GeneSCF: a real-time based functional enrichment tool with support for multiple organisms.
BMC Bioinformatics
; 17(1): 365, 2016 Sep 13.
Article
en En
| MEDLINE
| ID: mdl-27618934
ABSTRACT
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.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Bases de Datos Factuales
/
Análisis de Secuencia de ARN
/
Análisis de Secuencia de ADN
/
Metabolómica
/
Ontología de Genes
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2016
Tipo del documento:
Article
País de afiliación:
Suecia