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 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.

Authors

Institutions

Publication Details

Journal
Algorithms for Molecular Biology
Published
2025-09-29
DOI
https://doi.org/10.1186/s13015-025-00290-w
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exclusive functional signatures for gene annotation with vast OpenOrd layout

Vsevolod J. Makeev, Gleb S. Buzanov
Algorithms for Molecular Biology
Bioinformatics and Genomic Networks
article

Exclusive functional signatures for gene annotation with vast OpenOrd layout

Vsevolod J. Makeev, Gleb S. Buzanov
article en

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.

Algorithms for Molecular BiologyVol. 20(1)
Moscow Institute of Physics and Technology (RU), Cancer Research UK Manchester Institute (GB), Vavilov Institute of General Genetics (RU), National Computing Centre (United Kingdom) (GB)
Russian Academy of Sciences
Openalex Percentile: Top 76%
Bioinformatics and Genomic Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.