split_fastqcats

A tool for processing and splitting concatenated FastQ reads based on primer sequences, particularly designed for long-read RNA sequencing data.

Overview

split_fastqcats is designed to handle FastQ reads with the following structure:

TSO---cDNA---polyA---UMI---revRTprimer

Example structure:

AAGCAGTGGTATCAACGCAGAGTGAAT---cDNA---polyA--N---UMI---GTACTCTGCGTTGATACCACTGCTT

The tool performs the following:

• Identifies and validates primer sequences using Smith-Waterman alignment
• Handles potential sequencing errors with configurable mismatch tolerance
• Processes polyA tails and UMIs
• Splits concatenated reads into individual segments
• Provides detailed statistics about the processing

Installation

Using pip

pip install split_fastqcats

From source

git clone https://github.com/cribbslab/split_fastqcats.git
cd split_fastqcats
pip install -e .

Using conda/mamba

mamba env create -f conda/environments/split_fastqcats.yml
conda activate fastcats
pip install -e .

Usage

Command Line Interface

Basic usage:

split-fastqcats input.fastq.gz \
    -fp AAGCAGTGGTATCAACGCAGAGT \
    -rp ACTCTGCGTTGATACCACTGCTT \
    processed.fastq.gz \
    lowqual.fastq.gz \
    bin.fastq.gz \
    stats.csv

Arguments:

• input.fastq.gz: Input FastQ file (gzipped)
• -fp, --forward_primer: Forward primer sequence (default: AAGCAGTGGTATCAACGCAGAGT)
• -rp, --reverse_primer: Reverse primer sequence (default: ACTCTGCGTTGATACCACTGCTT)
• -i, --indexes: Optional index sequences as 'index:start_seq,end_seq'
• processed.fastq.gz: Output file for correctly processed reads
• lowqual.fastq.gz: Output file for low quality reads
• bin.fastq.gz: Output file for binned reads
• stats.csv: Output statistics file

Python API

from split_fastqcats import FastqSplitter

# Initialize the splitter
splitter = FastqSplitter(
    forward_primer="AAGCAGTGGTATCAACGCAGAGT",
    reverse_primer="ACTCTGCGTTGATACCACTGCTT",
    index_dict={'1': ['AAATTTGGGCCC', 'GGGCCCAAATTT']}
)

# Process reads
splitter.split_reads(
    input_file="input.fastq.gz",
    processed_output="processed.fastq.gz",
    lowqual_output="lowqual.fastq.gz",
    bin_output="bin.fastq.gz",
    stats_output="stats.csv"
)

Output Files

1. processed.fastq.gz: Contains reads that:

   • Have valid primer pairs
   • Meet quality thresholds
   • Have correct index sequences (if specified)

2. lowqual.fastq.gz: Contains reads that:

   • Have only one valid primer
   • Have mismatched indexes
   • Meet basic quality requirements but fail stricter criteria

3. bin.fastq.gz: Contains reads that:

   • Lack valid primers
   • Have too many primer matches (>10)
   • Fail basic quality requirements

4. stats.csv: Contains processing statistics:

   • Total sequences processed
   • Number of processed reads
   • Number of low-quality reads
   • Number of binned reads

Quality Control Parameters

The tool implements several QC measures:

• Smith-Waterman alignment for primer detection
• Maximum 5 mismatches allowed in primer sequences
• Minimum 75% match score required for primer identification
• Index validation with 83% minimum match score
• Position validation for index sequences

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this tool in your research, please cite:

@software{split_fastqcats2024,
  author = {Cribbs, Adam},
  title = {split_fastqcats: A tool for processing concatenated FastQ reads},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/cribbslab/split_fastqcats}
}