Skip to main content

Extract and analyze satellite DNA from raw sequences.

Project description

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:
conda create -n extractr_env python=3.9
  1. Activate the environment:
conda activate extractr_env
  1. Install Jellyfish:
conda install -c bioconda jellyfish
  1. Install extracTR using pip:
pip install extracTR

To deactivate the environment when you're done:

conda deactivate

Usage

Before running extracTR, ensure that you have removed adapters from your sequencing reads and activated the Conda environment:

conda activate extractr_env

Basic usage:

For paired-end FASTQ files:

extracTR -1 reads_1.fastq -2 reads_2.fastq -o output_prefix

For single-end FASTQ file:

extracTR -1 reads.fastq -o output_prefix

For genome assembly in FASTA format:

extracTR -f genome.fasta -o output_prefix

Advanced usage with custom parameters:

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 (default: 1)
  • -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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

extracTR-0.2.14.tar.gz (13.5 kB view details)

Uploaded Source

File details

Details for the file extracTR-0.2.14.tar.gz.

File metadata

  • Download URL: extracTR-0.2.14.tar.gz
  • Upload date:
  • Size: 13.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.19

File hashes

Hashes for extracTR-0.2.14.tar.gz
Algorithm Hash digest
SHA256 029cc34532f61ff03a584424bb4b3992cca194e2c107867aa7c502e75a9a98ba
MD5 b5cad64c827c8251063be8e0d427a4bb
BLAKE2b-256 7bc24cc2285caca8b90c06501676616227f1429a8a83214a2b01e4798a4e1b02

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page