rectify-rna 2.2.0

Unified RNA 3' End Correction Framework for poly(A)-tailed RNA sequencing

RECTIFY: Unified RNA 3' End Correction Framework

Correct poly(A) tail artifacts in RNA 3' end sequencing data with a single command.

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Quick Start

Install

pip install rectify-rna

Run

# Simplest: FASTQ input with bundled yeast genome (no external files needed!)
rectify correct reads.fastq.gz --organism yeast -o corrected.tsv

# All-in-one: correct + analyze (uses bundled WT NET-seq for yeast by default)
rectify run reads.bam --genome genome.fa --annotation genes.gtf --output-dir results/

New in v2.2.0: RECTIFY now includes:

• Bundled yeast genome (S288C R64-5-1) and GFF annotations from SGD
• Pre-processed WT NET-seq data (Churchman lab)
• FASTQ support with automatic minimap2 alignment
• CPA motif database for transcription factor binding site matching

Or run steps separately / use custom NET-seq data

# 1. Correct 3' end positions (bundled WT NET-seq for yeast)
rectify correct reads.bam --genome genome.fa --organism yeast --output corrected.tsv

# With custom/mutant NET-seq data (overrides bundled WT data)
rectify correct reads.bam --genome genome.fa --netseq-dir my_mutant_netseq/ --output corrected.tsv

# 2. Analyze results (clustering, differential expression, GO enrichment, enriched motifs around cluster peaks)
rectify analyze corrected.tsv --annotation genes.gtf --output-dir results/

# With explicit sample metadata (optional - conditions auto-inferred from sample names like WT_rep1, KO_rep2)
rectify analyze corrected.tsv --annotation genes.gtf --manifest samples.tsv --reference WT --output-dir results/

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What You Get

Corrected 3' End Positions

Each read gets a corrected position with confidence scores and QC metrics:

┌───────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  read_id   │ chrom │ strand │ original_3prime │ corrected_3prime │ shift │ confidence │ polya_length │ qc_flags │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│  read001   │ chrI  │   +    │     147592      │     147585       │  -7   │    HIGH    │      42      │   PASS   │
│  read002   │ chrI  │   +    │     147594      │     147591       │  -3   │   MEDIUM   │      38      │   PASS   │
│  read003   │ chrI  │   -    │     147602      │     147602       │   0   │    HIGH    │      55      │   PASS   │
│  read004   │ chrII │   +    │     283109      │     283104       │  -5   │    LOW     │      31      │ AG_RICH  │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Key fields:

• original_3prime: Mapped 3' end position from aligner
• corrected_3prime: True CPA site (shifted upstream to first non-A)
• shift: Correction applied (negative = shifted upstream through A-tract)
• confidence: HIGH (single peak), MEDIUM (dominant peak), SPLIT (multiple peaks), LOW (uncertain)
• polya_length: Measured poly(A) tail length (aligned + soft-clipped A's)
• qc_flags: PASS, AG_RICH (possible mispriming), or other warnings

Processing Statistics

Comprehensive QC report showing read flow through each correction stage:

┌─────────────────────────────────────────────────────────────────────────────┐
│  RECTIFY Processing Summary                                                 │
├─────────────────────────────────────────────────────────────────────────────┤
│  Total reads processed         │  993,000   │  99.3%                        │
│  Reads with poly(A) detected   │  890,000   │  89.6%                        │
│  Positions corrected           │  180,000   │  18.1%                        │
│  Mean poly(A) length           │     42.3 bp                                │
├─────────────────────────────────────────────────────────────────────────────┤
│  Confidence Distribution                                                    │
│    HIGH                        │  750,000   │  ████████████████████  75.5%  │
│    MEDIUM                      │  200,000   │  █████                 20.1%  │
│    LOW                         │   43,000   │  █                      4.3%  │
└─────────────────────────────────────────────────────────────────────────────┘

Analysis Outputs (rectify analyze)

Cluster-level counts for differential expression:

┌────────────────────────────────────────────────────────────────────────────────────────┐
│  cluster_id  │  gene   │ chrom │ strand │  start  │   end   │ sample_A │ sample_B │ ... │
├────────────────────────────────────────────────────────────────────────────────────────┤
│  cluster_1   │  ACT1   │ chrVI │   +    │ 54696   │ 54702   │   1523   │   1891   │     │
│  cluster_2   │  ACT1   │ chrVI │   +    │ 54715   │ 54718   │    342   │    128   │     │
│  cluster_3   │  PGK1   │ chrIII│   -    │ 137218  │ 137225  │   2104   │   2087   │     │
└────────────────────────────────────────────────────────────────────────────────────────┘

DESeq2 differential expression (gene and cluster level):

┌──────────────────────────────────────────────────────────────────────────────────────┐
│   gene   │ baseMean │ log2FoldChange │  lfcSE  │  stat   │  pvalue  │    padj     │
├──────────────────────────────────────────────────────────────────────────────────────┤
│  RPL3    │  8542.3  │     1.82       │  0.12   │  15.2   │ 2.1e-52  │  4.8e-49    │
│  HSP82   │  3201.7  │    -2.14       │  0.18   │ -11.9   │ 1.3e-32  │  1.5e-29    │
│  ENO2    │  5876.2  │     0.92       │  0.09   │  10.2   │ 1.8e-24  │  1.4e-21    │
└──────────────────────────────────────────────────────────────────────────────────────┘

3' UTR shift analysis (alternative polyadenylation):

┌──────────────────────────────────────────────────────────────────────────────────────────┐
│   gene   │ proximal_cluster │ distal_cluster │ shift_score │ direction │    padj     │
├──────────────────────────────────────────────────────────────────────────────────────────┤
│  FAS1    │    cluster_12    │   cluster_14   │    0.42     │  SHORTEN  │  3.2e-08    │
│  CDC19   │    cluster_28    │   cluster_31   │   -0.38     │  LENGTHEN │  1.7e-06    │
│  TDH3    │    cluster_45    │   cluster_47   │    0.25     │  SHORTEN  │  4.1e-04    │
└──────────────────────────────────────────────────────────────────────────────────────────┘

Visualization outputs:

• pca_plot.png - Sample clustering and batch effect detection
• heatmap.png - Gene expression heatmap with hierarchical clustering
• shift_browser_*.png - Genome browser plots showing CPA site usage per condition
• motif_results/ - Sequence motifs enriched near CPA sites (MEME format)

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Overview

RECTIFY addresses two fundamental problems affecting RNA 3' end mapping:

1. A-tract Ambiguity (Universal): Genomic A-tracts near true 3' ends create positional uncertainty affecting ALL poly(A)-tailed RNA-seq technologies
2. Technology-Specific Artifacts:
   • AG mispriming: Internal priming on A/G-rich regions (oligo-dT methods)
   • Poly(A) tail alignment: Tail bases align to genomic A-tracts creating systematic shifts (when poly(A) is sequenced)

NET-seq Refinement

For organisms with available NET-seq data, RECTIFY can resolve A-tract ambiguity by using nascent RNA 3' end positions to infer true CPA sites. The bundled WT NET-seq data is auto-detected based on your genome:

Organism	NET-seq Data	Auto-detected from
S. cerevisiae	Churchman lab (bundled)	Genome size ~12 Mb, chrI-XVI
Other eukaryotes	User-provided	Use --netseq-dir

Without NET-seq: RECTIFY still corrects A-tract artifacts and reports ambiguity windows - downstream analysis remains valid, just with wider confidence intervals.

Custom NET-seq: Provide your own NET-seq data (e.g., from mutant strains) with --netseq-dir to override the bundled WT data for condition-specific refinement.

Poly(A) tails cause systematic 3' end mapping errors. When poly(A) tails align to genomic A-tracts, the apparent 3' end shifts downstream. RECTIFY corrects this artifact using NNLS deconvolution:

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Features

Feature	Description
A-tract Correction	Detects genomic A-tracts and calculates ambiguity windows
Poly(A) Measurement	Reports observed tail length (aligned + soft-clipped)
Indel Correction	Fixes alignment artifacts in poly(A) regions
AG Mispriming Detection	Flags likely internal priming events (oligo-dT methods)
NET-seq Refinement	Resolves ambiguity using nascent RNA data (optional)
Proportional Assignment	Splits reads across multiple CPA sites when appropriate
FASTQ Support	Direct FASTQ input with automatic minimap2 alignment
Bundled Genomes	Yeast S288C genome and annotations included
Motif Database	CPA-related TF binding sites for motif matching

Bundled Data (New in v2.2.0)

For yeast (S. cerevisiae), RECTIFY includes:

Data	Description	Size
Genome	S288C R64-5-1 reference	3.7 MB
Annotation	SGD GFF3 with gene names	5.0 MB
GO Annotations	Gene Ontology from SGD	400 KB
NET-seq	WT pan-mutant consensus	~1 MB
Motif Database	CPA factors, NNS pathway, general TFs	10 KB

Supported Technologies

• Nanopore direct RNA-seq (minimap2)
• QuantSeq (oligo-dT short-read)
• Helicos (single-molecule)
• PacBio Iso-Seq
• Any poly(A)-tailed RNA-seq

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Downstream Analysis

RECTIFY includes a comprehensive analyze command:

rectify analyze corrected.tsv --annotation genes.gtf --output-dir results/

Module	Description	Output
Clustering	Group nearby CPA sites	clusters.tsv, count matrices
Differential Expression	DESeq2-based analysis	deseq2_results.tsv
PCA	Sample QC and batch effects	pca_plot.png
Shift Analysis	Condition-specific CPA usage	shift_results.tsv
GO Enrichment	Functional enrichment	go_enrichment.tsv
Motif Discovery	Sequence motifs near CPA sites	motif_results/

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Installation Options

From PyPI (recommended)

pip install rectify-rna

From Conda (coming soon)

# Bioconda (recommended for bioinformatics)
conda install -c conda-forge -c bioconda rectify-rna

# Or with mamba (faster)
mamba install -c conda-forge -c bioconda rectify-rna

From source (development)

git clone https://github.com/k-roy/RECTIFY.git
cd RECTIFY
pip install -e .

Note: This is a beta release. Please report issues at GitHub Issues.

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Command Reference

Basic Usage

# Simplest: FASTQ input with bundled yeast genome
rectify correct reads.fastq.gz --organism yeast -o corrected.tsv

# BAM input with custom genome
rectify correct reads.bam --genome genome.fa --output corrected.tsv

# With annotation (recommended)
rectify correct reads.bam --genome genome.fa --annotation genes.gtf --output corrected.tsv

# Nanopore with NET-seq refinement
rectify correct reads.bam \
  --genome genome.fa \
  --annotation genes.gtf \
  --aligner minimap2 \
  --netseq-dir netseq_bigwigs/ \
  --output corrected.tsv

# QuantSeq (oligo-dT)
rectify correct reads.bam \
  --genome genome.fa \
  --annotation genes.gtf \
  --polya-sequenced \
  --output corrected.tsv

Key Options

Option	Description
input	Input BAM or FASTQ file (FASTQ auto-aligned with minimap2)
--genome	Reference genome FASTA (optional with --organism yeast)
--organism	Use bundled genome/annotation for organism (e.g., yeast)
--annotation	Gene annotation GTF/GFF
--netseq-dir	Directory with NET-seq bigWig files
--aligner	Aligner used: minimap2, star, bowtie2
--polya-sequenced	Poly(A) tail was sequenced (not just primed)
--threads	Number of threads (default: 4)

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How It Works (Technical Details)

Click to expand detailed algorithm description

The Correction Pipeline

RECTIFY corrects 3' end mapping artifacts by walking each read through multiple correction steps:

===============================================================================
STEP 1: RAW ALIGNMENT
===============================================================================

The aligner maps a Nanopore direct RNA read to the genome. The poly(A) tail
is soft-clipped where the genomic A-tract ends, but the aligned region of the tail
contains additional errors due to alignment and sequencing errors.

genome: 5'..CTAGTGACAGTCAAAAAAAA-AAACAAAAGTAAAAAAAAAAAA|CTAGCGATC..3'
                                                       |
                                              CPA site (true 3' end)

read:   5'..CTAGTGACAGTCAAAAAAAATAAA-AAAAA--AAAAAAAAAAAAAAAAAAAAA..
                                 ^      ^^  |___________________|
                              T error  dels   soft-clipped tail

Key observations:
  - Soft-clip boundary placed at mapped 3' end
  - Deletions (-) = aligner attempts to maximize alignment of the poly(A) tail
    at expense of indels
  - 'T' in the poly(A) tail = Nanopore sequencing error in the homopolymer

===============================================================================
STEP 2: INDEL CORRECTION - Walk Backwards to Find True 3' End
===============================================================================

Starting from soft-clip boundary, walk upstream comparing genome vs read
to find the first position where both agree on a non-A base:

genome: ..CTAGTGACAGTCAAAAAAAA-AAACAAAAGTAAAAAAAAAAAA|..
read:   ..CTAGTGACAGTCAAAAAAAATAAA-AAAAA--AAAAAAAAAA.|..
                     ^         ^      ^^
                  AGREE     T error  dels
                  (C=C)    (ignore) (count)

RECTIFY walks back from soft-clip boundary:
  - Skip all A's (ambiguous with poly(A) tail)
  - Ignore deletions where mRNA has the poly(A) tail: 3 bp total
  - Ignore T (likely sequencing error in homopolymer)
  - Find first non-A agreement: 'C' at upstream position
  - Result: ambiguity window spanning the A-tract from upstream C to downstream C

===============================================================================
STEP 3: NET-seq REFINEMENT (Optional)
===============================================================================

For species with NET-seq data, we can resolve the ambiguity window.

NET-seq captures nascent RNA bound to RNA Pol II. Cleavage intermediates
are oligo-adenylated before release, with a distribution of tail lengths:

                           CPA site (cleavage here)
                                  |
                                  v
    ___________                  ✂️
   |  Pol II   |====~~~CGUACGUAG*AAA...     <- oligo-A tail added
   |___________|                 3'            after cleavage

Oligo(A) tail length distribution (typical):
   1A:  ████████████  ~12%
   2A:  ██████████    ~10%
   3A:  █████████     ~9%
   4A:  ████████      ~8%
   5A:  ███████       ~7%
   6A:  ██████        ~6%    mean ~6.6 A's
   7A:  █████         ~5%
   8A:  ████          ~4%
   9A:  ███           ~3%
  10A:  ██            ~2%
  ...decreasing...

When oligo-adenylated intermediates align to a genome with downstream A's,
the oligo-A tail extends the alignment into the genomic A-tract:

                        true CPA
                           |
genome:       ...CGTACGTAG|AAAAAAAA|GTCACC...
                          |^^^^^^^^|
                          genomic A-tract
                                   |
aligned:      ...CGTACGTAG*AAAAAAA      <- oligo-A aligns to genomic A's
                          |<------>|
                           SHIFT (apparent 3' end moves downstream)

This creates a spreading artifact: signal is shifted downstream.

RECTIFY uses NNLS (Non-Negative Least Squares) deconvolution to remove
the spreading artifact and recover true peak positions:

1. Build convolution matrix from oligo(A) PSF (Point-Spread Function)
   - ~54% of signal stays at true position
   - ~46% spreads downstream (mean ~3bp)

2. Solve: observed = A @ true_peaks (regularized NNLS)

3. Assign reads PROPORTIONALLY to deconvolved peaks:
   - If 2 peaks with 70%/30% signal → assign 0.7 and 0.3 reads

Confidence assignment:
  - HIGH:   Single dominant peak (>90% of signal)
  - MEDIUM: Dominant peak (>70% of signal)
  - SPLIT:  Multiple significant peaks (no dominant)
  - LOW:    Weak signal or no NET-seq data

===============================================================================
STEP 4: FINAL OUTPUT (with proportional apportionment)
===============================================================================

When NET-seq reveals multiple peaks within an ambiguity window, reads are
APPORTIONED PROPORTIONALLY to each peak position. This preserves quantitative
accuracy for downstream analysis.

Example: Single dominant peak (HIGH confidence)
-------------------------------------------------
Ambiguity window [31, 42], NET-seq shows single peak at position 35:

                 31              35                          42
                 |               |                           |
Ambiguity:       |===============================================|
NET-seq peak:                    #

Output: read001 → position 35, weight 1.0, confidence HIGH

Example: Multiple peaks (SPLIT confidence)
--------------------------------------------
A SINGLE Nanopore read with ambiguity window [31, 42] can be refined using
NET-seq to reveal THREE distinct CPA sites at positions 33, 36, and 40:

                 31   33   36        40                       42
                 |    |    |         |                        |
Ambiguity:       |==============================================|
NET-seq peaks:        ###  ##        #
                      50%  30%       20%

Output: read001 → split into THREE output rows (one read becomes three):
  - read001 at position 33, weight 0.5
  - read001 at position 36, weight 0.3
  - read001 at position 40, weight 0.2

This preserves quantitative accuracy: if 100 reads map to this ambiguous
region, the output will have ~50 reads at pos 33, ~30 at pos 36, ~20 at pos 40.

Without NET-seq: Reports ambiguity window [31, 42] and uses leftmost
position (most conservative estimate), weight 1.0.

Module Architecture

RECTIFY applies corrections modularly based on your data:

1. Module 1: A-tract Ambiguity (always applied)

   • Identifies genomic A-tracts near 3' ends
   • Calculates ambiguity windows

2. Module 2A: AG Mispriming (when oligo-dT priming used)

   • Screens for downstream AG-richness
   • Flags likely misprimed reads

3. Module 2B: Poly(A) Detection (when poly(A) IS sequenced)

   • Measures observed poly(A) tail length (aligned A's + soft-clipped A's)
   • Reports tail length for QC

4. Module 2C: Indel Correction (when poly(A) IS sequenced)

   • Detects and corrects indel artifacts in aligned region

5. Module 3: NET-seq Refinement (optional)

   • Resolves ambiguity using NET-seq data via NNLS deconvolution
   • Apportions reads PROPORTIONALLY to multiple peaks
   • Assigns confidence scores (HIGH/MEDIUM/SPLIT/LOW)

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Citation

If you use RECTIFY, please cite:

Original RECTIFY (AG mispriming correction):

Roy KR, Chanfreau GF. Robust mapping of polyadenylated and non-polyadenylated RNA 3' ends at nucleotide resolution by 3'-end sequencing. Methods. 2020 Apr 1;176:4-13. doi: 10.1016/j.ymeth.2019.05.016. PMID: 31128237

RECTIFY 2.0 (unified framework):

Manuscript in preparation

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License

MIT License - See LICENSE for details

Contact

• Kevin R. Roy - [e-mail]
• GitHub: k-roy/RECTIFY

Acknowledgments

• Original RECTIFY development supported by Chanfreau Lab, UCLA
• NET-seq data from Churchman Lab, Harvard Medical School