SAFPred: synteny-aware gene function prediction for bacteria using protein embeddings Article Swipe
Aysun Urhan
,
Bianca-Maria Cosma
,
Ashlee M. Earl
,
Abigail L. Manson
,
Thomas Abeel
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae328
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae328
Motivation Today, we know the function of only a small fraction of the protein sequences predicted from genomic data. This problem is even more salient for bacteria, which represent some of the most phylogenetically and metabolically diverse taxa on Earth. This low rate of bacterial gene annotation is compounded by the fact that most function prediction algorithms have focused on eukaryotes, and conventional annotation approaches rely on the presence of similar sequences in existing databases. However, often there are no such sequences for novel bacterial proteins. Thus, we need improved gene function prediction methods tailored for bacteria. Recently, transformer-based language models—adopted from the natural language processing field—have been used to obtain new representations of proteins, to replace amino acid sequences. These representations, referred to as protein embeddings, have shown promise for improving annotation of eukaryotes, but there have been only limited applications on bacterial genomes. Results To predict gene functions in bacteria, we developed SAFPred, a novel synteny-aware gene function prediction tool based on protein embeddings from state-of-the-art protein language models. SAFpred also leverages the unique operon structure of bacteria through conserved synteny. SAFPred outperformed both conventional sequence-based annotation methods and state-of-the-art methods on multiple bacterial species, including for distant homolog detection, where the sequence similarity to the proteins in the training set was as low as 40%. Using SAFPred to identify gene functions across diverse enterococci, of which some species are major clinical threats, we identified 11 previously unrecognized putative novel toxins, with potential significance to human and animal health. Availability and implementation https://github.com/AbeelLab/safpred.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btae328
- https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae328/57826417/btae328.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4398200498
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4398200498Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/bioinformatics/btae328Digital Object Identifier
- Title
-
SAFPred: synteny-aware gene function prediction for bacteria using protein embeddingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-05-22Full publication date if available
- Authors
-
Aysun Urhan, Bianca-Maria Cosma, Ashlee M. Earl, Abigail L. Manson, Thomas AbeelList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btae328Publisher landing page
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae328/57826417/btae328.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae328/57826417/btae328.pdfDirect OA link when available
- Concepts
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Synteny, Computer science, Computational biology, Function (biology), Gene, Artificial intelligence, Genetics, Biology, GenomeTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.transformer-based | 99 |
| abstract_inverted_index.https://github.com/AbeelLab/safpred. | 255 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.78846643 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |