Background

Interpretation of biological data often deals with lists of genes obtained from high-throughput studies. Although plenty of available packages have been developed for gene-centric functional enrichment analysis, most are specifically designed for **model organisms**, leaving **protists** unnoticed. The validity of functional enrichment analysis relies heavily on up-to-date gene functional annotations. However, existing annotation packages for **protists** are outdated and apt to impact enrichment results negatively.

What we have done?

We have established **ProFun**, **a web server for functional enrichment analysis of parasitic protozoan genes**. Gene functional enrichment analysis becomes as simple as pasting a list of gene IDs into the textbox of our website. The intuitive web interface facilitates users to manipulate enrichment results easily, reveal meaningful biological events, and create publication-ready charts.

ProFun utilizes the Docker container, ShinyProxy, and R Shiny to construct a scalable web service with load-balancing infrastructure. We have integrated a series of visual analytic functions, in-house scripts, and our custom-made annotation packages to create **three major analytic modules:**

  1. Gene Overlaps

  2. Over-representation Analysis (ORA)

  3. Gene Set Enrichment Analysis (GSEA)

    ProFun is the first web application that enables gene functional enrichment analysis of protists. In addition to supporting 34 protists, ProFun also allows the comparison of functional enrichment results across complicated experimental designs.

The Framework of ProFun

Figure 1. The Framework of ProFun. ProFun accepts two types of input data, 1. Gene list(s) 2. Two-column CSV format data. The first column is Gene IDs and the second column is gene regulation value (log2 Fold Change). A scalable web application with load-balancing function was built by ShinyProxy and Docker container technology. ProFun includes three analytic modules: (1) Gene Overlaps; (2) Over-representation Analysis (ORA); (3) Gene Set Enrichment Analysis (GSEA). ProFun provides an intuitive web-based interface to perform gene functional analysis for 34 protists, facilitating users to reveal functional consensus or differences between different experimental conditions efficiently.

Figure 1. The Framework of ProFun. ProFun accepts two types of input data, 1. Gene list(s) 2. Two-column CSV format data. The first column is Gene IDs and the second column is gene regulation value (log2 Fold Change). A scalable web application with load-balancing function was built by ShinyProxy and Docker container technology. ProFun includes three analytic modules: (1) Gene Overlaps; (2) Over-representation Analysis (ORA); (3) Gene Set Enrichment Analysis (GSEA). ProFun provides an intuitive web-based interface to perform gene functional analysis for 34 protists, facilitating users to reveal functional consensus or differences between different experimental conditions efficiently.