This FastQC Tutorial explains how to evaluate the quality of FASTQ sequencing files before beginning downstream next-generation sequencing analysis. Whether you are working with RNA-Seq, whole-genome sequencing, variant calling, metagenomics, ChIP-Seq, or other NGS datasets, quality control should be one of the first computational steps in your workflow.

Modern sequencing platforms can generate millions or billions of reads. However, raw sequencing data may contain low-quality bases, adapter sequences, unexpected sequence composition, duplicated reads, or other technical characteristics that can influence downstream analysis.

FastQC provides a quick visual summary of these properties.

For an NGS analyst, the important skill is not simply running FastQC. You must understand what each FastQC graph means, which warnings matter, which failures may be expected for a particular experiment, and what action—if any—you should take next.

In this complete beginner-friendly FastQC Tutorial, you will learn:

• What FastQC is
• Why FASTQ quality control matters
• How FASTQ quality scores work
• How to install FastQC
• How to run FastQC from Linux
• How to analyze paired-end sequencing data
• How to interpret every major FastQC module
• What PASS, WARN, and FAIL mean
• How to detect adapter contamination
• How to interpret sequence duplication
• How RNA-Seq, WGS, variant calling, and metagenomics FastQC reports differ
• When trimming is actually required
• How to run FastQC after trimming
• How to summarize multiple FastQC reports with MultiQC
• Common FastQC mistakes
• How FastQC fits into complete NGS pipelines

If you are new to sequencing, first read our What Is Next-Generation Sequencing (NGS)? Complete Beginner’s Guide.

FastQC Tutorial: What Is FastQC?

FastQC is a quality-control application developed for high-throughput sequencing data.

Its purpose is to rapidly inspect sequencing files and identify characteristics that may require attention before downstream analysis.

The official FastQC website describes FastQC as a modular quality-control tool that provides graphical and tabular summaries of high-throughput sequencing data. It can analyze FASTQ as well as several alignment-file formats and can generate standalone HTML reports for automated workflows.

FastQC does not:

• Align sequencing reads
• Trim adapters
• Remove low-quality reads
• Quantify genes
• Call variants
• Perform differential expression

Instead, it answers a more fundamental question:

What does the quality and composition of my sequencing data look like?

The answer helps determine what preprocessing, if any, should happen next.

Why Is FASTQ Quality Control Important?

Imagine beginning an RNA-Seq or variant-calling pipeline without checking the raw sequencing reads.

If the data contain substantial technical problems, those problems can propagate through the entire analysis.

Potential consequences include:

• Poor alignment rates
• Incorrect gene quantification
• False-positive variants
• Missed variants
• Reduced assembly quality
• Biased abundance estimates
• Increased computational processing
• Misleading biological conclusions

A good NGS workflow therefore typically begins with:

Raw FASTQ
↓
Quality Control
↓
Evaluate Problems
↓
Preprocess if Necessary
↓
Quality Control Again
↓
Downstream Analysis

Quality control is not merely a checkbox.

It is a decision-making stage.

What Is a FASTQ File?

Before learning FastQC, you should understand what the tool is examining.

A FASTQ file contains sequencing reads together with quality scores.

A simplified FASTQ record looks like:

@READ_001
ACGTGCTAGCTAGCTAGCTA
+
FFFFFFFFFFFFFFFFFFFF

Each read normally contains four lines.

Line 1: Read Identifier

@READ_001

Identifies the sequencing read.

Line 2: Sequence

ACGTGCTAGCTAGCTAGCTA

Contains the nucleotide sequence.

Line 3: Separator

+

Separates the sequence from its quality string.

Line 4: Quality Scores

FFFFFFFFFFFFFFFFFFFF

Encodes the estimated quality of each sequenced base.

FastQC analyzes these reads collectively to detect trends in sequencing quality and sequence composition.

What Are Phred Quality Scores?

Sequencing quality is commonly represented using Phred quality scores.

The Phred score is related to the estimated probability that a base call is incorrect.

A commonly used relationship is:

Q = -10 log10(P)

where:

• Q = Phred quality score
• P = estimated probability of an incorrect base call

For example:

This is why Q30 is frequently mentioned when discussing high-quality sequencing data.

However, FastQC should not be interpreted by applying one universal Q30 rule to every experiment.

The expected quality profile depends on:

• Sequencing platform
• Read length
• Sequencing chemistry
• Library preparation
• Experimental design

Where Does FastQC Fit in an NGS Pipeline?

FastQC usually appears immediately after obtaining FASTQ files.

For example, an RNA-Seq workflow may look like:

FASTQ
↓
FastQC
↓
Adapter / Quality Processing if Required
↓
FastQC
↓
STAR or HISAT2
↓
BAM
↓
featureCounts
↓
Count Matrix
↓
Differential Expression

For practical training in the complete transcriptomics workflow, explore:

Hands-On RNA-Seq Analysis: From FASTQ to Differential Expression

A genomic variant-analysis workflow might look like:

FASTQ
↓
FastQC
↓
Read Processing if Required
↓
BWA Alignment
↓
BAM
↓
Variant Calling
↓
VCF
↓
Variant Annotation

For this pathway, continue with:

Learn Variant Calling: NGS Data Analysis

You can also read our Variant Calling Explained: Complete NGS Variant Analysis Guide.

FastQC Tutorial: How to Install FastQC

FastQC can be installed in several ways.

The official FastQC distribution is available from the Babraham Bioinformatics FastQC page.

FastQC is written in Java, and the official software page notes that a suitable Java runtime is required.

For bioinformatics workflows on Linux, package managers such as Conda or Mamba are often convenient.

Install FastQC with Conda

If you use Conda:

conda install -c conda-forge -c bioconda fastqc

Then verify the installation:

fastqc --version

You should see the installed FastQC version.

Install FastQC with Mamba

If you use Mamba:

mamba install -c conda-forge -c bioconda fastqc

Mamba is often faster at resolving bioinformatics software environments.

Check FastQC Help

Use:

fastqc --help

to see available command-line options.

FastQC Tutorial: How to Run FastQC on a FASTQ File

Suppose your directory contains:

sample1.fastq.gz

Run:

fastqc sample1.fastq.gz

FastQC can directly analyze gzip-compressed FASTQ files, so you generally do not need to decompress .fastq.gz files first. The official FastQC documentation lists gzip-compressed FASTQ among its supported input formats.

After FastQC completes, you will usually obtain files such as:

sample1_fastqc.html
sample1_fastqc.zip

The HTML file contains the interactive report you normally inspect.

Running FastQC on Multiple FASTQ Files

If your folder contains several FASTQ files:

sample1.fastq.gz
sample2.fastq.gz
sample3.fastq.gz
sample4.fastq.gz

you can run:

fastqc *.fastq.gz

FastQC will generate a separate report for each file.

Running FastQC with Multiple Threads

For several large sequencing files, you can specify multiple threads.

For example:

fastqc -t 4 *.fastq.gz

This asks FastQC to process files using multiple worker threads.

Choose the thread count according to the computational resources available on your system.

Save FastQC Reports to a Separate Directory

First create a directory:

mkdir fastqc_results

Then run:

fastqc -o fastqc_results *.fastq.gz

Now your project remains organized:

project/
│
├── raw_data/
│   ├── sample1.fastq.gz
│   └── sample2.fastq.gz
│
└── fastqc_results/
    ├── sample1_fastqc.html
    ├── sample1_fastqc.zip
    ├── sample2_fastqc.html
    └── sample2_fastqc.zip

Good directory organization becomes increasingly important as NGS projects grow.

FastQC Tutorial for Paired-End Sequencing

Paired-end sequencing normally generates two FASTQ files for each sample.

For example:

sample1_R1.fastq.gz
sample1_R2.fastq.gz

Run FastQC on both files:

fastqc sample1_R1.fastq.gz sample1_R2.fastq.gz

or:

fastqc *.fastq.gz

You will receive separate reports for:

sample1_R1
sample1_R2

This is important because R1 and R2 can have different quality profiles.

For example, R2 may sometimes show a stronger quality decrease near the end of the read than R1.

Never inspect only R1 and assume R2 is identical.

Understanding PASS, WARN and FAIL in FastQC

Each FastQC module receives a summary status.

You may see:

PASS

Usually represented by a green check mark.

WARN

Usually represented by an orange warning symbol.

FAIL

Usually represented by a red failure symbol.

A common beginner mistake is thinking:

Every red FastQC module means the sequencing dataset is unusable.

That is incorrect.

FastQC applies predefined heuristics.

Certain sequencing applications naturally violate those expectations.

For example:

• RNA-Seq may show sequence-composition bias
• Amplicon sequencing can show extremely high duplication
• Small RNA sequencing may contain substantial adapter signal
• Metagenomic samples may have unusual GC distributions
• Highly expressed RNA transcripts can increase duplication
• Trimmed datasets may have variable read lengths

Therefore:

PASS ≠ automatically perfect

and:

FAIL ≠ automatically unusable

The correct interpretation depends on the experiment.

The official FastQC documentation emphasizes that the tool provides a rapid overview of areas where potential problems may exist; these modules need to be interpreted in context rather than treated as absolute biological judgments.

FastQC Tutorial: Understanding the FastQC Report

FastQC reports contain several modules.

The exact set may depend on the data and software version, but commonly encountered modules include:

1. Basic Statistics
2. Per Base Sequence Quality
3. Per Tile Sequence Quality
4. Per Sequence Quality Scores
5. Per Base Sequence Content
6. Per Sequence GC Content
7. Per Base N Content
8. Sequence Length Distribution
9. Sequence Duplication Levels
10. Overrepresented Sequences
11. Adapter Content

Let’s examine each one.

1. Basic Statistics

The Basic Statistics section provides an overview of the input file.

Information may include:

• Filename
• File type
• Sequence quality encoding
• Total sequences
• Total bases
• Poor-quality sequences
• Sequence length
• GC percentage

For example:

Filename: sample_R1.fastq.gz
Total Sequences: 25,000,000
Sequence Length: 150
%GC: 48

This section provides useful context before examining the more detailed graphs.

What Should You Check in Basic Statistics?

Verify:

Correct Filename

Make sure you analyzed the intended sample.

Number of Reads

Unexpectedly low read counts may indicate:

• Incomplete downloads
• Failed sequencing
• Incorrect files
• Earlier filtering

Read Length

If the experiment was expected to generate 150-bp reads but the report shows something very different, investigate.

GC Content

GC percentage should make biological sense for the organism and experiment.

However, do not judge GC content using one universal expected percentage.

2. Per Base Sequence Quality

Per Base Sequence Quality is one of the most important FastQC graphs.

It displays the distribution of quality scores at each position along the read.

The x-axis represents:

Position in read

The y-axis represents:

Phred quality score

You may see box plots across each base position.

What Does a Good Per Base Quality Plot Look Like?

Generally, you want most base positions to remain in the high-quality region.

A typical high-quality profile may show:

Read start → high quality
Middle → high quality
Read end → moderate decline

A gradual reduction near the 3′ end of longer reads is relatively common.

What If Quality Drops at the Read End?

Suppose your 150-bp reads show:

Bases 1–120:
high quality

Bases 121–150:
progressive decline

Possible actions include:

• Evaluate whether the downstream aligner tolerates the profile
• Perform quality trimming if justified
• Use a tool such as fastp or Cutadapt
• Compare alignment performance

Do not automatically trim all bases below an arbitrary value without considering the downstream pipeline.

Modern aligners often use base-quality information and can tolerate some low-quality bases.

3. Per Tile Sequence Quality

The Per Tile Sequence Quality module examines whether specific regions of the sequencing flow cell show consistently different quality.

This can help reveal localized instrument-related problems.

Ideally, quality should be relatively uniform across sequencing tiles.

Strong localized patterns may suggest:

• Flow-cell imaging problems
• Instrument issues
• Local sequencing chemistry problems

This module may not be present or equally informative for every sequencing technology.

4. Per Sequence Quality Scores

The Per Sequence Quality Scores module summarizes the mean quality score for each read.

Instead of asking:

How good is base position 100?

it asks:

How good is each entire sequencing read on average?

Ideally, the distribution should be concentrated toward higher quality scores.

If a substantial group of reads has very low average quality, you may need to investigate:

• Sequencing performance
• Read filtering
• Sample-specific problems

5. Per Base Sequence Content

The Per Base Sequence Content graph shows the proportion of:

A
C
G
T

at each position across all sequencing reads.

For a random genomic library, nucleotide proportions may become relatively stable across most of the read.

However, strong differences near the beginning of reads can occur for biological or library-preparation reasons.

Why Can RNA-Seq Fail Per Base Sequence Content?

RNA-Seq libraries can show non-random sequence composition near the start of reads because of factors such as library preparation and priming biases.

Therefore, a warning or failure here does not automatically mean your RNA-Seq experiment is poor.

This is a good example of why FastQC modules must be interpreted according to assay type.

If you are analyzing RNA-Seq data, continue with our RNA-Seq Explained: Complete Beginner’s Guide.

6. Per Sequence GC Content

This module displays the GC-content distribution across individual sequencing reads.

For a relatively homogeneous genomic library, the observed distribution may resemble a smooth expected distribution.

Unexpected peaks can potentially indicate:

• Contamination
• Mixed organisms
• Library bias
• Overrepresented sequence populations

But context is critical.

GC Content in Metagenomics

In metagenomic sequencing, your sample may contain many organisms with different genome compositions.

Therefore:

one simple normal GC distribution

may not be expected.

A complex GC distribution can reflect real biological diversity.

This is another case where a FastQC warning is not automatically evidence of poor data.

For microbial-community analysis, explore:

Master Metagenomics and Microbiome Data Analysis Using Linux

The course provides a practical pathway for learners who want to progress from raw sequencing data into microbial-community analysis.

7. Per Base N Content

Sometimes a sequencing instrument cannot confidently determine whether a position is:

A
C
G
or
T

The base may instead be reported as:

N

The Per Base N Content module shows the percentage of ambiguous N bases at each read position.

Ideally, this should remain low.

A substantial increase may indicate poor base calling or problematic sequencing cycles.

8. Sequence Length Distribution

This module shows how sequencing read lengths are distributed.

For an untrimmed fixed-length sequencing run, you might expect:

150 bp
150 bp
150 bp
150 bp

After trimming, you may instead see:

150 bp
148 bp
143 bp
137 bp
121 bp
...

That does not automatically indicate a problem.

Variable sequence lengths may be expected after:

• Adapter trimming
• Quality trimming
• Protocol-specific processing
• Certain sequencing technologies

Interpret the graph according to how the FASTQ files were generated.

9. Sequence Duplication Levels

The Sequence Duplication Levels module estimates how often identical or highly repeated sequences occur in the dataset.

High duplication can sometimes indicate:

• PCR amplification
• Low library complexity
• Over-sequencing

But high duplication can also be biologically expected.

Sequence Duplication in RNA-Seq

Suppose one transcript is extremely highly expressed.

Many independent RNA molecules can produce identical or similar sequence reads.

Therefore, duplication in RNA-Seq does not necessarily represent PCR artifacts.

Highly expressed transcripts naturally generate many repeated reads.

This means:

Do not automatically remove duplicate reads from RNA-Seq simply because FastQC reports high duplication.

Duplicate interpretation depends on library type and experimental design.

Sequence Duplication in Variant Calling

For genomic DNA sequencing, excessive duplication may reduce the amount of independent information available for variant discovery.

Variant-calling pipelines may therefore include duplicate marking depending on library preparation and workflow.

Read our Variant Calling Explained guide for the broader FASTQ-to-VCF pipeline.

10. Overrepresented Sequences

The Overrepresented Sequences module identifies sequences occurring much more frequently than expected.

Possible sources include:

• Sequencing adapters
• PCR primers
• Highly abundant transcripts
• Ribosomal RNA
• Contaminants
• Library-specific sequences

FastQC may attempt to identify known sequence types where possible.

Example: Adapter Sequence

Suppose a sequencing insert is shorter than the read length.

The sequencer may read through the biological fragment and begin sequencing the adapter.

Conceptually:

Biological Insert:
ACGTACGTACGT

Adapter:
AGATCGGAAGAGC

The resulting read may contain:

ACGTACGTACGTAGATCGGAAGAGC

If this occurs in many reads, adapter sequence may appear as overrepresented sequence content.

11. Adapter Content

The Adapter Content module shows whether known adapter sequences appear across the sequencing reads.

Ideally, adapter content should remain low.

An increase near the 3′ end may indicate read-through into sequencing adapters.

For example:

Beginning of read:
little adapter signal

End of read:
increasing adapter signal

This commonly occurs when:

sequencing read length
>
biological insert length

Should You Trim Adapter Sequences?

If genuine adapter contamination is present, adapter trimming is generally appropriate before many downstream analyses.

Common tools include:

• fastp
• Cutadapt
• Trimmomatic

For example, your workflow may become:

Raw FASTQ
↓
FastQC
↓
Adapter Contamination Detected
↓
Cutadapt / fastp
↓
Trimmed FASTQ
↓
FastQC Again
↓
Downstream Analysis

The second FastQC run verifies whether the intended preprocessing actually improved the problem.

FastQC Before and After Trimming

A strong QC workflow normally preserves reports from both stages.

For example:

01_raw_fastqc/
02_trimmed_reads/
03_trimmed_fastqc/

This makes it possible to compare:

Before Trimming

• Adapter content
• End-of-read quality
• Overrepresented sequences

After Trimming

• Adapter removal
• Quality improvement
• Read-length changes
• Remaining issues

Do not delete your original QC reports.

They form part of your analysis record.

Should You Always Trim Low-Quality Reads?

No.

This is one of the most important lessons in this FastQC Tutorial.

The workflow should not automatically be:

FASTQ
↓
Trim everything
↓
Analysis

Instead:

FASTQ
↓
FastQC
↓
Understand the QC profile
↓
Decide whether preprocessing is justified

Unnecessary aggressive trimming can:

• Shorten reads
• Reduce mapping uniqueness
• Remove useful sequence
• Reduce effective coverage

The goal is not to make every FastQC symbol green.

The goal is to produce data suitable for the downstream biological analysis.

FastQC for RNA-Seq

For RNA-Seq, pay particular attention to:

• Per Base Sequence Quality
• Adapter Content
• Overrepresented Sequences
• Sequence Duplication
• Per Base Sequence Content
• GC Content

However, remember:

High Duplication

May reflect genuinely abundant transcripts.

Sequence Content Bias

May reflect library-preparation characteristics.

Overrepresented Sequences

Could include highly expressed biological transcripts as well as technical contaminants.

A red symbol therefore requires interpretation, not panic.

For a complete RNA-Seq pipeline, see:

Hands-On RNA-Seq Analysis: From FASTQ to Differential Expression

and read our RNA-Seq Explained guide.

FastQC for Whole Genome Sequencing

For WGS, important considerations include:

• Overall read quality
• Adapter contamination
• Duplication
• GC distribution
• Sequence quality toward read ends

The typical workflow is:

FASTQ
↓
FastQC
↓
Preprocessing if Required
↓
BWA
↓
BAM
↓
Variant Calling
↓
VCF

Read our Whole Genome Sequencing Explained article for the complete genomic pipeline.

FastQC for Variant Calling

Poor FASTQ quality can affect variant calling because genomic variants are inferred from sequencing evidence.

Potential issues include:

Low-Quality Bases

May introduce false mismatches.

Adapter Contamination

Can reduce alignment performance.

Poor Mapping

Can generate misleading variant evidence.

Excessive Duplication

Can reduce effective independent genomic coverage.

FastQC is therefore one of the earliest safeguards in a variant-discovery workflow.

For hands-on training:

Learn Variant Calling: NGS Data Analysis

This is the most directly relevant individual BioInformatix course for learners interested in FASTQ-to-VCF genomic analysis.

FastQC for Metagenomics

Metagenomic datasets require particularly careful interpretation.

A metagenomic sample may contain:

Bacteria
Archaea
Viruses
Fungi
Host DNA
Unknown organisms

Different organisms can have very different:

• GC content
• Sequence composition
• Relative abundance

Therefore, unusual GC distributions or sequence composition may reflect real community structure.

However, you should still investigate:

• Poor sequencing quality
• Adapter contamination
• Host contamination
• Technical overrepresentation

For practical microbial-community analysis, explore:

Master Metagenomics and Microbiome Data Analysis Using Linux

FastQC for Small RNA Sequencing

Small RNA libraries often contain very short biological inserts.

For example, mature miRNAs are much shorter than standard Illumina read lengths.

Therefore, sequencing can easily continue through:

small RNA insert
↓
adapter

Strong adapter contamination may therefore be expected before preprocessing.

Correct adapter removal is particularly important in small RNA workflows because read length is biologically meaningful.

Learners interested in miRNA sequencing can continue with:

Learn Advanced Transcriptomics: lncRNA, miRNA & Psi-Seq Data Analysis

FastQC Tutorial: How to Analyze Many Samples with MultiQC

Suppose your project contains 40 paired-end samples.

That means:

40 samples × 2 FASTQ files
=
80 FastQC reports

Opening 80 HTML files individually becomes inefficient.

This is where MultiQC becomes extremely useful.

MultiQC scans analysis output directories and combines recognized reports into a single interactive HTML summary. The current MultiQC documentation specifically supports FastQC outputs and can summarize multiple samples together.

Install MultiQC

Using Conda:

conda install -c conda-forge -c bioconda multiqc

or Mamba:

mamba install -c conda-forge -c bioconda multiqc

Run MultiQC

Suppose all your FastQC reports are stored inside:

fastqc_results/

Run:

multiqc fastqc_results/

Or move into your project directory and run:

multiqc .

MultiQC scans supported output files and generates a report such as:

multiqc_report.html

The official MultiQC documentation describes multiqc . as the basic command for scanning the current directory and generating a combined report.

Why Use MultiQC?

Instead of checking:

Sample01
Sample02
Sample03
Sample04
...
Sample80

one at a time, MultiQC allows you to identify:

• Samples with lower quality
• Adapter contamination across the cohort
• GC-content outliers
• Read-count differences
• Sequence-length differences
• Unexpected sample-specific behavior

This is particularly important for large RNA-Seq, WGS, and metagenomics projects.

Example FastQC and MultiQC Workflow

Suppose your directory contains:

Control1_R1.fastq.gz
Control1_R2.fastq.gz
Control2_R1.fastq.gz
Control2_R2.fastq.gz
Disease1_R1.fastq.gz
Disease1_R2.fastq.gz
Disease2_R1.fastq.gz
Disease2_R2.fastq.gz

Create a QC directory:

mkdir fastqc_results

Run FastQC:

fastqc -t 4 -o fastqc_results *.fastq.gz

Run MultiQC:

multiqc fastqc_results/

Now inspect:

multiqc_report.html

This gives you both:

individual FastQC reports

and:

one cohort-level MultiQC summary

What Should You Record from FastQC?

For reproducible analysis, maintain a basic QC record.

For example:

This is more useful than simply writing:

FastQC passed.

Quality control is a process of evidence and decisions.

Common FastQC Mistakes Beginners Should Avoid

Mistake 1: Treating Every FAIL as Fatal

FastQC thresholds are generic.

Some assay-specific characteristics naturally trigger warnings or failures.

Always interpret results in biological context.

Mistake 2: Trying to Make Every Module Green

The purpose of preprocessing is not to obtain an aesthetically perfect FastQC report.

The purpose is to prepare appropriate data for downstream analysis.

Mistake 3: Inspecting Only One Paired-End File

Always inspect both:

R1

and:

R2

because their quality profiles may differ.

Mistake 4: Trimming Without Looking at FastQC

Do not automatically run aggressive trimming before understanding the raw data.

Mistake 5: Ignoring Adapter Content

Significant read-through adapters can affect alignment and downstream analysis.

Mistake 6: Assuming High Duplication Is Always PCR Bias

RNA-Seq and other enrichment-based assays can show biologically meaningful duplication.

Mistake 7: Treating Metagenomic GC Content Like Human WGS

Metagenomics contains multiple organisms.

A complex GC distribution may be completely expected.

Mistake 8: Running FastQC Only Before Trimming

If preprocessing changes the reads, run quality control again.

Mistake 9: Ignoring Sample-to-Sample Differences

A single poor sample can affect downstream statistics.

This is why MultiQC is useful for cohort-level comparison.

Mistake 10: Removing Samples Based Only on FastQC

A FastQC warning alone is rarely sufficient reason to remove a biological sample.

Investigate:

• Sequencing metrics
• Mapping rates
• Sample metadata
• Experimental design
• Downstream QC

before making exclusion decisions.

FastQC Does Not Detect Every NGS Problem

FastQC is extremely useful, but it is not a complete sequencing-quality solution.

FastQC primarily examines characteristics of the reads themselves.

After alignment, you should evaluate additional metrics such as:

• Mapping percentage
• Properly paired reads
• Duplicate rate
• Coverage
• Insert size
• Strand specificity
• Gene-assignment rate
• Contamination
• Library complexity

For RNA-Seq, downstream QC may include:

STAR alignment statistics
↓
featureCounts assignment rate
↓
PCA
↓
Sample clustering

For WGS:

BWA alignment
↓
BAM statistics
↓
Coverage
↓
Duplicate metrics
↓
Variant quality

Think of FastQC as the first layer of NGS quality control, not the final layer.

FastQC vs MultiQC

These tools are complementary.

A typical workflow is:

FASTQ files
↓
FastQC
↓
Multiple FastQC reports
↓
MultiQC
↓
One combined project report

MultiQC can also aggregate reports from many other bioinformatics tools, which makes it useful throughout larger pipelines.

FastQC Tutorial: A Practical Decision-Making Workflow

Instead of asking:

Does my FastQC report contain red symbols?

ask these questions.

1. Is Overall Base Quality Acceptable?

If yes, continue.

If not, investigate low-quality regions.

2. Is Adapter Contamination Present?

If substantial, trim adapters.

3. Are Overrepresented Sequences Expected?

Determine whether they represent:

• Adapters
• Highly expressed transcripts
• rRNA
• Contamination
• Experimental targets

4. Is GC Distribution Biologically Reasonable?

Interpret according to:

• Organism
• Library type
• Metagenomic complexity

5. Is Duplication Expected?

Consider:

• RNA-Seq expression
• PCR amplification
• Targeted sequencing
• Library complexity

6. Are R1 and R2 Similar?

Check both independently.

7. Are Some Samples Different from the Rest?

Use MultiQC.

8. Does Preprocessing Actually Improve the Data?

Run FastQC again after trimming.

This approach is much more scientifically useful than treating FastQC as a simple pass/fail test.

Learn FastQC as Part of a Complete RNA-Seq Workflow

FastQC becomes most useful when you understand what happens after quality control.

For RNA-Seq, the progression is:

FASTQ
↓
FastQC
↓
Read Processing
↓
STAR / HISAT2
↓
BAM
↓
featureCounts
↓
DESeq2
↓
Differentially Expressed Genes

Learn this practically with:

Hands-On RNA-Seq Analysis: From FASTQ to Differential Expression

You can also read:

• RNA-Seq Explained
• Differential Gene Expression Analysis

Learn FastQC as Part of Variant Calling

For genomic variant analysis:

FASTQ
↓
FastQC
↓
Read Preprocessing
↓
BWA
↓
BAM
↓
GATK / BCFtools
↓
VCF
↓
Variant Annotation

Continue with:

Learn Variant Calling: NGS Data Analysis

and read:

• Whole Genome Sequencing Explained
• Variant Calling Explained

Learn FastQC as Part of Metagenomics

A metagenomics pipeline might look like:

FASTQ
↓
FastQC
↓
Read Quality Processing
↓
Host Read Removal
↓
Taxonomic Classification
↓
Abundance Estimation
↓
Functional Analysis
↓
Microbiome Interpretation

For this pathway, explore:

Master Metagenomics and Microbiome Data Analysis Using Linux

This is the most relevant BioInformatix individual course for readers interested in microbial NGS data.

Become an NGS Data Analyst with BioInformatix

FastQC is an essential skill, but professional NGS analysis requires understanding what happens before and after quality control.

You eventually need to connect:

FASTQ
↓
Quality Control
↓
Alignment / Quantification
↓
BAM or Count Matrix
↓
Statistical or Variant Analysis
↓
Biological Interpretation

For learners who want structured training across several major NGS workflows, the primary BioInformatix pathway is:

NGS & Transcriptomics Analyst Bundle: Master RNA-Seq, Variant Calling & Single-Cell Genomics

Instead of learning FastQC as an isolated command, this pathway helps place quality control within complete sequencing-analysis workflows.

You can build practical expertise across areas such as:

• RNA-Seq
• Variant calling
• Advanced transcriptomics
• Single-cell genomics
• Linux-based NGS analysis
• Public sequencing datasets
• Biological interpretation

For aspiring NGS Data Analysts, understanding the complete workflow is far more valuable than memorizing individual software commands.

Recommended FastQC and NGS Learning Roadmap

Stage 1 — Understand NGS

Read:

What Is Next-Generation Sequencing?

Stage 2 — Learn Linux

Start with the free:

Linux Command Line Essentials for Bioinformatics

FastQC and many other sequencing tools are commonly run through Linux command-line environments.

Stage 3 — Understand FASTQ Files

Learn:

• FASTQ structure
• Quality encoding
• Paired-end reads
• Compressed FASTQ files

Stage 4 — Practice FastQC

Run FastQC on:

R1
R2

and interpret each report.

Stage 5 — Learn MultiQC

Combine reports across multiple samples and identify outliers.

Stage 6 — Practice Read Preprocessing

Learn when and why to use:

• fastp
• Cutadapt
• Trimmomatic

Stage 7 — Choose a Complete NGS Workflow

Transcriptomics

Hands-On RNA-Seq Analysis

Genomics

Learn Variant Calling

Microbiome

Master Metagenomics and Microbiome Data Analysis

Stage 8 — Develop Broader NGS Expertise

Continue through the:

NGS & Transcriptomics Analyst Bundle

Frequently Asked Questions

What is FastQC?

FastQC is a quality-control application used to examine high-throughput sequencing data and generate graphical reports showing read quality and sequence characteristics.

What is FastQC used for?

FastQC is used to inspect sequencing characteristics such as:

• Base quality
• Sequence quality
• GC content
• Adapter contamination
• Duplication
• Sequence composition
• Overrepresented sequences

Does FastQC modify FASTQ files?

No.

FastQC analyzes the data and generates reports. It does not trim or modify sequencing reads.

Can FastQC analyze .fastq.gz files?

Yes. FastQC supports gzip-compressed FASTQ files directly.

Do I need to unzip FASTQ files before FastQC?

Usually no.

You can run:

fastqc sample.fastq.gz

directly.

What does a red FastQC result mean?

A red result indicates that the module crossed FastQC’s predefined failure threshold.

It does not automatically mean the dataset is unusable.

Interpret the module according to the sequencing experiment.

Does every FastQC module need to pass?

No.

Some sequencing assays naturally generate warnings or failures in particular modules.

The objective is to understand whether the observed pattern is expected or technically problematic.

What is a good Phred score?

Q30 corresponds approximately to an error probability of 1 in 1,000 bases and is commonly considered high-quality sequencing.

However, appropriate quality expectations depend on the sequencing platform and downstream workflow.

Why does sequencing quality decrease near the read end?

Sequencing confidence can decrease across later sequencing cycles, especially in longer reads.

A modest decline does not automatically require trimming.

Why does RNA-Seq show high duplication?

Highly expressed transcripts can naturally generate many repeated sequencing reads.

High duplication is therefore not automatically a PCR artifact in RNA-Seq.

Why does RNA-Seq fail per-base sequence content?

Library-preparation and priming biases can produce non-random nucleotide composition, particularly near read starts.

Interpret this module in the context of the RNA-Seq protocol.

Should I always trim reads after FastQC?

No.

Only preprocess reads when the QC results and downstream workflow justify it.

What tools can remove sequencing adapters?

Common tools include:

• fastp
• Cutadapt
• Trimmomatic

Should I run FastQC again after trimming?

Yes.

Running FastQC again allows you to verify that the intended technical problem was improved.

What is MultiQC?

MultiQC aggregates outputs from FastQC and many other bioinformatics programs into a single project-level report.

What is the difference between FastQC and MultiQC?

FastQC analyzes individual sequencing files.

MultiQC summarizes outputs from many samples and tools.

Do I run FastQC before alignment?

Yes. Raw-read FastQC is normally performed before alignment.

Additional quality-control metrics should then be examined after alignment.

Is FastQC used in RNA-Seq?

Yes. FastQC is widely used at the beginning of RNA-Seq pipelines.

Is FastQC used in variant calling?

Yes. Raw-read quality assessment is an important early step in genomic variant-analysis workflows.

Is FastQC used in metagenomics?

Yes, although some FastQC modules—especially sequence composition and GC-content modules—must be interpreted carefully because metagenomic samples contain multiple organisms.

Final Thoughts

This FastQC Tutorial provides the foundation for evaluating raw NGS sequencing data before beginning downstream analysis.

The most important workflow to remember is:

Raw FASTQ
↓
FastQC
↓
Interpret the Report
↓
Preprocess Only if Necessary
↓
FastQC Again
↓
Downstream NGS Analysis

FastQC should help you answer questions such as:

• Are the base-quality scores acceptable?
• Does quality deteriorate strongly across the read?
• Are sequencing adapters present?
• Are unusual sequences overrepresented?
• Is sequence duplication expected?
• Does the GC profile make biological sense?
• Are R1 and R2 behaving similarly?
• Are some samples clearly different from the rest?

Most importantly:

Do not treat FastQC as a traffic-light system where green means usable and red means unusable.

Quality-control results must always be interpreted according to the library preparation, sequencing technology, organism, and downstream analysis.

If your goal is transcriptomics, continue with the Hands-On RNA-Seq Analysis course.

If your goal is genomic variant discovery, continue with Learn Variant Calling: NGS Data Analysis.

If you are working with microbial communities, continue with Master Metagenomics and Microbiome Data Analysis Using Linux.

For broader training across multiple sequencing workflows, follow the NGS & Transcriptomics Analyst Bundle as the primary learning pathway.