdna_init
summary.json	Library protocol, strand specificity, fragment-length histograms, calibration results, alignment counts, and global quantification totals
config.yaml	Resolved run configuration (parameters, I/O paths). Rerun with rigel quant --config config.yaml
annotated.bam	Optional annotated BAM with ZT, ZG, ZI, ZJ, ZP, ZW, ZC, ZH, ZN, ZS, ZL tags

The nrna values in transcript- and gene-level tables are derived from shared nRNA-span counts that are pro-rated across transcripts sharing the same span.

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How it works

Rigel runs in two logical stages.

Architecture

 FASTA + GTF ──▶ Index Build (index.py) ──▶ Feather index files
                                                    │
 BAM file ──────────────────────────────────────────┤
                                                    ▼
                              ┌──────────────────────────────────┐
                              │  Stage 1: BAM Scan & Training    │
                              │  C++: BamScanner → Resolver      │
                              │  Py:  buffer.py, strand_model.py │
                              └──────────────┬───────────────────┘
                                             │ FragmentBuffer + models
                                             ▼
                              ┌──────────────────────────────────┐
                              │  Stage 2: Score & Route          │
                              │  C++: fused_score_buffer         │
                              │  Py:  scan.py → ScoredFragments  │
                              └──────────────┬───────────────────┘
                                             │ CSR arrays
                                             ▼
                              ┌──────────────────────────────────┐
                              │  Stage 3: gDNA Calibration       │
                              │  Py:  calibration.py, locus.py   │
                              └──────────────┬───────────────────┘
                                             │ per-locus γ (gDNA fraction)
                                             ▼
                              ┌──────────────────────────────────┐
                              │  Stage 4: Locus-Level EM         │
                              │  C++: batch_locus_em (SQUAREM)   │
                              │  Py:  estimator.py dispatch      │
                              └──────────────┬───────────────────┘
                                             │ posterior counts
                                             ▼
                              ┌──────────────────────────────────┐
                              │  Stage 5: Output                 │
                              │  Py:  cli.py → Feather/TSV/JSON  │
                              └──────────────────────────────────┘

BAM scan and model training

A native scanner reads the BAM once, resolves fragments against the indexed annotation, classifies splice structure, trains strand and fragment-length models, and writes resolved fragment data into a columnar buffer.

The main strand model is trained from annotated spliced fragments with unambiguous gene assignment. Diagnostic exonic and intergenic strand models are also retained for reporting, but gDNA itself is always scored with strand probability 0.5.

gDNA calibration

Before per-locus EM, Rigel runs an aggregate-first calibration pass over genomic regions. Each region is classified using three signals: fragment density (Gaussian mixture), strand balance (Beta-Binomial with shared κ), and fragment length (separate RNA and gDNA models). Any region with spliced fragments is forced to zero gDNA probability. The resulting per-region posteriors (γ) are fragment-count-weighted to the locus level and used to initialize the gDNA component in the EM.

Locus-level EM

Ambiguous fragments are routed into CSR form and grouped into connected components of overlapping transcripts. For a locus with T transcripts and N unique nRNA spans, Rigel solves a T + N + 1 component problem:

• T mRNA components
• N shared nRNA components
• 1 merged gDNA component for the locus

The solver runs VBEM (default) or MAP-EM with SQUAREM acceleration. A tripartite prior (coverage-weighted OVR for mRNA, sparsifying Dirichlet for nRNA, calibrated γ for gDNA) is applied. Post-EM fragments are assigned using the configured assignment mode (sample by default).

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Documentation

Document	Description
docs/MANUAL.md	CLI reference, parameter defaults, configuration rules, and output schema
docs/METHODS.md	Algorithmic description of the implemented model and priors
docs/PUBLISHING.md	Release workflow for PyPI and Bioconda
docs/PARAMETERS.md	Complete parameter reference with defaults and config dataclass mapping

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Citing Rigel

If you use Rigel in research, cite the repository for now:

Iyer MK. Rigel: Bayesian RNA-seq quantification with joint mRNA, nascent RNA, and genomic DNA modeling. 2026. https://github.com/mkiyer/rigel

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License

Rigel is distributed under the GNU General Public License v3.0.

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Development

pytest tests/ -v
pytest tests/ --cov=rigel --cov-report=term-missing