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DIAMOND2GO: rapid Gene Ontology assignment and enrichment detection for functional genomics.

作者信息

Golden Christopher, Studholme David J, Farrer Rhys A

机构信息

Medical Research Council Centre for Medical Mycology at the University of Exeter, Department of Biosciences, Faculty of Health and Life Sciences, Exeter, United Kingdom.

Biosciences, University of Exeter, Exeter, United Kingdom.

出版信息

Front Bioinform. 2025 Aug 15;5:1634042. doi: 10.3389/fbinf.2025.1634042. eCollection 2025.


DOI:10.3389/fbinf.2025.1634042
PMID:40894377
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12394471/
Abstract

DIAMOND2GO (D2GO) is a high-speed toolset for assigning Gene Ontology (GO) terms to genes or proteins based on sequence similarity. Leveraging the ultra-fast alignment capabilities of DIAMOND, which is 100 to 20,000 times faster than BLAST, D2GO enables rapid functional annotation of large-scale datasets. D2GO maps GO terms from pre-annotated sequences in the NCBI non-redundant database to query sequences. During benchmarking, D2GO assigned over 2 million GO terms to 98% of 130,184 predicted human protein isoforms in under 13 min on a standard laptop. In addition to annotation, D2GO includes an enrichment analysis tool that allows users to identify significantly overrepresented GO terms between subsets of sequences. We compared D2GO against two widely used tools, Blast2GO and eggNOG-mapper, and observed substantial differences in the number and type of annotations produced. These discrepancies reflect varying sensitivities and specificities across tools and suggest that using multiple methods in tandem may improve overall annotation coverage. D2GO is open-source and freely available under the MIT license at https://github.com/rhysf/DIAMOND2GO.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/300c/12394471/cce0a9223fec/fbinf-05-1634042-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/300c/12394471/f644f525eadc/fbinf-05-1634042-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/300c/12394471/cce0a9223fec/fbinf-05-1634042-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/300c/12394471/f644f525eadc/fbinf-05-1634042-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/300c/12394471/cce0a9223fec/fbinf-05-1634042-g002.jpg

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本文引用的文献

[1]
GOReverseLookup: A gene ontology reverse lookup tool.

Comput Biol Med. 2025-6

[2]
The Gene Ontology knowledgebase in 2023.

Genetics. 2023-5-4

[3]
Two-speed genome evolution drives pathogenicity in fungal pathogens of animals.

Proc Natl Acad Sci U S A. 2023-1-10

[4]
Wei2GO: weighted sequence similarity-based protein function prediction.

PeerJ. 2022

[5]
PANTHER: Making genome-scale phylogenetics accessible to all.

Protein Sci. 2022-1

[6]
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

Genome Biol. 2019-11-19

[7]
Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems.

Nat Genet. 2019-10

[8]
Next-generation genome annotation: we still struggle to get it right.

Genome Biol. 2019-5-16

[9]
Predicting human protein function with multi-task deep neural networks.

PLoS One. 2018-6-11

[10]
GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank.

Bioinformatics. 2018-7-15

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