FusionGDB


34755868	FusionGDB 2.0: fusion gene annotation updates aided by deep learning. [PMID: 34755868] Kim P, Tan H, Liu J, Lee H, Jung H, Kumar H, Zhou X. 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. Nucleic Acids Res. 2022:50(D1) \| 18 Citations (from Europe PMC, 2025-03-15)
30407583	FusionGDB: fusion gene annotation DataBase. [PMID: 30407583] Kim P, Zhou X. Abstract Gene fusion is one of the hallmarks of cancer genome via chromosomal rearrangement initiated by DNA double-strand breakage. To date, many fusion genes (FGs) have been established as important biomarkers and therapeutic targets in multiple cancer types. To better understand the function of FGs in cancer types and to promote the discovery of clinically relevant FGs, we built FusionGDB (Fusion Gene annotation DataBase) available at https://ccsm.uth.edu/FusionGDB. We collected 48 117 FGs across pan-cancer from three representative fusion gene resources: the improved database of chimeric transcripts and RNA-seq data (ChiTaRS 3.1), an integrative resource for cancer-associated transcript fusions (TumorFusions), and The Cancer Genome Atlas (TCGA) fusions by Gao et al. For these ?48K FGs, we performed functional annotations including gene assessment across pan-cancer fusion genes, open reading frame (ORF) assignment, and retention search of 39 protein features based on gene structures of multiple isoforms with different breakpoints. We also provided the fusion transcript and amino acid sequences according to multiple breakpoints and transcript isoforms. Our analyses identified 331, 303 and 667 in-frame FGs with retaining kinase, DNA-binding, and epigenetic factor domains, respectively, as well as 976 FGs lost protein-protein interaction. FusionGDB provides six categories of annotations: FusionGeneSummary, FusionProtFeature, FusionGeneSequence, FusionGenePPI, RelatedDrug and RelatedDisease. Nucleic Acids Res. 2019:47(D1) \| 50 Citations (from Europe PMC, 2025-03-15)

FusionGDB 2.0: fusion gene annotation updates aided by deep learning. [PMID: 34755868]

Kim P, Tan H, Liu J, Lee H, Jung H, Kumar H, Zhou X.

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.

Nucleic Acids Res. 2022:50(D1) | 18 Citations (from Europe PMC, 2025-03-15)

FusionGDB: fusion gene annotation DataBase. [PMID: 30407583]

Kim P, Zhou X.

Abstract

Gene fusion is one of the hallmarks of cancer genome via chromosomal rearrangement initiated by DNA double-strand breakage. To date, many fusion genes (FGs) have been established as important biomarkers and therapeutic targets in multiple cancer types. To better understand the function of FGs in cancer types and to promote the discovery of clinically relevant FGs, we built FusionGDB (Fusion Gene annotation DataBase) available at https://ccsm.uth.edu/FusionGDB. We collected 48 117 FGs across pan-cancer from three representative fusion gene resources: the improved database of chimeric transcripts and RNA-seq data (ChiTaRS 3.1), an integrative resource for cancer-associated transcript fusions (TumorFusions), and The Cancer Genome Atlas (TCGA) fusions by Gao et al. For these ?48K FGs, we performed functional annotations including gene assessment across pan-cancer fusion genes, open reading frame (ORF) assignment, and retention search of 39 protein features based on gene structures of multiple isoforms with different breakpoints. We also provided the fusion transcript and amino acid sequences according to multiple breakpoints and transcript isoforms. Our analyses identified 331, 303 and 667 in-frame FGs with retaining kinase, DNA-binding, and epigenetic factor domains, respectively, as well as 976 FGs lost protein-protein interaction. FusionGDB provides six categories of annotations: FusionGeneSummary, FusionProtFeature, FusionGeneSequence, FusionGenePPI, RelatedDrug and RelatedDisease.

Nucleic Acids Res. 2019:47(D1) | 50 Citations (from Europe PMC, 2025-03-15)

URL:	https://compbio.uth.edu/FusionGDB2
Full name:	Fusion Gene annotation DataBase
Description:	FusionGDB is the Fusion Gene annotation DataBase, aiming to provide a resource or reference for functional annotation of fusion genes in cancer for better therapeutic targets. The second version has a intensive functional annotation on ~ 110K human fusion genes with enhanced contents.
Year founded:	2019
Last update:	2021
Version:	v2.0
Accessibility:	Accessible
Country/Region:	United States

Data type:	DNA Protein
Data object:	Animal
Database category:	Gene genome and annotation Genotype phenotype and variation
Major species:	Homo sapiens
Keywords:	Pan-cancer fusion gene

University/Institution:	University of Texas Health Science Center at Houston
Address:	Center for Computational Systems Medicine School of Biomedical Informatics The University of Texas Health Science Center at Houston 7000 Fannin Street, Houston, TX 77030
City:	Houston
Province/State:
Country/Region:	United States
Contact name (PI/Team):	Pora Kim
Contact email (PI/Helpdesk):	Pora.Kim@uth.tmc.edu

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Database Commons a catalog of worldwide biological databases