Summary and Schedule
Welcome to the Genome Annotation Workshop
This workshop provides a hands-on introduction to genome annotation, covering structural and functional annotation techniques optimized for the RCAC cluster. You’ll learn best practices for gene prediction using BRAKER3, Helixer, and Easel, as well as functional annotation with EnTAP.
Designed for researchers and bioinformaticians, this workshop will equip you with the skills to generate high-quality, reproducible genome annotations, leveraging transcriptomic, proteomic, and homology-based evidence. You’ll also explore strategies for quality assessment and troubleshooting common challenges in annotation workflows.
Learning Objectives
By the end of this workshop, you will be able to:
- Set up and run multiple genome annotation pipelines (BRAKER3, Helixer, EASEL) on an HPC cluster
- Perform functional annotation using EnTAP to assign gene functions, GO terms, and pathway associations
- Assess annotation quality using BUSCO, OMArk, and structural metrics
- Compare gene predictions across tools and select the most appropriate approach for your organism
Prerequisites
- Basic command-line skills (navigating directories, editing files, running commands)
- An account on the Purdue RCAC cluster (Negishi and Gilbreth)
- Familiarity with basic genomics concepts
See the Setup page for detailed instructions on data access and software configuration.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction to Genome Annotation |
What is genome annotation, and why is it important? What are the different types of genome annotation? What challenges make genome annotation difficult? What data sources are used to improve annotation accuracy? How does the annotation process fit into genomics research? |
| Duration: 00h 20m | 2. Annotation Strategies |
What are the different strategies used for genome annotation? How do various methods predict genes, and what are their strengths and limitations? What role do evidence play in improving gene predictions? How do deep learning and large models enhance genome annotation accuracy? What tools are used in this workshop, and how do they fit into different strategies? |
| Duration: 00h 42m | 3. Annotation Setup |
How should files and directories be structured for a genome annotation
workflow? Why is RNA-seq read mapping important for gene prediction? How does repeat masking improve annotation accuracy? What preprocessing steps are necessary before running gene prediction tools? |
| Duration: 00h 59m | 4. Annotation using BRAKER |
What is BRAKER3? What are the different scenarios in which BRAKER3 can be used? How to run BRAKER3 with different input requirements? What are the different output files generated by BRAKER3? |
| Duration: 01h 41m | 5. Annotation using Helixer |
How to predict genes using Helixer? How to download trained models for Helixer? How to run Helixer on the HPC cluster (Gilbreth)? |
| Duration: 02h 03m | 6. Annotation using Easel |
What are the steps required to set up and run EASEL on an HPC
system? How is Nextflow used to manage and execute the EASEL workflow? What configuration files need to be modified before running EASEL? How do you submit and monitor an EASEL job using Slurm? |
| Duration: 02h 30m | 7. Functional annotation using EnTAP |
What is EnTAP, and how does it improve functional annotation for
non-model transcriptomes? How does EnTAP filter, annotate, and assign functional roles to predicted transcripts? What databases and evidence sources does EnTAP integrate for annotation? What are the key steps required to set up and execute EnTAP on an HPC system? |
| Duration: 02h 52m | 8. Annotation Assessment |
How to assess the quality of a genome annotation? What are the different tools available for assessing the quality of a genome annotation? How to compare the predicted annotation with the reference annotation? How to measure the number of raw reads assigned to the features predicted by the annotation? |
| Duration: 03h 24m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Instructors
Arun Seetharam, Ph.D.: Arun is a lead bioinformatics scientist at Purdue University’s Rosen Center for Advanced Computing. With extensive expertise in comparative genomics, genome assembly, annotation, single-cell genomics, NGS data analysis, metagenomics, proteomics, and metabolomics. Arun supports a diverse range of bioinformatics projects across various organisms, including human model systems.
Charles Christoffer, Ph.D.: Charles is a Senior Computational Scientist at Purdue University’s Rosen Center for Advanced Computing. He has a Ph.D. in Computer Science in the area of structural bioinformatics and has extensive experience in protein structure prediction.
Schedule
| Time | Session |
|---|---|
| 8:30 AM | Arrival & Setup |
| 9:00 AM | Introduction & Annotation Strategies – Overview of genome annotation, structural vs. functional annotation, key challenges, and selecting the right pipeline |
| 10:30 AM | Break |
| 10:40 AM | Gene Annotation with BRAKER – Running BRAKER for ab initio and RNA-seq-supported annotation, gene model evaluation |
| 12:00 PM | Lunch Break |
| 1:00 PM | Interpreting BRAKER Results & Gene Annotation with Helixer – Reviewing BRAKER outputs, refining predictions, and using Helixer for deep-learning-based annotation |
| 2:30 PM | Gene Annotation with EASEL – Running the EASEL Nextflow pipeline for automated genome annotation |
| 2:50 PM | Break |
| 3:10 PM | Functional Annotation with EnTAP & Annotation Quality Assessment – Assigning gene functions, GO term mapping, evaluating completeness with BUSCO, and benchmarking gene models |
| 4:30 PM | Wrap-Up & Discussion – Troubleshooting, Q&A, and next steps |
What is not covered
- Gene prediction using MAKER
- Evidence based gene prediction (EviAnn, EVidenceModeler)
- Genome assembly
- Comparative analyses
Pre-requisites
- Basic knowledge of genomics
- Basic knowledge of command line interface
- Basic knowledge of bioinformatics tools
Data Sets
To copy only data:
The worked out folder is available at
/depot/workshop/data/annotation_workshop-results on the
training cluster. You can copy the data to your scratch space using the
following command:
Only use this if you are unable to finish the exercises in the workshop.
You only need one directory on Gilbreth cluster. See below for details.
Software Setup
Details
SSH key setup for different systems is detailed in the expandable sections below.