REGIS (RNA-seq Guided Identification System) is a comprehensive, modular bioinformatics pipeline designed for the high-confidence identification and functional characterization of Long Non-Coding RNAs (lncRNAs).
Re-engineered in Go, REGIS v1.1.3 brings a premium Terminal User Interface (TUI), robust process management, REST API server, Slurm HPC support, and a seamless developer experience, while maintaining rigorous scientific accuracy.
- 🖥️ Modern TUI: Real-time progress tracking, system resource monitoring (CPU/RAM), and beautiful visualizations using
Bubble Tea. - 🧬 Flexible Analysis: Supports both De Novo (Trinity) and Reference-based (HISAT2/StringTie) assembly modes.
- 🛡️ Robust Quality Control: Integrated FastQC, Trimmomatic, and SortMeRNA (rRNA filtering) for clean data.
- 🎯 High-Confidence Filtering:
- Multi-step coding potential assessment using CPC2 and CPAT.
- Strict length (>200nt) and probability thresholds.
- Classification of Novel Intergenic, Antisense, and Intronic lncRNAs against reference annotations.
- 🔗 Functional Prediction:
- RNAfold for secondary structure prediction.
- LncTar and IntaRNA for lncRNA-mRNA interaction discovery.
- Consensus Analysis to identify high-confidence targets confirmed by multiple tools.
- 🧪 Enrichment Ready: Automatically generates gene lists (Background, Associated, Targets) formatted for enrichment analysis (e.g., getENRICH).
- 📊 Comprehensive Reporting: Generates interactive MultiQC reports, IGV HTML genome browser reports, and a final pipeline summary in JSON/Markdown/HTML.
graph TD
%% Nodes
Input([Input FASTQ]) --> QC[01. FastQC]
QC --> Trim[02. Trimmomatic]
Trim --> Sort{SortMeRNA?}
Sort -- Yes --> Clean([Cleaned Reads])
Sort -- No --> Clean
Clean --> Mode{Analysis Mode}
%% Reference Based Branch
Mode -- Reference --> Align[04. HISAT2 Alignment]
Align --> Assemble1[StringTie Assembly]
%% De Novo Branch
Mode -- De Novo --> Assemble2[04. Trinity Assembly]
%% Convergence
Assemble1 --> Coding[05. CPC2 Coding Potential]
Assemble2 --> Coding
Coding --> CPAT[06. CPAT Validation]
CPAT --> Filter[07. LncRNA Filtering]
%% Functional Analysis
Filter --> Struct[08. RNAfold Structure]
Filter --> Targets{Target Predict?}
Targets -- Yes --> LncTar[09. LncTar]
Targets -- Yes --> IntRNA[10. IntaRNA]
LncTar --> Consensus[11. Consensus Targets]
IntRNA --> Consensus
%% Downstream
Consensus --> Enrich[12. Enrichment Lists]
Filter --> RSeQC[13. RSeQC]
%% Reporting
RSeQC --> IGV[14. IGV Report]
IGV --> MQC[15. MultiQC]
MQC --> Final[16. Final Summary]
%% Styling
style Input fill:#f9f,stroke:#333,stroke-width:2px
style Final fill:#9f9,stroke:#333,stroke-width:2px
style Mode fill:#ff9,stroke:#333,stroke-width:2px
The easiest way to install REGIS and all its dependencies is via Conda/Mamba.
REGIS is available on Anaconda Cloud: 🔗 Anaconda Package Overview
conda create -n regis_env -c bioconda -c conda-forge -c jitendralab python=3.10
conda activate regis_env
conda install jitendralab::regisIf you don't have Conda installed on your system, we highly recommend installing Miniforge (which includes mamba for faster resolving):
# Download the installer (Linux x86_64)
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
# Run the installer
bash Miniforge3-Linux-x86_64.sh
# Restart your terminal or source your .bashrc
source ~/.bashrcIf you prefer to build from source, you will need to manually install dependencies.
- Install Go (v1.21+): Download Go
- External Tools:
Note: CPC2, CPAT, LncTar, and IntaRNA may require manual installation or specific bioconda recipes.
conda create -n regis -c bioconda -c conda-forge \ fastqc trimmomatic sortmerna hisat2 trinity stringtie \ samtools gffcompare seqkit bedtools subread \ python=3.10 rna-seqc multiqc igv-reports sqlite conda activate regis
Warning
Apple Silicon (M1/M2/M3) Users: The sortmerna binary from Bioconda uses AVX2 instructions incompatible with Rosetta 2, causing a crash. Please omit the --sortmerna flag when running REGIS natively on macOS ARM systems, or run the pipeline inside a Linux Docker container.
# Clone the repository
git clone https://github.com/BioinformaticsOnLine/regis.git
cd regis/regis-go
# Build the binary
go build -o regis .
# Verify installation
./regis --helpREGIS offers two modes of operation: Interactive TUI and CLI Arguments.
Simply run regis without any arguments to launch the TUI wizard. It will guide you through setting up your analysis.
./regisFor automated workflows or power users, use command-line flags.
./regis -t paired \
-m reference \
-f1 R1.fastq.gz -f2 R2.fastq.gz \
-r genome.fa \
-g annotation.gtf \
-o results_dir| Flag | Description | Required? |
|---|---|---|
| -t | Data type: single or paired |
✅ |
| -m | Method: denovo or reference |
✅ |
| -f1 | Input file 1 (Forward/Single) | ✅ |
| -f2 | Input file 2 (Reverse, for paired) | Conditional |
| -r | Reference Genome FASTA | Conditional |
| -g | Reference Annotation GTF/GFF | Conditional |
| -o | Output Directory | ✅ |
| -c | CPU Cores (default: auto-detect) | ❌ |
| --sortmerna | Enable rRNA filtering (Highly Recommended) | ❌ |
| --lnctar | Enable LncTar predictions | ❌ |
| --intarna | Enable IntaRNA predictions | ❌ |
| --skip-cpat | Skip CPAT validation (use CPC2 only) | ❌ |
REGIS organizes results into a structured directory tree:
results_dir/
├── 01_fastqc/ # FastQC reports
├── 02_trimmed/ # Cleaned FASTQ files
├── 03_sortmerna/ # rRNA filtered reads (if enabled)
├── 04_alignment/ # HISAT2 BAM files / Trinity Assembly
├── 05_assembly/ # StringTie GTF assemblies
├── 06_cpc2/ # Coding potential results
├── 07_validation/ # CPAT results & Consensus list
├── 08_lncrna_analysis/ # Final LncRNA Characterization
│ ├── filtered/ # Final lncRNA sequences (FASTA/GTF)
│ ├── novel_lncrnas/ # Specifically novel, antisense, intronic transcripts
│ └── expression/ # FeatureCounts expression matrices
├── 11_target_prediction/ # LncTar & IntaRNA results
├── 12_enrichment/ # Gene lists for enrichment (Background vs Targets)
├── 15_multiqc/ # Aggregate QC report
└── 16_pipeline_report/ # FINAL SUMMARY (HTML, JSON, Markdown)
REGIS Team:
- Dr. Jitendra Narayan (Principal Investigator) - University of Namur / CSIR-IGIB
- Dr. Stefano Tiozzo (Principal Investigator) - CNRS-Sorbonne University
- Pranjal Pruthi (Lead Developer & Researcher) - CSIR-IGIB
Funding:
- Supported by The Rockefeller Foundation and CSIR-IGIB.
If you use REGIS in your research, please cite:
REGIS: A Comprehensive RNA-seq Guided Identification System for lncRNA Discovery. [Paper Link]
- Core pipeline implementation
- TUI interface
- REST API
- SLURM support
- Kubernetes support
- Web UI dashboard
- Nextflow DSL2 compatibility
- Cloud execution (AWS Batch, GCP)
REGIS wraps several academic tools. Please also cite:
- LncTar: Li, J., et al. (2015). LncTar: a tool for predicting the RNA targets of long noncoding RNAs. Briefings in Bioinformatics.
- Note: LncTar is included/used under a license restricted to non-commercial genomic research.
- FastQC: Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc
-
HISAT2: Kim, D., Paggi, J.M., Park, C., Bennett, C., & Salzberg, S.L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology, 37, 907-915. https://doi.org/10.1038/s41587-019-0201-4
-
StringTie: Pertea, M., Pertea, G.M., Antonescu, C.M., Chang, T.C., Mendell, J.T., & Salzberg, S.L. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nature Biotechnology, 33, 290-295. https://doi.org/10.1038/nbt.3122
-
Trinity: Grabherr, M.G., et al. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29, 644-652. https://doi.org/10.1038/nbt.1883
-
CPC2: Kang, Y.J., et al. (2017). CPC2: a fast and accurate coding potential assessment tool. Nucleic Acids Research, 45(W1), W12-W16. https://doi.org/10.1093/nar/gkx428
-
CPAT: Wang, L., et al. (2013). CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Research, 41(6), e74. https://doi.org/10.1093/nar/gkt006
- IntaRNA: Mann, M., et al. (2017). IntaRNA 2.0: enhanced and customizable prediction of RNA-RNA interactions. Nucleic Acids Research, 45(W1), W435-W439. https://doi.org/10.1093/nar/gkx279
REGIS is licensed under the GNU General Public License v3.0. See LICENSE for details.