Efficient evidence-based genome annotation with EviAnn.

ABSTRACT: For many years, machine learning-based ab initio gene finding approaches have been central components of eukaryotic genome annotation pipelines, and they remain so today. The reliance on these approaches was originally sustained by the high cost and low availability of gene expression data, a primary source of evidence for gene annotation along with protein homology. However, innovations in modern sequencing technologies have revolutionized the acquisition of gene expression data, allowing scientists to rely more heavily on this class of evidence. In addition, proteins found in a multitude of well-annotated genomes represent another invaluable resource for gene annotation. Existing annotation packages often underutilize these data sources, which prompted us to develop EviAnn ( Evi d

SUBMITTER: Zimin AV 

PROVIDER: S-EPMC12132231 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature