GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.
BACKGROUND
High-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.


RESULTS
In 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.


CONCLUSIONS
GeneSCF 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.
Department of Medical Genetics, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-40530, Sweden.
Santhilal
S
Subhash
Department of Medical Genetics, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, SE-40530, Sweden. kanduri.chandrasekhar@gu.se.
Chandrasekhar
C
Kanduri
D016208:Databases, Factual / Q000592:standards* / Q000706:statistics & numerical data; D063990:Gene Ontology; D006801:Humans; D055432:Metabolomics / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
Cancer enrichment; Functional enrichments; Gene Ontology; Gene enrichment tool; KEGG; Pathway enrichments; Real-time analysis