Exclusive functional signatures for gene annotation with vast OpenOrd layout

A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a graph-based algorithm that partitions all genes into non-overlapping classes of functionally related genes, thus assigning a single function to ea...

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Vydáno v:Algorithms for molecular biology Ročník 20; číslo 1; s. 17 - 15
Hlavní autoři: Buzanov, Gleb, Makeev, Vsevolod
Médium: Journal Article
Jazyk:angličtina
Vydáno: England BioMed Central Ltd 29.09.2025
Springer Nature B.V
BMC
Témata:
Algorithms
Annotations
Anopheles
Biological activity
Enrichr database
Functional annotation
Gene clustering
Gene functions
Gene set enrichment analysis
Genes
Graph representations
Graph-based algorithm
OpenOrd
Signatures
Gene functions
Gene clustering
Graph-based algorithm
Functional annotation
OpenOrd
Bioinformatics
Enrichr database
ISSN:1748-7188, 1748-7188
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Abstract A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a graph-based algorithm that partitions all genes into non-overlapping classes of functionally related genes, thus assigning a single function to each gene. To this end, many functional signatures are combined into a single weighted graph, which is partitioned into cliques. For a poorly annotated marker gene, our approach fetches a number of genes that belong to the same class, some of which can be well annotated and are likely to take part in the same biological process.
AbstractList A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a graph-based algorithm that partitions all genes into non-overlapping classes of functionally related genes, thus assigning a single function to each gene. To this end, many functional signatures are combined into a single weighted graph, which is partitioned into cliques. For a poorly annotated marker gene, our approach fetches a number of genes that belong to the same class, some of which can be well annotated and are likely to take part in the same biological process. Keywords: Gene clustering, Graph-based algorithm, Functional annotation, Enrichr database, OpenOrd, Gene functions, Bioinformatics
Abstract A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a graph-based algorithm that partitions all genes into non-overlapping classes of functionally related genes, thus assigning a single function to each gene. To this end, many functional signatures are combined into a single weighted graph, which is partitioned into cliques. For a poorly annotated marker gene, our approach fetches a number of genes that belong to the same class, some of which can be well annotated and are likely to take part in the same biological process.
A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a graph-based algorithm that partitions all genes into non-overlapping classes of functionally related genes, thus assigning a single function to each gene. To this end, many functional signatures are combined into a single weighted graph, which is partitioned into cliques. For a poorly annotated marker gene, our approach fetches a number of genes that belong to the same class, some of which can be well annotated and are likely to take part in the same biological process.
A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a graph-based algorithm that partitions all genes into non-overlapping classes of functionally related genes, thus assigning a single function to each gene. To this end, many functional signatures are combined into a single weighted graph, which is partitioned into cliques. For a poorly annotated marker gene, our approach fetches a number of genes that belong to the same class, some of which can be well annotated and are likely to take part in the same biological process.A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a graph-based algorithm that partitions all genes into non-overlapping classes of functionally related genes, thus assigning a single function to each gene. To this end, many functional signatures are combined into a single weighted graph, which is partitioned into cliques. For a poorly annotated marker gene, our approach fetches a number of genes that belong to the same class, some of which can be well annotated and are likely to take part in the same biological process.
ArticleNumber 17
Audience Academic
Author Makeev, Vsevolod
Buzanov, Gleb
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Keywords Gene functions
Gene clustering
Graph-based algorithm
Functional annotation
OpenOrd
Bioinformatics
Enrichr database
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Snippet A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest VOL-Gene, a...
Abstract A biological study can produce a limited number of marker genes, not large enough to be used in gene set enrichment analysis. Here we suggest...
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SubjectTerms Algorithms
Annotations
Anopheles
Biological activity
Enrichr database
Functional annotation
Gene clustering
Gene functions
Gene set enrichment analysis
Genes
Graph representations
Graph-based algorithm
OpenOrd
Signatures
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Title Exclusive functional signatures for gene annotation with vast OpenOrd layout
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