Navigating Eukaryotic Genome Annotation Pipelines: A Route Map to BRAKER, Galba, and TSEBRA (2403.19416v1)

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.

• The paper demonstrates that integrating RNA-Seq and proteomic data via BRAKER and TSEBRA enhances gene prediction accuracy across diverse genome sizes.
• The paper outlines methodological advancements, detailing BRAKER versions and Galba’s efficient protein-to-genome alignment for large genomic datasets.
• The paper emphasizes practical guidelines for data preparation and computational workflow optimization to achieve precise and sensitive eukaryotic genome annotations.

Navigating Eukaryotic Genome Annotation Pipelines: A Route Map to BRAKER, Galba, and TSEBRA

Genome annotation remains a critical task in eukaryotic genomic research, particularly for understanding protein-coding genes. The paper under discussion provides a comprehensive comparison and practical guidelines for applying two state-of-the-art genomic annotation pipelines—BRAKER and Galba—together with the TSEBRA combiner. These pipelines are accompanied by detailed procedures to optimize gene prediction, ensuring precision and sensitivity in varying genomic conditions.

BRAKER, in its iterations 1 through 3, integrates the GeneMark-ETP and AUGUSTUS gene finders. Each version of BRAKER iteratively increases its capability to leverage extrinsic data to guide gene prediction with TSEBRA's assistance, which merges gene predictions, enhancing both sensitivity and specificity.

Key Highlights of BRAKER:

1. BRAKER1 relies solely on RNA-Seq data to guide its self-training gene prediction paradigm. Though useful, its performance diminishes with larger genome sizes (>1Gbp) due to a reliance on rich transcriptomic input.
2. BRAKER2 introduced Proteomic data into its prediction arsenal, optimizing accuracy even without closely related species proteins, thus broadening the pipeline's utility especially when only a clade-specific protein database is available.
3. BRAKER3 represents the epitome of this evolutionary branch, capable of assimilating both RNA-Seq and proteomic data, substantially elevating the prediction accuracy across various genome sizes. Notably, BRAKER3's application is limited in extremely large genomes where test runs have not been validated.
4. TSEBRA's Role is to balance transcript selection processes, ensuring the generated annotations benefit maximally from input datasets, particularly in diverse RNA-Seq and proteomic environments.

Conversely, Galba adopts a simplified approach using direct protein-to-genome alignments facilitated by miniprot. This design proves advantageous in large genomes where conventional aligner and combiner methods might falter. Galba's emphasis on precise alignment makes it a preferable choice over BRAKER2 for genomes exceeding 1Gbp.

Galba's Methodology and Efficiency:

• Galba sidesteps the reliance on voluminous databases by focusing on highly relevant protein sets, thus reducing computational load and enhancing prediction in more extensive genomic landscapes.
• The pipeline iteratively refines gene predictions through evidence-supported alignment, invoking techniques similar to those in BRAKER but optimized for its primary alignment strategy.

The paper's extensive discourse on computational prerequisites, procedural steps for each pipeline version, and input data preparation underscores the importance of optimizing predictive workflows according to project-specific genomic contexts. Its delineation of using RNA-Seq data, regardless of short or long read formats, reiterates the importance of upstream data quality and alignment precision.

Moreover, with mentions of improvements such as compleasm for BUSCO completeness checks, the research positions these pipelines as adaptable frameworks within the larger genomic tool ecosystem. This adaptability is critical for further enhancing genomes' annotated quality, be it for functional genomics, evolutionary biology, or applied sciences.

Implications for Future AI and Genomics Interplay:

As AI continues to push computational boundaries, one can anticipate future developments wherein these pipelines integrate more nuanced machine-learning models. Such integration could optimize the prediction fidelity further by dynamically adapting to genomic intricacies—potentially revolutionizing eukaryotic gene annotation processes on a grander scale.

In summation, the methodologies detailed in the paper provide crucial directives pivotal for genomic researchers aiming to harness cutting-edge computational pipelines. The delineated approaches do not only facilitate precision in genomic annotation but also pave paths for scalable, future-ready genomic analyses.