Step-by-Step Guide: Trimming Illumina Reads Using Custom Algorithms and Tools

Trimming Illumina reads is a crucial step in preprocessing sequencing data to remove low-quality bases, adapter sequences, and other artifacts. This guide provides a step-by-step approach to trimming Illumina reads using custom algorithms, Python scripts, and popular tools.

1. Understand the Trimming Process

Trimming involves:

• Quality Trimming: Removing low-quality bases from the ends or middle of reads.
• Adapter Trimming: Removing adapter sequences that may be present due to library preparation.
• Read Filtering: Discarding reads that are too short or of poor quality after trimming.

2. Prepare Your Data

Ensure your data is in FASTQ format. A FASTQ file typically contains four lines per read:

1. Read identifier (starts with @)
2. Sequence
3. Separator (usually +)
4. Quality scores (ASCII-encoded)

Example:

@read1
ACGTACGTACGT
+
IIIIIIIIIIII

3. Use Python for Custom Trimming

If you prefer a custom approach, you can implement trimming algorithms in Python.

Example: Modified Richard Mott Algorithm

This algorithm trims low-quality bases from the 3′ end of reads.

def mott_trim(sequence, quality, limit=0.05):
    """
    Trims a sequence using the modified Richard Mott algorithm.
    :param sequence: DNA sequence (str)
    :param quality: Quality scores (str, ASCII-encoded)
    :param limit: Quality threshold (float)
    :return: Trimmed sequence and quality scores (str, str)
    """
    tseq = []
    S = 0
    for q, n in zip(quality, sequence):
        Q = ord(q) - 64  # Convert ASCII to Phred score
        p = 10 ** (Q / -10.0)  # Probability of error
        s = S
        S += limit - p
        if S < 0:
            S = 0
        if s > S:
            break
        tseq.append(n)
    trimmed_seq = ''.join(tseq)
    trimmed_qual = quality[:len(trimmed_seq)]
    return trimmed_seq, trimmed_qual

# Example usage
sequence = "ACGTACGTACGT"
quality = "IIIIIIIIIIII"
trimmed_seq, trimmed_qual = mott_trim(sequence, quality)
print("Trimmed Sequence:", trimmed_seq)
print("Trimmed Quality:", trimmed_qual)

4. Use Existing Tools for Trimming

Several tools are available for trimming Illumina reads. Below are some popular options.

Option 1: FASTX-Toolkit

• Description: A collection of command-line tools for processing FASTQ/A files.
• Installation:
  bash

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  sudo apt-get install fastx-toolkit

• Usage:
  bash

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  fastq_quality_trimmer -t 30 -l 50 -i input.fastq -o trimmed.fastq

  • -t 30: Trim bases with quality below 30.
  • -l 50: Discard reads shorter than 50 bases after trimming.

Option 2: Trimmomatic

• Description: A flexible tool for trimming Illumina data.
• Installation:
  bash

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  sudo apt-get install trimmomatic

• Usage:
  bash

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  java -jar trimmomatic.jar PE -phred33 input_1.fastq input_2.fastq \
    output_1_paired.fastq output_1_unpaired.fastq \
    output_2_paired.fastq output_2_unpaired.fastq \
    ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

  • ILLUMINACLIP: Remove adapter sequences.
  • LEADING/TRAILING: Trim low-quality bases from the start/end.
  • SLIDINGWINDOW: Trim using a sliding window.
  • MINLEN: Discard reads shorter than the specified length.

Option 3: Cutadapt

• Description: A tool for removing adapter sequences.
• Installation:
  bash

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  pip install cutadapt

• Usage:
  bash

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  cutadapt -a ADAPTER_SEQ -o trimmed.fastq input.fastq

  • -a ADAPTER_SEQ: Specify the adapter sequence to remove.

5. Combine Trimming and Filtering

For a comprehensive preprocessing pipeline, combine quality trimming, adapter removal, and read filtering.

Example Pipeline:

1. Remove Adapters:
   bash

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   cutadapt -a AGATCGGAAGAGC -o trimmed_1.fastq input_1.fastq

2. Quality Trim:
   bash

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   fastq_quality_trimmer -t 20 -l 50 -i trimmed_1.fastq -o final_1.fastq

3. Filter Short Reads:
   bash

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   awk 'NR%4==2 {if (length($0) >= 50) print}' final_1.fastq > filtered_1.fastq

6. Automate with Scripts

Automate the trimming process using a shell script or Python script.

Shell Script:

#!/bin/bash

# Input files
input_1="input_1.fastq"
input_2="input_2.fastq"

# Step 1: Remove adapters
cutadapt -a AGATCGGAAGAGC -A AGATCGGAAGAGC -o trimmed_1.fastq -p trimmed_2.fastq $input_1 $input_2

# Step 2: Quality trim
fastq_quality_trimmer -t 20 -l 50 -i trimmed_1.fastq -o final_1.fastq
fastq_quality_trimmer -t 20 -l 50 -i trimmed_2.fastq -o final_2.fastq

# Step 3: Filter short reads
awk 'NR%4==2 {if (length($0) >= 50) print}' final_1.fastq > filtered_1.fastq
awk 'NR%4==2 {if (length($0) >= 50) print}' final_2.fastq > filtered_2.fastq

echo "Trimming and filtering complete!"

7. Validate Results

After trimming, validate the results using quality control tools like FastQC:

fastqc filtered_1.fastq filtered_2.fastq

By following these steps, you can effectively trim and preprocess Illumina sequencing data using custom algorithms or existing tools. This ensures high-quality input for downstream analyses like alignment and variant calling.