FastQC is a program designed to spot potential problems in high throughput sequencing datasets. It runs a set of analyses on one or more raw sequence files in fastq or bam format and produces a report which summaries the results.

FastQC Screenshot

FastQC will highlight any areas where this library looks unusual and where you should take a closer look. The program is not tied to any specific type of sequencing technique and can be used to look at libraries coming from a large number of different experiment types (Genomic Sequencing, ChIP-Seq, RNA-Seq, BS-Seq etc.).

This project page contains the source code for the application and is only really useful to people wanting to develop new functionality or trace bugs in FastQC. If you just want to run the program, then you want to go to the project web page where you can download the compiled packages for Windows, OSX, and Linux.

Resources and Learning Material

How to Run a FastQC Analysis:

This example demonstrates how to perform a FastQC analysis on a sequencing data file. To execute the example, you'll need the FastQ file test.fastq and the script

Downloading the Files

You can download the necessary files through these links:

  • test.fastq - The FastQ file containing sequencing data.
  • - The shell script to run the FastQC analysis.

Steps to Execute on Our Platform

  1. Upload Files: Click on the folder icon next to the Volume line and upload both test.fastq and to the working directory of your project.
  2. Set Working Directory: Make sure the Working Directory is set to /data (or the mounted volume to entered!).
  3. Configure Run Script: In the Run Script field, enter as the input.
  4. Create the Project: Click on Create to prepare your project environment.
  5. Run the Container: Go to the project tab and start the container to run the script.

Following these instructions will initiate the FastQC analysis on test.fastq, providing valuable insights into the quality of the sequencing data.

Tags for FastQC

  1. Sequencing Data Analysis
  2. Quality Control
  3. Bioinformatics
  4. NGS
  5. Data Quality Assessment

Explore FastQC Documentation

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