FusionGDB 20: Fusion Gene Annotation Updates Aided by Deep Learning

Authors

Pora Kim
Hua Tan
Jiajia Liu
Haeseung Lee
Hyesoo Jung
Himanshu Kumar
Xiaobo Zhou

Publication Date

1-7-2022

Journal

Nucleic Acids Research

Abstract

A knowledgebase of the systematic functional annotation of fusion genes is critical for understanding genomic breakage context and developing therapeutic strategies. FusionGDB is a unique functional annotation database of human fusion genes and has been widely used for studies with diverse aims. In this study, we report fusion gene annotation updates aided by deep learning (FusionGDB 2.0) available at https://compbio.uth.edu/FusionGDB2/. FusionGDB 2.0 has substantial updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with 44 human genomic features across five cellular role's categories, transcribed chimeric sequence and following open reading frame analysis with coding potential based on deep learning approach with Ribo-seq read features, and rigorous investigation of the protein feature retention of individual fusion partner genes in the protein level. Among ∼102k fusion genes, about 15k kept their ORF as In-frames, which is two times compared to the previous version, FusionGDB. FusionGDB 2.0 will be used as the reference knowledgebase of fusion gene annotations. FusionGDB 2.0 provides eight categories of annotations and it will be helpful for diverse human genomic studies.

Keywords

Amino Acid Sequence, Databases, Genetic, Deep Learning, Gene Fusion, Genome, Human, Genomics, Humans, Knowledge Bases, Molecular Sequence Annotation

DOI

10.1093/nar/gkab1056

PMID

34755868

PMCID

PMC8728198

PubMedCentral® Posted Date

11-10-2021

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes