8 g:Profiler (web)
Functional enrichment analysis can be performed using various web-based tools, each of which is designed to meet specific analytical needs. These tools often vary in the databases they use, their statistical approaches, and their capabilities to perform different types of analysis, such as Over-Representation Analysis (ORA) or Gene Set Enrichment Analysis (GSEA).
Throughout the following chapters, each web-based tool is paired with its command-line (R) equivalent, so you can compare the two approaches side by side.
8.1 
gProfiler is known for its integration of numerous species and databases. It supports ORA, including an ordered/ranked-list mode, enabling users to assess Gene Ontology (GO), biological pathways, regulatory motifs and protein databases. With gProfiler one can
8.1.1 Steps to perform ORA in g:Profiler:
- Prepare Input List: Ensure your input is formatted as one gene per line or in a suitable format for g:Profiler.
- Input Gene List: Paste your prepared gene list directly into the input box on the g:Profiler web page or upload a file containing your list.
- Select Organism: Choose the appropriate organism from the Organism dropdown menu (e.g., Homo sapiens for human data).
- Choose Statistical Domain Scope: Under Advanced options, select your preferred statistical background from the Statistical domain scope menu. If you choose “Custom” background, provide your custom background list by pasting or uploading the relevant file.
- Set Significance Threshold: Select the desired significance threshold method, such as g:SCS, Bonferroni, or Benjamini-Hochberg. - Specify the threshold value (e.g., 0.05, 0.1, etc.).
- Select Functional Annotation Databases: Navigate to the Data sources tab and choose one or more databases for analysis. Available options include:
- Gene Ontology (GO): Biological Process, Molecular Function, and Cellular Component.
- KEGG Pathways
- Reactome Pathways
- WikiPathways
- TRANSFAC
- mirTarBase
- Human Protein Atlas
- CORUM
- Human Phenotype Ontology (HP)
- Run Query: Run the analysis and review the enriched terms, pathways, and visual outputs. Download the results as needed for further exploration.
8.1.1.1 Browse the gProfiler Results
Overview: The analysis provided a comprehensive list of enriched terms across selected databases, highlighting significant GO. The results give a high-level summary of pathways or terms most relevant to the input data.
Detailed Results: The detailed results section includes a tabulated format with enriched terms, adjusted p-values, and relevant statistics. Each entry provides information such as the enrichment score, associated genes, and functional annotations, allowing for an in-depth understanding of biological significance.
GO Context: The Gene Ontology (GO) context is divided into three main categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The analysis identifies which GO terms are significantly enriched, offering insights into the broader biological implications of the gene set. This helps in pinpointing processes such as cellular responses, metabolic pathways, and molecular interactions.
Query Info: This section includes specifics about the input data, including the total number of queried genes and any identifiers not recognised or mapped. It also details the statistical background used, the chosen organism, and other analysis settings, ensuring transparency and reproducibility of the results.
8.1.2 Steps to use a ranked gene list in g:Profiler:
g:Profiler’s “Ordered query” option lets you analyse a ranked gene list, but it does not implement the Subramanian et al. 2005 GSEA algorithm covered in the GSEA (web) chapter later in the book. Instead, it runs standard ORA (hypergeometric/Fisher’s exact test) on a series of prefixes of your ranked list (top 10, top 20, top 50 genes, and so on) and reports whichever prefix gives the strongest enrichment for each term. It does not use a running-sum enrichment score, does not weight genes by the magnitude of their ranking metric, and does not use permutation testing — so treat it as ORA over an incrementally-growing ranked list, not as an alternative implementation of GSEA.
- Prepare Your Pre-ranked List: Steps to provide a ranked gene list are given here.
- Input Gene List: Paste your prepared gene list directly into the input box on the g:Profiler web page or upload a file containing your list.
- Select Organism: Same as above.
- Select Ordered query: The “Ordered query” option in g:Profiler is designed to work with pre-ranked gene lists.
- Set Significance Threshold: Same as above.
- Provide a Custom GMT: This GMT file can be downloaded from MSigDB.
- Run Query: Same as above.
Challenge: Ranked-list ORA with gProfiler
Download the Hallmark gene sets (h.all.v2024.1.Hs.symbols.gmt) from MSigDB and use it as Custom GMT.