The value of a genome assembly hinges on its annotation quality. However, structural annotation remains challenging due to variability in genome size, complexity, and the diversity and quality of supporting data.

Accurate annotation is essential for downstream analyses — variation detection, comparative genomics, functional genomics, systems biology, genome editing, and synthetic biology all rely on accurate gene models.

The Earlham Institute has developed a number of tools and pipelines to support high-quality annotation, including:

• Mikado (PMID: 30052957): Scores and selects transcript models from multiple assembly sources to create reliable gene annotations.
• Portcullis (PMID: 30418570): Improves splice-junction accuracy for high-confidence gene structures.

Both were foundational in the wheat genome annotation by the IWGSC (PMID: 30115783).

• REAT (Robust and Extendable Eukaryotic Annotation Toolkit) and minos:

These pipelines integrate Mikado and Portcullis along with evidence-guided prediction tools to produce high-quality annotations. 

We have used them to support projects across plants, fungi, insects, protists, and fish within consortia such as the European Reference Genome Atlas (ERGA) and Darwin Tree of Life.

In this 3-day virtual course, you will:

• Get an overview of Next Generation Sequencing technologies relevant to genome annotation, gaining a deeper understanding of the benefits of each platform.
• Understand what to look for in a sample that will pass quality control and that will likely succeed in producing viable sequencing data, including how to assess the quality of RNA-Seq data.
• Learn about reference guided transcriptome assembly and steps for processing short and long read data.
• Learn about alternative approaches for annotating protein coding genes in eukaryotic species utilizing transcriptome and homology data, via projection and evidence guided gene prediction. Discuss the challenges of annotation in different contexts.
• Hands on experience of annotation tools including tools and pipelines developed at the Earlham Institute. You will build gene models, explore the use of combiners/choosers for integrating alternative gene predictions and assess the accuracy of different annotation tools.

Who is this training for?

Advanced PhD students and post-doctoral researchers who are undertaking projects involving annotating a genome assembly and looking to improve your awareness of different approaches and pipelines.

Prerequisites

You are expected to have experience with using the command line, and should be comfortable using the functions covered in the Software Carpentry lesson, The Unix Shell. We suggest you refresh your memory of these lessons if needed.