Evidence map›Paper›PMID 34755868›Full record

ArticleNucleic acids research2022

FusionGDB 2.0: fusion gene annotation updates aided by deep learning.

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

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Pora KimSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0002-8321-6864
Hua TanSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0003-4090-113X
Jiajia LiuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Haeseung LeeIntellectual Information Team, Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea.
Hyesoo JungDepartment of Neurology, Asan Medical Center, Seoul, Korea.
Himanshu KumarSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Xiaobo ZhouSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0001-7191-6495
The University of Texas Health Science Center at Houston · USAsan Medical Center · KRKorea Institute of Oriental Medicine · KR

Funding

Functional annotation of new genes aided by deep learningR35GM138184 · NIGMS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KIM, PORA · 2020 to 2024
$1.7M
NIGMS NIH HHS R35 GM138184
6 · The paper itself

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.

Indexed as

Databases, GeneticGenomicsAmino Acid SequenceDeep LearningGene FusionGenome, HumanHumansKnowledge BasesMolecular Sequence Annotation

Identifiers

PMID34755868
PMCPMC8728198
OpenAlexW3214538404

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.