Biological pathway and network analysis (gene ontology, KEGG, Reactome)
The computational analysis of biological molecules (genes, proteins) in the context of predefined functional groupings (Gene Ontology) and interconnected metabolic/signaling pathways (KEGG, Reactome) to interpret high-throughput omics data.
This skill transforms raw, high-dimensional genomic or proteomic data into biologically interpretable insights, directly accelerating target discovery, biomarker identification, and mechanistic understanding of disease or drug action. It bridges the gap between raw data generation and actionable biological hypotheses, de-risking R&D investment and informing clinical trial design.
1Careers
1Categories
9.2 Avg Demand
15%
Avg AI Risk
How to Learn Biological pathway and network analysis (gene ontology, KEGG, Reactome)
Focus on: 1) Core terminology: pathways, enrichment analysis, gene sets, p-value, FDR. 2) Understanding the structure of GO (Biological Process, Molecular Function, Cellular Component) and KEGG (BRITE hierarchy, pathway maps). 3) Basic proficiency in running a pre-built tool like DAVID or Enrichr for a simple gene list.
Move to scripting-based analysis (R/Python) using libraries like clusterProfiler (R) or GSEApy (Python) for reproducibility. Analyze RNA-seq differential expression results, not just gene lists. Common mistakes: Ignoring background gene list specification, misinterpreting redundant GO terms, confusing over-representation analysis (ORA) with Gene Set Enrichment Analysis (GSEA).
Master multi-omics data integration (e.g., proteomics + transcriptomics) in pathway context. Learn to perform network analysis (e.g., protein-protein interaction networks using STRING, Cytoscape) to identify key hub genes or modules. Critically evaluate and select the most appropriate database and algorithm for a given biological question (e.g., Reactome for detailed reaction-level events vs. KEGG for broader metabolic maps).
Practice Projects
Beginner
Project
GO & KEGG Enrichment on a Pre-Packaged Gene List
Scenario
You are given a list of 200 differentially expressed genes (DEGs) from a public breast cancer dataset (e.g., TCGA). The task is to identify the primary biological themes.
How to Execute
1. Obtain a curated DEG list from a source like GEO or TCGA. 2. Submit the gene list (background: all human genes) to DAVID or Enrichr online tools. 3. Select GO Biological Process and KEGG Pathway categories. 4. Export results, filtering for terms with adjusted p-value < 0.05, and manually group redundant terms into themes (e.g., 'cell cycle', 'immune response').
Intermediate
Project
Script-Based GSEA on Transcriptomic Data
Scenario
Perform Gene Set Enrichment Analysis (GSEA) on a ranked list of genes from an RNA-seq experiment comparing treated vs. control samples, using hallmark gene sets (MSigDB).
How to Execute
1. In R, use DESeq2 or edgeR to generate a ranked list of genes by fold-change or statistic. 2. Using clusterProfiler, run `gseGO()` and `gseKEGG()` functions with the ranked list. 3. Visualize the running enrichment score for a top pathway (e.g., 'HALLMARK_OXIDATIVE_PHOSPHORYLATION') using `gseaplot2()`. 4. Interpret the NES (Normalized Enrichment Score) and FDR to report if the gene set is significantly enriched at the top or bottom of the ranked list.
A pharmaceutical team has phosphoproteomics and transcriptomics data from a drug-treated cancer cell line. Goal: Identify the primary mechanism of action by integrating kinase activity predictions with transcriptional pathway responses.
How to Execute
1. Use kinase-substrate databases (e.g., PhosphoSitePlus) with tools like KSEA to infer kinase activity from phosphoproteomics. 2. Perform pathway analysis on the transcriptomic DEGs. 3. In Cytoscape, build an integrated network linking inferred upstream kinases (from step 1) to enriched downstream pathways (from step 2) using known interactions (e.g., from STRING). 4. Prioritize a key signaling node (e.g., mTOR) based on network centrality scores (degree, betweenness) and validation from both data types.
clusterProfiler is the industry standard for ORA and GSEA in R. GSEApy is its Python equivalent. Cytoscape is used for biological network visualization and topological analysis. STRING provides protein-protein interaction data for network building. DAVID/Enrichr are essential for quick, exploratory analysis.
GO provides controlled vocabulary for gene function. KEGG offers manually drawn pathway maps for metabolism, signaling, and disease. Reactome provides a curated, peer-reviewed pathway database with detailed reaction-level information. MSigDB is a comprehensive collection of gene sets for GSEA.
Interview Questions
Answer Strategy
The question tests methodological rigor and communication skills. Strategy: Emphasize the distinction between a gene list (ORA) vs. ranked data (GSEA). Mention setting a proper background gene list. Recommend a reproducible script (clusterProfiler) over a web tool. For presentation, highlight the top 5-10 non-redundant, biologically interpretable pathways with their FDR values, and suggest linking hits to known drug targets in those pathways.
Answer Strategy
This tests critical thinking and database knowledge. The answer should show understanding of database ontology differences. Strategy: Explain that GO annotates genes to broad, process-oriented terms, while KEGG requires genes to be part of a specific, connected molecular map. Investigate by: 1) Checking if the genes in the GO term are actually part of a KEGG pathway. 2) Using Reactome, which may have a different structure for the same process. 3) Visually inspecting the KEGG pathway map for the immune system to see if the hits are scattered across multiple maps without reaching a single map's enrichment threshold.
Careers That Require Biological pathway and network analysis (gene ontology, KEGG, Reactome)