# extracTR
## Introduction
extracTR is a tool for identifying and analyzing tandem repeats in genomic sequences. It works with raw sequencing data (FASTQ) or assembled genomes (FASTA), using k-mer based approaches to detect repetitive patterns efficiently.
## Features
- Efficient tandem repeat detection from raw sequencing data
- Support for single-end and paired-end FASTQ files
- Support for genome assemblies in FASTA format
- Customizable parameters for fine-tuning repeat detection
- Output in easy-to-analyze CSV format
- Multi-threaded processing for improved performance
## Requirements
- Python 3.7 or later
- Jellyfish 2.3.0 or later
- Conda (for easy environment management)
## Installation
We recommend installing extracTR in a separate Conda environment to manage dependencies effectively.
1. Create a new Conda environment:
```bash
conda create -n extractr_env python=3.9
```
2. Activate the environment:
```bash
conda activate extractr_env
```
3. Install Jellyfish:
```bash
conda install -c bioconda jellyfish
```
4. Install extracTR using pip:
```bash
pip install extracTR
```
To deactivate the environment when you're done:
```bash
conda deactivate
```
## Usage
Before running extracTR, ensure that you have removed adapters from your sequencing reads and activated the Conda environment:
```bash
conda activate extractr_env
```
Basic usage:
For paired-end FASTQ files:
```bash
extracTR -1 reads_1.fastq -2 reads_2.fastq -o output_prefix -c 30
```
For single-end FASTQ file:
```bash
extracTR -1 reads.fastq -o output_prefix -c 30
```
For genome assembly in FASTA format:
```bash
extracTR -f genome.fasta -o output_prefix -c 30
```
Advanced usage with custom parameters:
```bash
extracTR -1 reads_1.fastq -2 reads_2.fastq -o output_prefix -t 64 -c 30 -k 25
```
Options:
- `-1, --fastq1`: Input file with forward DNA sequences in FASTQ format
- `-2, --fastq2`: Input file with reverse DNA sequences in FASTQ format (optional for paired-end data)
- `-f, --fasta`: Input genome assembly in FASTA format
- `-o, --output`: Prefix for output files
- `-t, --threads`: Number of threads to use (default: 32)
- `-c, --coverage`: Coverage to use for indexing (required)
- `-k, --k`: K-mer size to use for indexing (default: 23)
- `--lu`: Coverage cutoff for k-mers (default: 100 * coverage)
Note: You must provide either FASTQ file(s) or a FASTA file as input.
## Output
extracTR generates the following output files:
- `{output_prefix}.csv`: Main output file containing detected tandem repeats
- `{output_prefix}.sdat`: Intermediate file with k-mer frequency data
- Additional files for detailed analysis and debugging
Raw data
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"description": "# extracTR\n\n## Introduction\n\nextracTR is a tool for identifying and analyzing tandem repeats in genomic sequences. It works with raw sequencing data (FASTQ) or assembled genomes (FASTA), using k-mer based approaches to detect repetitive patterns efficiently.\n\n## Features\n\n- Efficient tandem repeat detection from raw sequencing data\n- Support for single-end and paired-end FASTQ files\n- Support for genome assemblies in FASTA format\n- Customizable parameters for fine-tuning repeat detection\n- Output in easy-to-analyze CSV format\n- Multi-threaded processing for improved performance\n\n## Requirements\n\n- Python 3.7 or later\n- Jellyfish 2.3.0 or later\n- Conda (for easy environment management)\n\n## Installation\n\nWe recommend installing extracTR in a separate Conda environment to manage dependencies effectively.\n\n1. Create a new Conda environment:\n\n```bash\nconda create -n extractr_env python=3.9\n```\n\n2. Activate the environment:\n\n```bash\nconda activate extractr_env\n```\n\n3. Install Jellyfish:\n\n```bash\nconda install -c bioconda jellyfish\n```\n\n4. Install extracTR using pip:\n\n```bash\npip install extracTR\n```\n\nTo deactivate the environment when you're done:\n\n```bash\nconda deactivate\n```\n\n## Usage\n\nBefore running extracTR, ensure that you have removed adapters from your sequencing reads and activated the Conda environment:\n\n```bash\nconda activate extractr_env\n```\n\nBasic usage:\n\nFor paired-end FASTQ files:\n```bash\nextracTR -1 reads_1.fastq -2 reads_2.fastq -o output_prefix -c 30\n```\n\nFor single-end FASTQ file:\n```bash\nextracTR -1 reads.fastq -o output_prefix -c 30\n```\n\nFor genome assembly in FASTA format:\n```bash\nextracTR -f genome.fasta -o output_prefix -c 30\n```\n\nAdvanced usage with custom parameters:\n\n```bash\nextracTR -1 reads_1.fastq -2 reads_2.fastq -o output_prefix -t 64 -c 30 -k 25\n```\n\nOptions:\n- `-1, --fastq1`: Input file with forward DNA sequences in FASTQ format\n- `-2, --fastq2`: Input file with reverse DNA sequences in FASTQ format (optional for paired-end data)\n- `-f, --fasta`: Input genome assembly in FASTA format\n- `-o, --output`: Prefix for output files\n- `-t, --threads`: Number of threads to use (default: 32)\n- `-c, --coverage`: Coverage to use for indexing (required)\n- `-k, --k`: K-mer size to use for indexing (default: 23)\n- `--lu`: Coverage cutoff for k-mers (default: 100 * coverage)\n\nNote: You must provide either FASTQ file(s) or a FASTA file as input.\n\n## Output\n\nextracTR generates the following output files:\n- `{output_prefix}.csv`: Main output file containing detected tandem repeats\n- `{output_prefix}.sdat`: Intermediate file with k-mer frequency data\n- Additional files for detailed analysis and debugging\n",
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