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The value of a genome assembly depends heavily on the quality of its accompanying genome annotation.
Automated procedures for annotation are therefore required that are robust to different genome characteristics and can efficiently and accurately annotate not only high-quality reference genomes but also large, fragmented “draft” genomes. The benefits of high-quality annotated genomes are well known - more accurate and complete annotation enables better interpretation of genome information within and across species, aiding researchers in their work to understand biological processes.
Earlham Institute (EI) has developed a number of tools to support high quality annotation, including Mikado (PMID: 30052957), which is an open-source Python3 and Cython program that provides a framework for integrating transcripts from multiple sources into a consolidated set of gene annotations, and Portcullis (PMID: 30418570), which is a tool to aid accurate splice junction detection.
Both tools were utilised to aid the annotation of the wheat genome as part of the International wheat genome sequencing consortium (IWGSC, PMID: 30115783) and are being used to support EIs work in large collaborative genome sequencing projects such as the Wheat 10+ and Darwin Tree of Life. To see a full list of tools featured in the workshop, please see the Further Information tab.
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 de novo and 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 from transcriptome data, explore the use of combiners/choosers for integrating alternative gene predictions (EVM / MINOS) 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 or generating transcriptome assemblies, and looking to improve your awareness of different approaches and pipelines.
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.
This training forms part of our BBSRC National Capability in Advanced Training