Back

AEGIS: an annotation extraction and genomic integration resource

Navarro-Paya, D.; Santiago, A.; Velt, A.; Moretto, M.; Rustenholz, C.; Matus, J. T.

2025-12-08 bioinformatics
10.64898/2025.12.04.692274 bioRxiv
Show abstract

MotivationGenome annotation files (GFF3/GTF) are the standard for storing genomic feature data, yet their flexibility often results in formatting inconsistencies that create bottlenecks for downstream bioinformatics analyses. A robust, unified framework is required to parse, standardise, and validate these files to ensure interoperability and facilitate complex comparative genomic tasks. ResultsWe present AEGIS (Annotation Extraction and Genomic Integration Suite), a comprehensive toolkit designed to parse, correct, and standardise genome annotations. Beyond quality control, AEGIS provides advanced modules for flexible feature extraction (e.g. coding sequences, promoters) and comparative genomic analysis. Uniquely, it integrates multiple lines of evidence, including sequence homology, synteny, and coordinate-based lift-overs, to assess gene model correspondence and infer orthology. We demonstrate the utility of AEGIS by quantifying complex structural changes between Arabidopsis annotation versions and identifying high-confidence orthologues across diverse plant genomes. Availability and ImplementationAEGIS is implemented in Python. Source code and documentation are freely available under the GPL-3 license at {{https://github.com/Tomsbiolab/aegis}} and as a Docker container at {{https://hub.docker.com/r/tomsbiolab/aegis}}. The package is also available on PyPI (pip install aegis-bio).

Published in Bioinformatics (predicted rank #1) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.