Quality Control in Sequencing Experiments

About the course  |  Intended audience  |  Prerequisites  |  Content details  |  Scheduled events

About the course

Quality control is an essential component of sequencing experiments and can help identify potential problems early in the analysis process. This course introduces the common pitfalls of short-read sequencing studies and provides approaches for visualisation and quality assessment of sequencing data.

Participants will gain an understanding of Illumina sequencing and the quality control metrics that can be extracted from sequencing reads, including base quality scores. The course also covers how quality metrics vary across different library types and how to distinguish between expected and unexpected quality control results.

Participants will be introduced to key software tools for sequencing quality assessment, including FastQC, FastQ Screen, and MultiQC. The emphasis of the course is on understanding and interpreting quality reports rather than on running the software itself.

By the end of the course, participants should be able to critically assess sequencing quality reports and recognise potential issues in sequencing experiments.

Teaching is primarily hands-on, with short presentations, demonstrations, and practical exercises introducing the concepts and methods needed to assess sequencing data quality.

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Intended audience

This course is suitable for:

• researchers and students who use sequencing as part of their work or research
• participants who want to understand how to assess the quality of sequencing data
• researchers seeking practical experience in interpreting sequencing quality reports

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Prerequisites

Participants should have:

• a working knowledge of the UNIX command line

The following experience is recommended:

• a basic understanding of high-throughput sequencing technologies (watch this excellent iBiology video)

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Content details

The course covers the following topics:

• Introduction to Illumina sequencing
  Introduces the principles of Illumina sequencing and the stages of the sequencing process that can influence data quality.
• Quality metrics for sequencing data
  Covers commonly used quality control metrics that can be extracted from sequencing reads, including base quality scores and other indicators of sequencing performance.
• Quality control across different library types
  Introduces how quality metrics vary between different sequencing library types and how to distinguish expected patterns from potential problems.
• Sequencing failures and quality assessment
  Covers common ways in which sequencing experiments may fail and demonstrates how quality control metrics can be used to detect and diagnose these issues.
• Quality control software
  Introduces widely used tools for assessing sequencing data quality, including FastQC, FastQ Screen, and MultiQC, with an emphasis on interpreting the reports generated by these tools.
• Practical interpretation of quality reports
  Provides hands-on experience in evaluating sequencing quality reports and identifying potential issues in sequencing datasets.

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