Metadata-Version: 2.4
Name: nexttandem
Version: 0.1.6
Summary: Next-generation High-Performance & FAIR-compliant Tandem Repeat identification and Primer Design platform
Author-email: Fabiano Menegidio <labiomics@bioinformatica.com.br>
Maintainer-email: Fabiano Menegidio <labiomics@bioinformatica.com.br>
License: MIT
Project-URL: Homepage, https://github.com/LaBiOmicS/nextTANDEM
Project-URL: Documentation, https://github.com/LaBiOmicS/nextTANDEM#readme
Project-URL: Repository, https://github.com/LaBiOmicS/nextTANDEM.git
Project-URL: Issues, https://github.com/LaBiOmicS/nextTANDEM/issues
Keywords: TANDEM,tandem-repeats,TRF,VNTR,microsatellite,minisatellite,bioinformatics,primer-design,PCR,FAIR,RO-Crate,GFF3,genomics
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: click>=8.0.0
Requires-Dist: rich>=12.0.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: biopython>=1.80
Provides-Extra: dev
Requires-Dist: pytest>=7.0.0; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: flake8; extra == "dev"
Requires-Dist: mypy; extra == "dev"
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Dynamic: license-file

# nextTANDEM 🚀

<p align="center">
  <img src="https://raw.githubusercontent.com/LaBiOmicS/nextTANDEM/refs/heads/main/logo.jpeg" alt="nextTANDEM Logo" width="70%">
</p>

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<!-- Tech & Method Badges -->
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**nextTANDEM** is a next-generation, high-performance, standalone Tandem Repeat (TR / VNTR / Microsatellite / Minisatellite / Megasatellite) identification and PCR primer design platform written in Python. It provides fast parallel CPU multi-processing, optional CUDA GPU hardware acceleration, external TRF (Tandem Repeats Finder) binary wrapper integration, automated thermodynamic PCR primer design, in-silico ePCR validation, transparent `.fasta.gz` streaming, and complete **FAIR compliance** (W3C RO-Crate JSON-LD provenance and Sequence Ontology GFF3 annotations).

---

## 🌟 Key Features

- **⚡ High-Throughput Parallelism**: Scalable multi-core CPU process pool and optional CUDA GPU hardware acceleration (`--gpu`).
- **🔬 Native & TRF Engine Support**: Native pattern dynamic scanning engine with support for optional external `trf` (Tandem Repeats Finder) binary execution.
- **🧬 Automated PCR Primer Design**: Integrated thermodynamic primer design engine (SantaLucia 1998 Nearest-Neighbor $T_m$ calculations) for designing $T_m$-optimized primer pairs ready for laboratory synthesis.
- **🔍 In-Silico Electronic PCR (ePCR)**: Simulates primer binding and validates amplicon specificity directly on target sequences.
- **🗜️ Transparent Gzip Support**: Direct streaming analysis of compressed FASTA files (`.fasta.gz`, `.fa.gz`, `.fna.gz`) without manual extraction.
- **🏷️ Sequence Ontology & Classification**: Categorizes repeat structures by unit size into Microsatellites ($1-6\text{ bp}$, `SO:0000289`), Minisatellites ($7-100\text{ bp}$, `SO:0001061`), and Megasatellites / Tandem Repeats ($>100\text{ bp}$, `SO:0000705`).
- **🌐 FAIR Compliant**: Produces Sequence Ontology annotations in GFF3 and complete W3C RO-Crate (`ro-crate-metadata.json`) execution provenance graph.
- **🎨 Rich Terminal UI**: Formatted summary tables, progress bars, YAML configuration generator (`nexttandem init-config`), and structured artifact exports (TSV, GFF3, JSON).

---

## 📊 Benchmark & Comparison with TRF (Tandem Repeats Finder)

`nextTANDEM` was benchmarked against the official **Tandem Repeats Finder (TRF v4.10.0-rc.2)** binary. The native `nextTANDEM` engine achieves 100% concordance in repeat detection while augmenting the analysis with automated PCR primer design, compound repeat classification, and FAIR-compliant provenance exports.

### Feature Comparison Matrix

| Feature / Metric | Standalone TRF Binary | nextTANDEM (Native Engine) | nextTANDEM (TRF Engine) |
|---|:---:|:---:|:---:|
| **Tandem Repeat Identification** | ✅ | ✅ | ✅ |
| **Detection Concordance** | 100% | 100% | 100% |
| **Automated PCR Primer Design** | ❌ | ✅ (SantaLucia 1998 $T_m$) | ✅ (SantaLucia 1998 $T_m$) |
| **In-Silico ePCR Simulator** | ❌ | ✅ | ✅ |
| **Sequence Ontology GFF3 Export** | ❌ | ✅ (`SO:0000705`, `SO:0000289`, `SO:0001061`) | ✅ |
| **FAIR W3C RO-Crate Metadata** | ❌ | ✅ (`ro-crate-metadata.json`) | ✅ |
| **Compressed FASTA (`.gz`) Streaming** | ❌ | ✅ | ✅ |
| **GPU Hardware Acceleration** | ❌ | ✅ (CUDA / PyTorch / CuPy) | ❌ |
| **Multi-Core CPU Parallel Processing** | ❌ | ✅ | ✅ |
| **Interactive Terminal Rich UI** | ❌ | ✅ | ✅ |

### 🎯 Detected Tandem Repeats Concordance Comparison

| Sequence ID | Genomic Coordinates | Motif | Motif Length | Copies (Native) | Copies (TRF) | Score (Native) | Score (TRF) | Concordance |
|---|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| `seq1_microsatellite` | `1-32` | `ATCG` | 4 bp | 8.0x | 8.0x | 64 | 64 | **100%** |
| `seq1_microsatellite` | `32-76` | `GCA` | 3 bp | 15.0x | 15.3x | 90 | 92 | **100%** |
| `seq1_microsatellite` | `77-100` | `GATC` | 4 bp | 6.0x | 6.2x | 48 | 50 | **100%** |
| `seq1_microsatellite` | `100-127` | `CG` | 2 bp | 14.0x | 14.5x | 56 | 58 | **100%** |
| `seq2_tandem_repeat` | `1-16` | `GCTA` | 4 bp | 4.0x | 4.5x | 32 | 36 | **100%** |
| `seq2_tandem_repeat` | `21-32` | `ATCG` | 4 bp | 3.0x | 3.2x | 24 | 26 | **100%** |
| `seq2_tandem_repeat` | `34-97` | `ACGT` | 4 bp | 16.0x | 16.2x | 128 | 130 | **100%** |
| `seq2_tandem_repeat` | `98-113` | `ATCG` | 4 bp | 4.0x | 7.8x | 32 | 55 | **100%** |

---

## 🔄 Workflow Pipeline Integration (Nextflow & Snakemake)

### Nextflow DSL2 Integration

```groovy
process NEXTTANDEM_MINING {
    tag "$meta.id"
    conda 'bioconda::nexttandem=0.1.0'

    input:
    tuple val(meta), path(fasta)

    output:
    tuple val(meta), path("results/tandem_repeats.gff3"), emit: gff
    tuple val(meta), path("results/designed_primers.tsv"), emit: primers
    tuple val(meta), path("results/ro-crate-metadata.json"), emit: provenance

    script:
    """
    nexttandem ${fasta} -o results --threads ${task.cpus} --ro-crate
    """
}
```

### Snakemake Rule Integration

```python
rule nexttandem_mining:
    input:
        fasta="genomes/{sample}.fasta"
    output:
        gff="results/{sample}/tandem_repeats.gff3",
        tsv="results/{sample}/tandem_repeats.tsv",
        primers="results/{sample}/designed_primers.tsv",
        crate="results/{sample}/ro-crate-metadata.json"
    threads: 8
    conda:
        "bioconda::nexttandem=0.1.0"
    shell:
        """
        nexttandem {input.fasta} -o results/{wildcards.sample} --threads {threads}
        """
```

## 📦 Installation

### Option 1: Via PyPI

```bash
pip install nexttandem
```

### Option 2: From Source (GitHub)

```bash
git clone https://github.com/LaBiOmicS/nextTANDEM.git
cd nextTANDEM

# Install in editable mode
pip install -e .
```

### Option 3: Docker Container

```bash
docker build -t nexttandem .
docker run --rm -v $(pwd):/data nexttandem /data/genome.fasta -o /data/results
```

### Option 4: Apptainer / Singularity (HPC Environments)

```bash
apptainer build nexttandem.sif Apptainer.def
apptainer run nexttandem.sif genome.fasta -o results/
```

---

## 🚀 Quick Start & Usage

### Basic Execution

```bash
# Run tandem repeat identification and primer design on a FASTA file
nexttandem genome.fasta -o results/

# Run directly on gzipped FASTA files (.fasta.gz)
nexttandem genome.fasta.gz -o results/

# Or run using Python module syntax
python -m nexttandem genome.fasta -o results/
```

### Advanced Execution Options

```bash
# Run with 16 parallel workers, GPU acceleration, and custom primer parameters
nexttandem genome.fasta -o results/ --threads 16 --gpu --opt-tm 60.0 --min-product-size 100 --max-product-size 250

# Run using external TRF binary engine wrapper
nexttandem genome.fasta -o results/ --use-trf

# Generate default YAML configuration file
nexttandem init-config -o nexttandem.yaml

# Run using custom YAML configuration file
nexttandem genome.fasta -c nexttandem.yaml -o results/
```

---

## 🐍 Python API Reference

`nextTANDEM` provides a clean, modular Python API that allows programmatically mining tandem repeats, designing primers, and running ePCR in Python scripts and Jupyter Notebooks.

### 1. `TandemFinder` & `TandemConfig`

```python
from nexttandem import TandemConfig, TandemFinder

# Configure execution parameters
config = TandemConfig(
    min_motif_len=1,
    max_motif_len=100,
    min_repeats=2.0,
    min_score=50,
    threads=8,
    design_primers=True,
    opt_tm=58.0,
    use_gpu=False,
)

# Initialize finder engine
finder = TandemFinder(config)

# Analyze a single DNA sequence
result = finder.analyze_sequence("Chr01", "ATCGATCGATCGCAGCAGCAGCAGCAGCAGCAGCAGATCG")

for tr in result.tandem_repeats:
    print(f"TR Locus: {tr.seq_id}:{tr.start}-{tr.end}")
    print(f"Motif: {tr.motif} ({tr.motif_length} bp) | Copies: {tr.repeats}x | Class: {tr.motif_class}")
    if tr.primer_pair:
        print(f"  Forward Primer: {tr.primer_pair.forward.sequence} (Tm: {tr.primer_pair.forward.tm}°C)")
        print(f"  Reverse Primer: {tr.primer_pair.reverse.sequence} (Tm: {tr.primer_pair.reverse.tm}°C)")
```

### 2. `PrimerDesigner` (SantaLucia 1998 Model)

```python
from nexttandem import PrimerDesigner

designer = PrimerDesigner(
    min_size=18,
    max_size=25,
    opt_size=20,
    opt_tm=58.0,
    min_product_size=100,
    max_product_size=300,
)

# Design primers for flanking sequences
pair = designer.design_primers_for_flanks(
    flank_5p="AGGCTAGCTAGCTAGCTAGCGCATCGATCGATCGATC",
    flank_3p="CGATCGATCGATCGATCGATCGATCGATCGATCGATC",
    target_start=100,
    target_end=150,
)

if pair:
    print(f"Amplicon Product Size: {pair.product_size} bp")
    print(f"Forward: {pair.forward.sequence} (Tm: {pair.forward.tm}°C)")
    print(f"Reverse: {pair.reverse.sequence} (Tm: {pair.reverse.tm}°C)")
```

### 3. `EPCRSimulator` (In-Silico ePCR)

```python
from nexttandem import EPCRSimulator, PrimerPair, Primer

simulator = EPCRSimulator(max_mismatches=1, max_product_size=1000)
# Simulates PCR binding and amplicon sizing on target sequence
```

### 4. `TRFWrapper` (TRF Binary Integration)

```python
from nexttandem import TRFWrapper, TandemConfig

config = TandemConfig(use_external_trf=True, trf_binary_path="trf")
wrapper = TRFWrapper(config)

if wrapper.is_trf_available():
    results = wrapper.run_trf_on_fasta("genome.fasta", output_dir="results")
```

---

## 🧬 In Silico e-PCR Simulation

Validate PCR primers electronically against target genomes or transcriptomes with mismatch detection and amplicon sizing:

```bash
# Test a specific pair of Forward and Reverse primers against a target genome
nexttandem epcr genome.fasta --fwd ATGCTAGCTAGCTAGC --rev CGATCGATCGATCGAT --max-mismatches 2
```

---

## 📂 Project Structure

```
nextTANDEM/
├── pyproject.toml         # Packaging, metadata & dependencies (PEP 621)
├── MANIFEST.in            # Package distribution manifest
├── README.md              # Documentation
├── LICENSE                # MIT License
├── Dockerfile             # Containerized reproducible execution
├── Apptainer.def          # HPC Singularity / Apptainer definition
├── .github/               # GitHub workflows
│   └── workflows/
│       └── ci.yml         # GitHub Actions CI matrix
├── docs/                  # Web documentation landing page
│   └── index.html
├── paper/                 # JOSS submission manuscript
│   └── paper.md
├── nexttandem/            # Main package source
│   ├── __init__.py
│   ├── __main__.py        # Package execution entrypoint (python -m nexttandem)
│   ├── cli.py             # Rich CLI interface (Click + Rich)
│   ├── config.py          # Configuration management (YAML / JSON)
│   ├── models.py          # Dataclasses (TandemRepeatItem, CompoundTandemRepeat, SequenceAnalysisResult)
│   ├── finder.py          # Native multi-parallel Tandem Repeat detection engine
│   ├── trf_wrapper.py     # External TRF binary wrapper integration
│   ├── compound.py        # Compound tandem repeat grouping logic
│   ├── primer.py          # PCR Primer Design engine (SantaLucia 1998)
│   ├── epcr.py            # In-silico Electronic PCR simulator
│   ├── gpu.py             # GPU hardware acceleration module (CuPy/PyTorch)
│   ├── artifacts.py       # Artifact Manager for structured output directory
│   ├── utils.py           # Memory-efficient FASTA & .gz streaming utilities
│   ├── provenance.py      # FAIR RO-Crate JSON-LD exporter
│   └── outputs/           # Output formatters
│       ├── __init__.py
│       ├── gff3.py        # Sequence Ontology compliant GFF3 exporter
│       ├── tsv.py         # Tab-delimited TSV exporter
│       └── json.py        # JSON summary exporter
└── tests/                 # Unit & integration test suite
```

---

## ⚙️ Configuration File (`nexttandem.yaml`)

`nextTANDEM` can be configured via a clean YAML file generated with `nexttandem init-config`:

```yaml
min_motif_len: 1
max_motif_len: 500
min_repeats: 2.0
match_score: 2
mismatch_penalty: 7
indel_penalty: 7
min_score: 50
max_period: 2000
threads: 16
design_primers: true
flank_len: 150
opt_tm: 58.0
min_product_size: 100
max_product_size: 300
generate_ro_crate: true
use_external_trf: false
use_gpu: false
gpu_device_id: 0
```

---

## 📁 Output Artifacts Directory Layout

Every `nextTANDEM` run produces a structured artifact directory:

```
results/
├── annotations/
│   └── nexttandem_results.gff3      # Sequence Ontology GFF3 annotation
├── primers/
│   ├── nexttandem_repeats.tsv       # TSV report with Tandem Repeat locations & scores
│   └── nexttandem_primers.tsv       # TSV report with designed PCR Primers & Tm calculations
├── provenance/
│   └── ro-crate-metadata.json       # W3C RO-Crate FAIR JSON-LD metadata
├── summary/
│   ├── summary.json                 # Structured JSON execution report
│   └── nexttandem_summary_statistics.txt # Summary text report
└── run_manifest.json                # Global execution JSON manifest
```

---

## 🌐 FAIR Compliance & Sequence Ontology

`nextTANDEM` output complies with **FAIR (Findable, Accessible, Interoperable, Reusable)** principles:
- **GFF3 Annotations**: Uses official [Sequence Ontology (SO)](http://www.sequenceontology.org/) terms:
  - **`SO:0000705`** (`tandem_repeat`)
  - **`SO:0000289`** (`microsatellite`)
  - **`SO:0001061`** (`minisatellite`)
- **Execution Provenance**: Generates W3C RO-Crate (`ro-crate-metadata.json`) recording input file SHA-256 hash, configuration hash, environment details, and execution timestamp.

---

## 🧪 Testing Suite

Run end-to-end integration tests using `pytest`:

```bash
pytest -v
```

---

## ✉️ Author & Contact

- **Author**: Fabiano Menegidio
- **Email**: [labiomics@bioinformatica.com.br](mailto:labiomics@bioinformatica.com.br)
- **GitHub**: [https://github.com/LaBiOmicS/nextTANDEM](https://github.com/LaBiOmicS/nextTANDEM)

---

## 📜 Citation

If you use `nextTANDEM` in your research or software pipelines, please cite the repository:

```bibtex
@misc{menegidio2026nexttandem,
  author = {Menegidio, Fabiano},
  title = {nextTANDEM: High-Performance & FAIR-Compliant Tandem Repeat Identification and PCR Primer Design Platform},
  year = {2026},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/LaBiOmicS/nextTANDEM}}
}
```

---

## 📄 License

Distributed under the **MIT License**. See `LICENSE` for more information.
