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FastQC is a free quality-control tool for high-throughput sequencing data. It reads a sequence file, runs a fixed set of checks, and produces an HTML report with a pass, warn or fail flag for each check. It reports on one file at a time and does not change your reads. Use it to decide whether a run looks usable and where to look next, not as an automatic accept-or-reject verdict.
What FastQC is
FastQC is developed by Babraham Bioinformatics, is written in Java and is released under GPL v3 or later. Its project page describes its job as giving “a quick impression of whether your data has any problems”. It accepts BAM, SAM and FastQ input (any FastQ variant), and its changelog lists gzip- and bzip2-compressed input and standard input as well. The latest version in that changelog is 0.12.0. If you need a refresher on the input format, see our FastQ format explained guide.
How FastQC is run
FastQC runs either as an interactive graphical application or offline from the command line, and it needs a Java Runtime Environment. Command-line options documented on the project page include --quiet, --nogroup, --svg, --memory and --min-length. Output is a set of summary graphs and tables in an HTML report, with a pass, warn or fail icon beside each section. The thresholds behind those icons are configurable.
The 12 analysis modules
The FastQC documentation lists these modules: Basic Statistics, Per Base Sequence Quality, Per Sequence Quality Scores, Per Base Sequence Content, Per Sequence GC Content, Per Base N Content, Sequence Length Distribution, Duplicate Sequences, Overrepresented Sequences, Adapter Content, Kmer Content and Per Tile Sequence Quality. The report is organised by these module names, so a “FastQC results” question usually becomes “what does this module’s flag mean for my experiment”.
How warn and fail are decided
The flags are rule-based and numeric. The two modules below are the ones whose rules are stated explicitly in the FastQC help pages.
| Module | Warn when | Fail when | Caveat stated in the documentation |
|---|---|---|---|
| Per Base Sequence Quality | Lower quartile for any base is below 10, or median for any base is below 25 | Lower quartile for any base is below 5, or median for any base is below 20 | General quality degradation is the common cause; a short, earlier loss of quality can also trigger the flag |
| Duplicate Sequences | Non-unique sequences exceed 20% of the total | Non-unique sequences exceed 50% of the total | Only sequences first seen in the first 100,000 sequences of each file are tracked, and reads longer than 75 bp are truncated to 50 bp for the comparison |
Because the duplication check samples the start of the file and truncates long reads, it is an estimate. Our fastp guide explains why FastQC and fastp duplication rates are not the same measurement.
FastQC vs MultiQC vs fastp
These tools do different jobs and are normally used together rather than as alternatives.
| Tool | Role | Source |
|---|---|---|
| FastQC | Analyses a single sequence file and reports quality metrics; does not modify reads | Babraham Bioinformatics project page |
| MultiQC | Parses results and statistics from other tools’ outputs across many samples and builds one standalone HTML report. It “doesn’t do any analysis for you”. It has a FastQC module that reads fastqc_data.txt files or *_fastqc.zip archives |
Seqera MultiQC documentation |
| fastp | Preprocessing: trims and filters reads, and writes its own quality report | See our fastp guide |
In practice: FastQC tells you what a file looks like, MultiQC puts many such reports side by side, and fastp is what you run when the report says reads need trimming or filtering. MultiQC’s FastQC module keeps the FastQC pass, warn and fail colours by default and can switch to standard MultiQC colours with status_checks: false.
Why are there two FASTQ files?
Paired-end sequencing produces two files per sample, conventionally called R1 (forward reads) and R2 (reverse reads), and FastQC treats each file as a separate input. You therefore get two FastQC reports per sample, and the quality profile of R1 and R2 often differs. The R1/R2 convention is covered in our FastQ format guide.
Common misunderstandings
- A fail is not automatically a bad run. The flags are fixed numeric rules applied to every library, and the documentation lets you configure the thresholds. Read the graph behind the flag before discarding data.
- FastQC does not clean data. It only reports. Trimming and filtering happen in a separate tool.
- Duplication is not measured over the whole file. See the 100,000-sequence and 50 bp limits above.
- MultiQC is not a replacement for FastQC. It summarises outputs that other tools produce.
A pre-analysis QC checklist built from the sources above
- Run FastQC on every FASTQ file, R1 and R2 separately.
- For each file, open Per Base Sequence Quality and compare against the warn and fail rules in the table.
- Treat the Duplicate Sequences flag as a sampled estimate.
- Where there are many samples, aggregate the outputs with MultiQC.
- If reads need trimming or filtering, use a preprocessing tool such as fastp, then re-run FastQC to see the effect.
For wider context on sequencing workflows, see our guide to what metagenomics is.
FAQ
What is FastQC?
FastQC is a Java quality-control tool from Babraham Bioinformatics that checks BAM, SAM or FastQ files with 12 analysis modules and writes an HTML report with pass, warn and fail flags.
Why are there two FASTQ files?
Paired-end sequencing writes forward (R1) and reverse (R2) reads to separate files. FastQC analyses each file on its own, giving two reports per sample.
What is the difference between FastQC and MultiQC?
FastQC analyses one sequence file. MultiQC parses the outputs of FastQC and other tools across many samples into one report and performs no analysis itself.
What is the difference between fastp and FastQC?
FastQC reports on reads without changing them. fastp trims and filters reads and writes its own report; their duplication rates are not the same measurement.
Sources
- Babraham Bioinformatics, FastQC project page and Help pages (Analysis Modules, Per Base Sequence Quality, Duplicate Sequences), read 2026-11-07.
- Seqera, MultiQC documentation and FastQC module, read 2026-11-07.
