Efficient evidence-based genome annotation with EviAnn.
Aleksey V Zimin1,2, Daniela Puiu3,4, Mihaela Pertea3,4
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. alekseyz@jhu.edu.
Nature Methods
|July 3, 2026
Summary
EviAnn, an evidence-based gene annotation system, leverages abundant transcript and protein data for accurate eukaryotic genome annotation. It outperforms existing tools, offering faster and more efficient gene finding.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Traditional ab initio gene finders were limited by scarce gene expression data.
- Modern sequencing provides abundant transcript and protein evidence for genome annotation.
- Existing tools underutilize this wealth of evidence.
Purpose of the Study:
- To develop EviAnn (Evidence-based Annotator), a novel eukaryotic gene annotation system.
- To create a data-driven approach that prioritizes transcript and protein evidence over ab initio predictions.
- To improve the efficiency and accuracy of eukaryotic genome annotation.
Main Methods:
- EviAnn builds exon-intron structures directly from transcript alignments and protein homology.
- It employs a data-driven strategy, contrasting with traditional ab initio methods.
- The system was evaluated against leading annotation packages like BRAKER3, MAKER2, and FINDER.
Main Results:
- EviAnn consistently outperforms BRAKER3, MAKER2, and FINDER in gene annotation accuracy.
- The system utilizes significantly less computational time compared to existing tools.
- A mammalian genome can be annotated in under an hour on a single multicore server.
Conclusions:
- EviAnn represents a significant advancement in evidence-based eukaryotic gene annotation.
- The system efficiently utilizes abundant transcriptomic and proteomic data for improved accuracy.
- EviAnn offers a faster, more resource-efficient alternative for genome annotation tasks.
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