Bad Reads, Bad Results: Why Trimming and Filtering Your NGS Data Is Non-Negotiable
Sehgeet kaur
Graduate Research Assistant at Virginia Tech | GBCB Program | Transforming Data into Insights | Communicating Science at Bioinformatic Bites
After months of careful sample preparation, precise quantification, and meticulous sequencing library construction, you finally receive your next-generation sequencing (NGS) data. You’re eager to move forward with analysis—aligning reads, identifying variants, or assembling a genome.
But then, something seems… off. Your alignments are messy. Some reads map where they shouldn’t. Variant calling results don’t make sense. You check your sequencing report and find unexpected adapter sequences, low-quality bases, and duplicate reads.
What went wrong?
Before you dive into data analysis, you need to clean up your reads. This means trimming unwanted sequences and filtering poor-quality reads—two preprocessing steps that are non-negotiable for accurate bioinformatics results.
The Two-Step Cleanup: Trimming vs. Filtering
Think of your raw sequencing reads like freshly harvested crops—before they are ready for consumption, they must be processed and refined. Similarly, raw sequencing data contains unwanted noise that must be removed before meaningful analysis can begin. Without proper trimming and filtering, your results can be misleading, error-prone, and computationally expensive to process.
1?? Trimming: Cleaning Up Reads at the Base Level
Trimming focuses on removing unwanted sequences from individual reads while keeping the read itself intact. It is a crucial preprocessing step that enhances read quality and prevents sequencing artifacts from interfering with downstream analysis.
?? What Trimming Removes:
? Adapter sequences – Leftover fragments from library preparation that can interfere with alignments.
? Primers – Unwanted sequences from amplification steps in PCR-based library prep.
? Low-quality bases – Sequencing errors, especially at the 3′ end of reads.
? Poly-G tails (common in NovaSeq data) – A technical artifact that can interfere with base calling.
2?? Filtering: Removing Unreliable Reads from the Dataset
Filtering is the process of discarding entire reads that do not meet quality criteria. While trimming improves the quality of individual reads, filtering ensures that only the most reliable reads are kept for analysis.
?? What Filtering Removes:
? Short reads – Reads below a certain length threshold that lack biological relevance.
? Duplicate reads – Redundant sequences resulting from PCR amplification bias.
? High-N content reads – Reads with too many ambiguous bases ("N") that reduce accuracy.
? Low-complexity reads – Reads composed of repetitive sequences or homopolymers, which can skew downstream results.
Choosing the Right Tool: A Comparative Analysis
Once you've decided to trim and filter your NGS reads, the next big question is:
Which tool should you use?
There are several bioinformatics tools available, each designed for different needs. Some tools focus on speed, others on precision, and some on contaminant removal. The best choice depends on your sequencing technology, dataset type, and research goals.
Let’s explore four of the most commonly used tools: Trimmomatic, Fastp, Cutadapt, and BBduk, and see how they compare.
?? 1?? Trimmomatic: The Versatile Workhorse
? Best for: Illumina data (paired-end & single-end reads), RNA-seq, whole-genome sequencing (WGS), amplicon sequencing
? Key Features:
? Limitations:
?? When to Use Trimmomatic:
?? RNA-seq: Ensures high-quality transcript quantification by removing adapter contamination.
?? WGS & Exome-seq: Helps with base calling accuracy for variant detection.
?? Amplicon sequencing: Customizable settings make it ideal for targeted sequencing studies.
?? 2?? Fastp: The Speed King & All-in-One Solution
? Best for: General-purpose trimming, quality control, fast preprocessing
? Key Features:
? Limitations:
?? When to Use Fastp:
? Large NGS datasets: Reduces preprocessing time significantly.
?? Basic trimming needs: Works well for routine Illumina RNA-seq and DNA-seq projects.
?? When you need a quick quality check: Provides an HTML report showing read quality before and after trimming.
? 3?? Cutadapt: The Adapter Slayer
? Best for: Small RNA sequencing, amplicon sequencing, removing specific adapters
? Key Features:
? Limitations:
?? When to Use Cutadapt:
?? Small RNA sequencing: Adapters are crucial to remove due to the short read length.
?? Amplicon sequencing (16S, ITS): Ensures clean reads for metagenomic studies.
?? Any dataset with persistent adapter contamination.
?? 4?? BBduk (BBTools): The Contaminant Cleaner
? Best for: Metagenomics, host DNA removal, high-depth sequencing
? Key Features:
? Limitations:
?? When to Use BBduk:
?? Metagenomics: Removes sequencing artifacts and host contamination.
?? Host-DNA removal: Especially useful when working with human microbiome samples.
?? Ultra-deep sequencing projects: Helps clean up massive datasets efficiently.
Which Tool Should You Use? A Quick Guide
Final Takeaways: Why Preprocessing Is the Foundation of Good Science
?? Never trust raw sequencing reads! Always trim and filter before analysis.
?? Choose the right tool for the job—Fastp is great for speed, Trimmomatic for control, Cutadapt for small RNA, and BBduk for contaminants.
?? Always check your data with FastQC before and after preprocessing.
?? Trimming and filtering aren’t optional—they are essential for reproducible results.
Skipping preprocessing is like trying to build a house without clearing the land first—you’re setting yourself up for unstable results. Clean your data first, and your research will thank you later!
Happy cleaning!!!!