[Paper Review] Navigating Eukaryotic Genome Annotation Pipelines: A Route Map to BRAKER, Galba, and TSEBRA

Tomáš Brůna, Lars Gabriel+1 more|arXiv (Cornell University)|Mar 28, 2024

Genomics and Phylogenetic Studies11 citations

TL;DR

A practical guide detailing how to run BRAKER, Galba, and TSEBRA for eukaryotic genome annotation, including inputs, containment, and workflows, with insect-focused guidance.

ABSTRACT

Annotating the structure of protein-coding genes represents a major challenge in the analysis of eukaryotic genomes. This task sets the groundwork for subsequent genomic studies aimed at understanding the functions of individual genes. BRAKER and Galba are two fully automated and containerized pipelines designed to perform accurate genome annotation. BRAKER integrates the GeneMark-ETP and AUGUSTUS gene finders, employing the TSEBRA combiner to attain high sensitivity and precision. BRAKER is adept at handling genomes of any size, provided that it has access to both transcript expression sequencing data and an extensive protein database from the target clade. In particular, BRAKER demonstrates high accuracy even with only one type of these extrinsic evidence sources, although it should be noted that accuracy diminishes for larger genomes under such conditions. In contrast, Galba adopts a distinct methodology utilizing the outcomes of direct protein-to-genome spliced alignments using miniprot to generate training genes and evidence for gene prediction in AUGUSTUS. Galba has superior accuracy in large genomes if protein sequences are the only source of evidence. This chapter provides practical guidelines for employing both pipelines in the annotation of eukaryotic genomes, with a focus on insect genomes.

Motivation & Objective

• Provide practical guidelines for applying BRAKER and Galba to annotate protein-coding genes in eukaryotic genomes.
• Explain how to prepare and select transcriptome and protein evidence for accurate predictions.
• Describe containerized deployment and HPC considerations to enable reproducible analyses.
• Discuss the roles of TSEBRA in combining predictions and improving gene sets.

Proposed method

• Describe the BRAKER and Galba pipelines and how they integrate evidence from RNA-Seq, proteins, and predictions.
• Explain how TSEBRA combines predictions from AUGUSTUS and GeneMark-based outputs for improved gene sets.
• Outline containerized deployment using Docker and Singularity for reproducible workflows.
• Provide input preparation workflows for genome masking, transcriptome data, and protein databases.
• Offer step-by-step instructions and toy/demonstration datasets to practice running the pipelines.

Figure 1: Schematic view of the BRAKER [ 1 , 2 , 3 ] and Galba [ 4 ] pipelines. A: In BRAKER, GeneMark-ET, -EP, or -ETP [ 7 , 8 , 9 ] is trained (using extrinsic data upon availability) and used to predict an initial set of genes (genemark.gtf). This set of genes is filtered, and the resulting high-

Experimental results

Research questions

• RQ1 How do BRAKER and Galba utilize different extrinsic evidence sources (transcriptome and proteins) to predict gene structures?
• RQ2 What are the practical steps to set up and run BRAKER, Galba, and TSEBRA in containerized environments?
• RQ3 How does TSEBRA influence the final gene set when combining BRAKER and Galba predictions?
• RQ4 What input data formats and preprocessing steps maximize accuracy and minimize runtime for these pipelines?
• RQ5 How do these pipelines perform on insect genomes and scalable genome sizes with varying evidence availability?

Key findings

• BRAKER3 provides high accuracy by integrating RNA-Seq and large protein databases and uses TSEBRA to combine predictions.
• Galba offers strong accuracy on large genomes using protein-to-genome spliced alignments with miniprot and training AUGUSTUS.
• TSEBRA serves as a combiner to improve gene sets by merging AUGUSTUS and GeneMark predictions.
• Iso-Seq data can be incorporated with a modified GeneMark-ETP container for BRAKER3 workflows.
• Genome masking and careful handling of repeat elements are crucial for reliable gene prediction.

Figure 3: Decision scheme for picking a suitable pipeline out of BRAKER3, BRAKER2, BRAKER1 (in combination with BRAKER2 and TSEBRA), and Galba.