1. Introduction to FGNet

FGNet allows to perform a Functional Enrichment Analysis (FEA) on a list of genes or expression set, and transform the results into networks. The resulting functional networks provide an overview of the biological functions of the genes/terms, and allows to easily see links between genes, overlap between clusters, finding key genes, etc.

FGNet takes as input a query list of genes selected by the user, and builds and displays networks of genes based in the existence of common functional terms that are enriched in certain subsets of genes of the list. By doing this, the tool allows to disclose groups/clusters of genes that have similar annotations and so they may have similar biological function in the cell. The discovery of molecular machines or functional modules within the cell (i.e. genes or proteins that work together to perform a biological process in the cells) is essential in modern molecular medicine and systems biology, because many times we do not know which are the gene partners playing in the same roles in a pathological state. FGNet is a tool that helps to create functional connections between different genes/proteins based on annotations. By grouping similar, redundant and homogeneous annotation content from the same or different biological resources into gene-term groups, the biological interpretation of large gene lists moves from a gene centric approach (where each gene is independent) to a functional-module centric approach (where the genes are interconnected). In this way, FGNet can provide a better representation of complex biological processes and reveal associations between genes.

Biological functional analysis

After obtaining a list of genes or proteins from an experiment or omic studies (microarrays, RNAseq, mass spectrometry, etc), the next step is usually to perform a functional analysis of the genes to search for the biological functions or processes in which they are involved. In order to facilitate the analysis of large lists of genes, multiple functional enrichment tools have been developed. These tools search for the genes in biological databases (i.e. GO, Kegg, Interpro), and test whether any biological annotations are over-represented in the query gene list compared to what would be expected in the whole population. However, the raw output from a functional enrichment analysis often provides dozens or hundreds of terms, and it still requires a lot of time and attention to go through the whole list of genes and annotations. A way to simplify this task is grouping genes and terms which often appear together and create associated networks: the Functional Networks.

FGNet builds the functional networks, based on data from a previous functional enrichment analysis (FEA). The package provides the functions to perform the FEA through four specific tools:

  • DAVID with Functional Annotation Clustering (DAVID-FAC), which measures relationships among annotation terms based on their co-association with subsets of genes within the query gene list (Huang et al). This type of clustering mostly results in groups of highly related terms, such as synonymous annotations from different annotation spaces (i.e. term “glycolysis” in KEGG and GO-BP), which also share most of their genes. This tool provides great coverage but does not avoid redundant terms and very general terms (like signal transduction or regulation of transcription that correspond to specific terms in Gene Ontology, GO).

  • GeneTerm Linker, a post-enrichment tool, which focuses on clearing and sorting the results from a previous modular enrichment analysis. This is achieved by filtering general terms with low information content (i.e. cellular process or protein binding) and redundant annotations (i.e. metabolic process and primary metabolic process). The remaining gene-term sets are grouped into metagroups based on their shared genes and terms (using a reciprocal linkage approach) (Fontanillo et al).

  • TopGO (Alexa et al), an enrichment analysis tool based on Gene Ontology (GO) that tests GO terms while accounting for the topology of the GO graph to eliminate local similarities and dependencies between GO terms. TopGO does not provide clusters, and therefore the functional network is built using only the gene-term sets. TopGO can be applied off-line.

  • GAGE (Luo et al), a gene set enrichment analysis (GSEA) tool. It searches for functional enrichment in gene sets (i.e. KEGG pathways, Reactome, GO) and allows including a signal value -like expression changes- to rank the genes and then to identify the enrichment in functional terms that are altered (i.e. changed in genes UP and DOWN) or altered consistently in one direction (UP or DOWN). GAGE also clusters the resulting enriched gene-term sets and can be applied off-line.

To build the network based on other other tools, the raw output should be saved into a text file which contains the enriched terms and their genes. (For more details see function format_results()).

Functional network

The functional network is the representation of the results from a functional enrichment analysis.

In the default network, all the nodes of the network are of the same type, i.e. genes OR terms, which are linked to each other if they are in the same gene-term set. In the plot, the genes/terms in the same groups (metagroups or clusters) are surrounded by a common background color.

In the bipartite network, the nodes are of two types, allowing to link the genes or terms, with the clusters they belong to. This network, can be built as an intersection network , a simplified functional network where all the genes/terms that belong to only one metagroup are clustered into a single node. This simplified network contains only the nodes in several groups.

In addition to the networks, FGNet also provides a few functions for further analysis. These functions allow to get a distance matrix , which represents the similarity between the groups based on the genes they share with each other (binary distance), and the distribution of degree and betweenness within the network and subnetworks, in order to find the most important genes (hubs).

All these functionalities can be accessed directly through the appropriate functions or the graphical user interface (GUI). In addition, FGNet also allows to generate an HTML report with an overview of these plots and analyses for a specific gene list.

Examples of functional network for different analyses: