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Gene Set Clustering based on Functional annotation GeneSCF

Online

Predicts the functionally relevant biological information for a set of genes in a real-time updated manner. GeneSCF is designed to handle information from more than 4000 organisms from freely available prominent functional databases like KEGG, Reactome and Gene Ontology. 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.

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GeneSCF versioning

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GeneSCF classification

  • Animals
    • Homo sapiens

GeneSCF specifications

Software type:
Package/Module
Restrictions to use:
Academic or non-commercial use
Output data:
The final output from GeneSCF is a table (text/tab-separated file, TSV) with list of ranked functions based on the number of hits from the user provided gene list. It also contains column with probability value (P-value) obtained by Fisher’s exact test using the contingency table and also columns containing false discovery rate (FDR) values using different multiple hypothesis correction methods.
Programming languages:
Perl, Shell (Bash)
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes
Interface:
Command line interface
Input data:
The standard input for GeneSCF is plain text document (text/plain) containing a list of genes in the form of official gene symbols.
Operating system:
Unix/Linux
License:
GNU General Public License version 3.0
Version:
1.1
Requirements:
R, ggplot
Source code URL:
https://github.com/santhilalsubhash/geneSCF

GeneSCF support

Documentation

Maintainer

  • Chandrasekhar Kanduri <>

Credits

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Publications

Institution(s)

Department of Medical Genetics, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

Funding source(s)

This work was supported by the grants from the Knut and Alice Wallenberg Foundation (KAW) (Dnr KAW 2014.0057), Swedish Foundation for Strategic Research (RB13-0204), Swedish Cancer Research foundation (Cancerfonden: Kontrakt no. 150796), the Swedish Research Council (VR-M: K2014-67X-20781-07-4), Barncancerfonden (PR2014/0147), Ingabritt Och Arne Lundbergs forskningsstiftelse and LUA/ALF.

Link to literature

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