ArticleBMC bioinformatics2016
GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
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- Differential methylation of linoleic acid pathway genes is associated with PTSD symptoms - a longitudinal study with Burundian soldiers returning from a war zone.Translational psychiatry · 2024Article
- The hypoxia-regulated ectonucleotidase CD73 is a host determinant of HIV latency.Cell reports · 2023Article
- Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases.Nature communications · 2023Article
- BMAL1 modulates senescence programming via AP-1.Aging · 2023Article
- A multi-omic analysis reveals the esophageal dysbiosis as the predominant trait of eosinophilic esophagitis.Journal of translational medicine · 2023Article
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7 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-throughput technologies such as ChIP-sequencing, RNA-sequencing, DNA sequencing and quantitative metabolomics generate a huge volume of data. Researchers often rely on functional enrichment tools to interpret the biological significance of the affected genes from these high-throughput studies. However, currently available functional enrichment tools need to be updated frequently to adapt to new entries from the functional database repositories. Hence there is a need for a simplified tool that can perform functional enrichment analysis by using updated information directly from the source databases such as KEGG, Reactome or Gene Ontology etc.
resultsIn this study, we focused on designing a command-line tool called GeneSCF (Gene Set Clustering based on Functional annotations), that can predict the functionally relevant biological information for a set of genes in a real-time updated manner. It is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. We successfully employed our tool on two of published datasets to predict the biologically relevant functional information. The core features of this tool were tested on Linux machines without the need for installation of more dependencies.
conclusionsGeneSCF is more reliable compared to other enrichment tools because of its ability to use reference functional databases in real-time to perform enrichment analysis. It is an easy-to-integrate tool with other pipelines available for downstream analysis of high-throughput data. More importantly, GeneSCF can run multiple gene lists simultaneously on different organisms thereby saving time for the users. Since the tool is designed to be ready-to-use, there is no need for any complex compilation and installation procedures.
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