ArticleNucleic acids research2023
Learning from the unknown: exploring the range of bacterial functionality.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Whole metagenome sequencing: not deep enough for complete microbial function recovery.Microbiome · 2026Article
- 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026Article
- Beyond sequence similarity: toward function-based screening of nucleic acid synthesis.Frontiers in bioengineering and biotechnology · 2026Article
- Cutting-edge deep-learning based tools for metagenomic research.National science review · 2025Review
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- Polyphasic taxonomy and genome mining ofCurrent research in microbial sciences · 2025Article
- Assembling bacterial puzzles: piecing together functions into microbial pathways.NAR genomics and bioinformatics · 2024Article
- SAFPred: synteny-aware gene function prediction for bacteria using protein embeddings.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Determining the repertoire of a microbe's molecular functions is a central question in microbial biology. Modern techniques achieve this goal by comparing microbial genetic material against reference databases of functionally annotated genes/proteins or known taxonomic markers such as 16S rRNA. Here, we describe a novel approach to exploring bacterial functional repertoires without reference databases. Our Fusion scheme establishes functional relationships between bacteria and assigns organisms to Fusion-taxa that differ from otherwise defined taxonomic clades. Three key findings of our work stand out. First, bacterial functional comparisons outperform marker genes in assigning taxonomic clades. Fusion profiles are also better for this task than other functional annotation schemes. Second, Fusion-taxa are robust to addition of novel organisms and are, arguably, able to capture the environment-driven bacterial diversity. Finally, our alignment-free nucleic acid-based Siamese Neural Network model, created using Fusion functions, enables finding shared functionality of very distant, possibly structurally different, microbial homologs. Our work can thus help annotate functional repertoires of bacterial organisms and further guide our understanding of microbial communities.
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Registered trials
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.