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pRNASeqTools

Integrated High-throughput Sequencing Data Analysis for Plant — Python3 rewrite of the original Perl pipeline.

Author: Dr. Chenjiang You
Python rewrite: DeepSeek

Overview

pRNASeqTools is a comprehensive NGS data analysis pipeline for plant genomics. It provides 16 analysis modes covering small RNA, mRNA, epigenomics, ribosome profiling, and more — all accessible through a single command-line interface.

Analysis Modes

Mode Description Key Tools
srna Small RNA-seq ShortStack, bowtie
mrna mRNA-seq (DE analysis) STAR, featureCounts, DESeq2
degradome Degradome-seq (PARE/GMUCT) STAR, sPARTA, riboWaltz
phasi phasiRNA analysis ShortStack
tt Truncation/tailing ShortStack
ribo Ribo-seq (RIBO Taper) Bowtie2, STAR, StringTie, RSEM, RiboTaper
cips CiPS uORF analysis ORFik, GenomicFeatures (R)
chip ChIP-seq bowtie2, Genrich / MACS3, deepTools
atac ATAC-seq bowtie2, Genrich / MACS3, deepTools
wgbs Whole-genome bisulfite Bismark, DMRcaller
clip CLIP-seq STAR, CLIPper
ts TS-CLIP-seq STAR, CLIPper
ribometh RiboMeth-seq STAR, RNAmodR
risi risiRNA analysis ShortStack
tf Two-factor DE DESeq2

Quick Start

1. Install dependencies

# Create conda environment
conda env create -f environment.yaml
conda activate prnaseqtools

# Install R packages from Bioconductor/GitHub
Rscript scripts/checkPackages.R

2. Prepare reference files

Place reference genome files in reference/:

reference/
├── ath_genes.gff          # Gene annotation (GFF3)
├── ath_chr_all.fasta      # Genome sequence
├── ath_chr_all.fasta.fai  # FASTA index
├── ath_miRNA_miRNA_star.gff  # miRNA annotation
└── ...

Supported genomes: ath (Arabidopsis), osa (Rice), b73 (Maize), gma (Soybean), smo, bra, w22

3. Run analysis

# Small RNA-seq
python pRNASeqTools_run.py srna -c "WT=data/WT.fq" -p "mut=data/mut.fq"

# mRNA-seq differential expression
python pRNASeqTools_run.py mrna -c "WT=SRR111111" -t "mut=SRR222222"

# Ribo-seq (RIBO Taper pipeline)
python pRNASeqTools_run.py ribo \
  --rna-control "WT=SRR111111" \
  --ribo-control "Ribo=SRR333333" \
  --contam "rRNA.fasta,tRNA.fasta,snRNA.fasta" \
  --ribotaper ~/RiboTaper_v1.3

# ChIP-seq (Genrich, default)
python pRNASeqTools_run.py chip --treatment "IP=data/ip.bam" --control "Input=data/input.bam"
# ChIP-seq (MACS3 + bdgdiff)
python pRNASeqTools_run.py chip --peak-caller macs3 --genome-size 1.35e8 \
  --treatment "IP=data/ip.bam" --control "Input=data/input.bam"

RIBO Taper Pipeline (ribo mode)

The ribo mode implements a full RIBO Taper workflow for translated ORF detection:

Step 1 — Bowtie2 contamination removal (rRNA, tRNA, snRNA, snoRNA)
Step 2 — Preprocess Ribo-seq reads
Step 3 — RNA-seq STAR 2-pass + StringTie transcriptome assembly
Step 4 — gffcompare → novel transcript filtering + gene_biotype
Step 5 — RSEM quantification → expressed isoform filtering (TPM)
Step 6 — STAR re-mapping with expressed annotation
Step 7 — RIBO Taper annotation files
Step 8 — Merge BAMs + Ribotaper.sh ORF detection
Step 9 — Output summary

Additional dependency: RiboTaper (manual install)

git clone https://github.com/hsinyenwu/RiboTaper.git ~/RiboTaper_v1.3

RiboTaper CLI options

Option Default Description
--rna-control (required) RNA-seq control: name=file1+file2...
--ribo-control (required) Ribo-seq control: name=file1+file2...
--contam (required) Contamination fasta for Bowtie2 index
--ribo-len 24,25,26,27,28 Ribo-seq read lengths
--cutoffs 8,9,10,11,12 RIBO Taper cutoffs
--tpm-threshold 0 Mean TPM threshold for isoform filtering
--ribotaper (auto-detect) Path to RIBO Taper installation

CiPS uORF Analysis (cips mode)

Downstream analysis after RIBO Taper: identifies translated upstream ORFs (uORFs) in 5' UTRs using Ribo-seq P-site periodicity.

Input: GTF + FASTA + P-site counts
  → Extract 5' UTRs
  → Find all uORFs (ORFik)
  → Compute frame-specific P-site counts (parallel)
  → Filter translated uORFs (minimum: 1aa, longer: ≥2aa)
  → Deduplicate overlapping uORFs
Output: 4 Excel files

CiPS CLI options

Option Default Description
--gtf <genome>_expressed.gtf Expressed GTF from ribo pipeline
--fasta reference/<genome>_chr_all.fasta Reference genome
--psite (required) P-site count file
--min-inframe-counts 10 Min in-frame Ribo-seq counts
--min-inframe-perc 50 Min in-frame percentage
--min-psite-perc 30 Min P-site % (longer uORFs)
--gene-desc (none) Gene description Excel

ChIP-seq / ATAC-seq (chip / atac modes)

Both modes support Genrich (default) and MACS3 peak calling, plus MACS3 bdgdiff for differential peak analysis between two conditions.

ChIP-seq with MACS3 + bdgdiff

bam → macs3 callpeak (per group, with Input) → macs3 bdgdiff → diff peaks

ATAC-seq with MACS3 + bdgdiff

bam → macs3 callpeak (per group, ATAC-specific params) → macs3 bdgdiff → diff peaks

Peak caller CLI options (chip)

Option Default Description
--peak-caller genrich genrich or macs3
--genome-size (required for macs3) Effective genome size (e.g. 1.35e8 for ath)
--control2 (none) Second Input for bdgdiff
--treatment2 (none) Second IP for bdgdiff

Peak caller CLI options (atac)

Option Default Description
--peak-caller genrich genrich or macs3
--genome-size (required for macs3) Effective genome size
--treatment2 (none) Second ATAC group for bdgdiff

bdgdiff example

# ChIP-seq: two-group differential peaks
pRNASeqTools chip --peak-caller macs3 --genome-size 1.35e8 \
  --control  "Input_WT=SRR111"  --treatment  "IP_WT=SRR222" \
  --control2 "Input_mut=SRR333" --treatment2 "IP_mut=SRR444"

# ATAC-seq: two-group differential peaks
pRNASeqTools atac --peak-caller macs3 --genome-size 1.35e8 \
  --treatment "ATAC_WT=SRR111" --treatment2 "ATAC_mut=SRR222"

Outputs:

  • {tag}_peaks.narrowPeak — per-group peaks
  • diff_{g1}_vs_{g2}_cond1.bed — condition 1-specific
  • diff_{g1}_vs_{g2}_cond2.bed — condition 2-specific
  • diff_{g1}_vs_{g2}_common.bed — shared peaks

Common Options (all modes)

Option Description
--outdir, -o Output directory (default: ./out)
--genome, -g Genome: ath, osa, b73, gma, smo, bra, w22
--thread, -t Number of threads (default: 4)
--adaptor, -a 3' adaptor sequence
--control, -c Control samples: name=file1+file2...
--treatment, -p Treatment samples

Dependencies

Core tools (via conda)

Aligners: STAR, bowtie, bowtie2, Bismark, ShortStack
Processing: cutadapt, samtools, bedtools, gffread, deepTools
Counting: featureCounts (subread), RSEM, StringTie
Peak calling: Genrich, MACS3
SRA: sra-tools
R: r-base, DESeq2, DMRcaller, pheatmap, dplyr, devtools

Mode-specific R packages

Package Mode Source
riboWaltz degradome GitHub
RNAmodR.RiboMethSeq ribometh Bioconductor
NMF — GitHub
Seurat srna (sc) GitHub
ORFik cips Bioconductor

Manual install

  • CLIPper — clip / ts modes: pip install clipper
  • RiboTaper — ribo mode: git clone https://github.com/hsinyenwu/RiboTaper.git
  • Reference files — contact the author

Input Format

Samples are specified as name=source pairs. Sources can be:

  • SRA accessions: WT=SRR123456 (auto-download via fasterq-dump)
  • Local files: WT=data/sample.fq or WT=data/sample.fq.gz
  • Multiple replicates: WT=rep1.fq+rep2.fq+rep3.fq
  • Paired-end: handled automatically for SRA accessions

Project Structure

pRNASeqTools_py/
├── pRNASeqTools_run.py        # Entry point
├── environment.yaml           # Conda environment
├── prnaseqtools/
│   ├── cli.py                 # CLI definition (argparse)
│   ├── auto_install.py        # Dependency auto-installer
│   ├── validate_options.py    # Input validation
│   ├── input_parser.py        # Sample specification parser
│   ├── functions.py           # Utilities (download, unzip, revcomp)
│   ├── reference.py           # Genome reference handling
│   ├── precheck.py            # Dependency checker
│   ├── logging_setup.py       # Logging
│   └── modes/                 # Analysis mode modules
│       ├── srna.py
│       ├── mrna.py
│       ├── degradome.py
│       ├── phasi.py
│       ├── tt.py
│       ├── ribo.py            # RIBO Taper pipeline
│       ├── cips.py            # CiPS uORF analysis
│       ├── chip.py
│       ├── atac.py
│       ├── wgbs.py
│       ├── clip.py
│       ├── ts.py
│       ├── ribometh.py
│       ├── risi.py
│       └── tf.py
├── scripts/                   # R analysis scripts
│   ├── checkPackages.R
│   ├── ribo.R
│   ├── ribotaper_filter_gtf.R
│   ├── ribotaper_filter_rsem.R
│   ├── cips_uORF.R
│   └── ...
└── reference/                 # Genome files (not versioned)

License

Contact the author for licensing information.

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