Overview of relevant sequencing assays and when they should be applied
9.40-10.45
Lecture: “Overview of sequencing technologies”
Explain the differences between short-read and long-read sequencing technologies
Describe the general idea behind Illumina and Nanopore sequencing, and the differences between them
Summarize the difference between single end reads vs paired end reads
Outline common reasons for failed sequencing, including sequencing best practices
Summarize pros and cons of each sequencing technology
11.00 – 12.00
Interactive Lecture “Sequencing Quality”
Understand the fastq format – and why it is gzipped
Explain what a phred-score is and how it is used to assess sequencing accuracy
Understand when and why read trimming can be necessary
13.00-15.00
Lecture/Practical: “Quality control of sequencing data”
Name and execute software used to facilitate QC on raw data for Nanopore
Understand the relation between read sizes and library preparation
Overview of read count in relation to sequencing depth and error assessment
Insights into overall read quality on the basis of base quality scoring
Introduction to assessment of base contents
Generate scripts for performing QC analysis
Understand general QC parameters
Discuss overall filtration criteria in sequencing
Discuss aspects of distinguishing good - from bad -samples
15:00- 16:30
Lecture/Practical:
“Contamination control, sample aggregation, and wrap-up"
Introduction to contamination control with Kraken
Inspect Kraken report
Aggregate QC results into a single report
Compare QC results from a whole dataset perspective
Discussion of the overall differences in determining QC parameters for Illumina and Nanopore
Discuss Advantages and pitfalls of each sequencing platform
Details
In this module participants will be introduced to sequencing assays, applicable for species determination and characterization of bacterial isolates. They will get an overview of Illumina and Nanopore sequencing technologies and learn when to apply which technology. Participants will learn how to generate, inspect, and interpret QC reports and how to perform quality control of raw data with Kraken and learn how to inspect the output generated by Kraken.
Finally, participants will be introduced to MutliQC for aggregating QC results, for dataset wide comparison.
By the end of the module, participants will be able to independently execute and assess QC on raw data.
