Annotation Strategies

Last updated on 2025-04-22 | Edit this page

Overview

Questions

  • 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?

Objectives

  • Understand the principles behind different genome annotation approaches.
  • Learn how intrinsic, extrinsic, and hybrid methods predict gene structures.
  • Explore the role of multi-evidence approaches, including transcriptomics, proteomics, and functional annotation.
  • Introduce modern machine learning and large model-based methods for gene annotation.
  • Familiarize with annotation tools used in this workshop and their specific applications.

Introduction

  • Genome annotation identifies functional elements in a genome, including protein-coding genes, non-coding rnas, and regulatory regions
  • Annotation strategies vary between evidence-based, ab initio, and hybrid approaches, each with strengths and limitations
  • Selecting the right method depends on genome complexity, available transcriptomic and proteomic data, and the need for accuracy versus sensitivity

Annotation Methods

Intrinsic (Ab Initio) Methods

  • Predict genes based on sequence features without external evidence.
  • Use statistical models such as hidden Markov models (HMMs) to recognize coding regions, exon-intron boundaries, and gene structures.
  • Rely on nucleotide composition patterns like codon usage, GC content, and splice site signals.
  • Perform well in well-studied genomes but struggle with novel genes and complex gene structures.

Extrinsic (Evidence-Based) Methods

  • Utilize external data such as RNA-seq, ESTs, and protein homology to guide gene predictions.
  • Align transcriptomic and proteomic data to the genome to refine exon-intron structures.
  • Improve annotation accuracy by incorporating evolutionary conservation and known gene models.
  • Require high-quality reference datasets, making them less effective in newly sequenced organisms with little prior data.

Hybrid Methods

  • Combine ab initio predictions with evidence-based approaches to improve accuracy and completeness.
  • Use gene predictions from statistical models and refine them with transcriptomic and proteomic data.
  • Balance sensitivity (detecting novel genes) and specificity (avoiding false positives).
  • Often used in large-scale genome annotation projects where both approaches complement each other.

Multi-Evidence based methods

  • Incorporate diverse biological data sources to improve gene prediction accuracy.
  • Use ribosome profiling (Ribo-seq) and proteomics to confirm protein-coding potential.
  • Leverage poly(A) signal presence to distinguish mature transcripts from non-coding RNA.
  • Integrate RNA-seq, long-read sequencing, and EST data to refine exon-intron structures.
  • Provide direct experimental validation, reducing reliance on purely computational models.

Large Model-Based Methods

  • Utilize deep learning and large language models (LMMs) trained on vast genomic datasets.
  • Predict gene structures by recognizing sequence patterns learned from diverse organisms.
  • Adapt to various genome complexities without requiring manually curated training data.
  • Identify non-canonical gene structures, alternative splicing events, and novel genes.
  • Example: Helixer, a GPU-based model, predicts genes using high-dimensional sequence patterns, improving over traditional HMM-based methods.

What do we annotate in the genome?

  • Protein-coding genes: Genes that encode proteins, identified based on open reading frames (ORFs) and coding sequence features.
  • Pseudogenes: Non-functional remnants of once-active genes that have accumulated mutations.
  • Non-coding RNAs (ncRNAs): Functional RNAs that do not code for proteins, including tRNAs, rRNAs, and lncRNAs.
  • Upstream Open Reading Frames (uORFs): Small ORFs in the 5’ untranslated region (UTR) that can regulate translation.
  • Alternative Splicing Variants: Different transcript isoforms from the same gene, generated by exon-intron rearrangements.
  • Repeat Elements and Transposons: Mobile DNA sequences that can impact gene function and genome structure.
  • Regulatory Elements: Promoters, enhancers, and other sequences controlling gene expression.

For this workshop, we focus only on protein-coding genes and their annotation.

Annotation Workflows Used

1. BRAKER3: Combines GeneMark-ET and AUGUSTUS to predict genes in eukaryotic genomes.

  • Integrates GeneMark and AUGUSTUS with RNA-Seq and/or protein evidence.
  • Supports various data types or runs without them.
  • Fully automated training and gene prediction.
  • Requires evidence data for high accuracy; performs poorly without it.
  • Struggles with scalability on larger genomes.

2. Helixer: A deep learning-based gene prediction tool that uses a convolutional neural network (CNN) to predict genes in eukaryotic genomes.

  • Utilizes a deep learning model trained on diverse animal and plant genomes, reducing the need for retraining on closely related species.
  • Provides probabilities for every base, enabling detailed predictions for intergenic, untranslated, coding, and intronic regions.
  • Outperforms traditional tools like AUGUSTUS in both base-wise metrics and RNA-Seq consistency.
  • Handles large and complex eukaryotic genomes effectively, even for species with longer genes.
  • Requires no additional data except genome – no repeatmasking!
  • Cannot predict alternative splicing events or spliced start codons.
Helixer
Helixer

3. EASEL: Efficient, Accurate, Scalable Eukaryotic modeLs: eukaryotic genome annotation tool

  • Combines machine learning, RNA folding, and functional annotations to enhance prediction accuracy.
  • Leverages RNA-Seq, protein alignments, and additional evidence for gene prediction.
  • Built on Nextflow, ensuring portability across HPC systems and compatibility with Docker and Singularity.
  • Features EnTAP integration for streamlined functional annotation.
  • Provides multiple formats (GFF, GTF, CDS, protein sequences) for downstream analysis.
  • Employs BUSCO for quality assessment and automates parameter optimization.
EASEL
EASEL

4. EnTAP: eukaryotic non-model annotation pipeline (functional annotation)

  • Optimized for functional annotation of transcriptomes without reference genomes, addressing challenges like fragmentation and assembly artifacts.
  • Much faster than traditional annotation pipelines.
  • Integrates taxonomic filtering, contaminant detection, and informativeness metrics for selecting optimal alignments.
  • Enables annotation across diverse repositories (e.g., RefSeq, Swiss-Prot) with customizable database integration.
  • Includes gene family assignments, protein domains, and GO terms for enriched annotation.
  • User-friendly, Unix-based pipeline suitable for HPC environments, combining simplicity with high throughput.
EnTAP
EnTAP

How to we access gene predictions?

  • Busco and Omark assess gene set completeness by checking the presence of conserved orthologs across species, helping identify missing or fragmented genes
  • Gff3 metrics provide structural statistics on gene models, including exon-intron distribution, gene lengths, and coding sequence properties, highlighting inconsistencies in annotation
  • Feature assignment using RNA-seq read mapping quantifies how well predicted genes capture transcriptomic evidence, indicating potential missing or incorrect annotations
  • Reference comparison with known annotations measures sensitivity and precision, identifying correctly predicted, missed, or falsely annotated genes
  • Multiple validation methods combine structural, functional, and comparative assessments to improve confidence in gene predictions and refine annotation quality

Key Points

  • Genome annotation strategies include ab initio, evidence-based, hybrid, multi-evidence, and large model-based methods, each with different strengths and data requirements.
  • Ab initio methods predict genes using sequence patterns, while evidence-based methods use RNA-seq, proteins, and evolutionary conservation for refinement.
  • Multi-evidence approaches integrate diverse biological data such as ribosome profiling, proteomics, and long-read RNA-seq to improve accuracy.
  • Large model-based methods, like Helixer, use deep learning to predict genes without requiring manual training, outperforming traditional tools in complex genomes.
  • The workshop focuses on protein-coding gene annotation using tools like BRAKER, Helixer, EASEL, and EnTAP, followed by assessment with BUSCO, OMARK, and structural metrics.