# KEGG vs. Reactome for Proteomics Pathway Analysis: Which Database Should You Use?

Proteomics experiments generate lists of differentially expressed proteins that require biological interpretation. KEGG and Reactome are the two pathway databases most frequently used for enrichment analysis, but they differ in curation philosophy, annotation depth, and statistical behavior. Researchers analyzing mass spectrometry data often find that the same protein list produces different enrichment results depending on which database they query. This is not an error. KEGG and Reactome are built on different principles. KEGG focuses on manually curated pathway maps that integrate genomic, chemical, and systemic information. Reactome organizes human biological processes into a hierarchical, reaction-based format with detailed molecular annotations. Understanding these structural differences is essential for interpreting your enrichment output correctly.

The practical outcome of this article is a decision framework. You will learn how to match your research question to the appropriate database, how to combine both databases for robust results, and how to avoid common interpretation errors that arise from database-specific biases.

## At a Glance

The table below summarizes the key differences between KEGG and Reactome for proteomics pathway analysis. Use this as a quick reference when planning your analysis workflow.

| Feature | KEGG | Reactome |
|---------|------|----------|
| Curation model | Manually curated pathway maps with cross-species integration | Expert-curated, reaction-based hierarchical pathways |
| Species coverage | Broad, including many prokaryotes and eukaryotes | Primarily human, with orthology-based projections to other species |
| Pathway granularity | Pathway maps with discrete nodes for genes, proteins, and compounds | Detailed reaction steps with molecular entities and subcellular localization |
| Statistical tools | KEGG enrichment via KOBAS, clusterProfiler, or DAVID | ReactomePA, clusterProfiler, or g:Profiler |
| Best suited for | Metabolic pathways, signaling cascades, cross-species comparisons | Detailed mechanistic understanding, reaction-level annotation, human biology |
| Common limitation | Pathway maps can be outdated, limited reaction detail | Human-centric bias, less useful for non-model organisms |
| Typical use case | Initial screening of metabolic and signaling enrichment | Deep mechanistic interpretation of human proteomics data |

Both databases are legitimate tools for enrichment analysis. The choice depends on your research question, your organism, and the level of mechanistic detail you need.

## Understanding the Structural Differences Between KEGG and Reactome

### KEGG Pathway Architecture

KEGG organizes biological information into pathway maps that represent molecular interaction and reaction networks. Each map is a manually drawn diagram that integrates genes, proteins, enzymes, and chemical compounds into a unified representation. The Kyoto Encyclopedia of Genes and Genomes was established to link genomic information with higher-order systemic functions, and its pathway database remains one of the most widely used resources in bioinformatics.

The KEGG pathway maps are organized into broad categories including metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems, and human diseases. Each map contains nodes that represent individual genes or gene products, and edges that represent molecular interactions, reactions, or regulatory relationships. The maps are cross-referenced with KEGG Orthology, which groups genes across species into functional orthologs, enabling comparative analysis.

For proteomics researchers, the practical implication of KEGG architecture is that enrichment analysis returns pathway maps instead of individual reaction steps. When you identify that the "glycolysis / gluconeogenesis" pathway is enriched in your dataset, you are seeing a curated map of the enzymes and intermediates involved in that process. This is useful for identifying which broad biological processes are active in your experimental condition.

### Reactome Pathway Architecture

Reactome is a free, open-source, curated, and peer-reviewed pathway database that organizes biological processes into a hierarchical structure. The database is built around the concept of reactions, where each reaction describes the conversion of input molecules to output molecules, often with detailed information about subcellular localization, regulatory mechanisms, and participating proteins.

The hierarchical organization of Reactome means that pathways are nested. A top-level pathway such as "Metabolism" contains sub-pathways for carbohydrates, lipids, amino acids, and other molecular classes. Each sub-pathway contains individual reactions with detailed annotations. This structure allows researchers to drill down from broad process categories to specific molecular events.

Reactome is primarily human-centric, although orthology-based projections allow researchers to map human pathways onto other species. For proteomics experiments on human samples, Reactome provides the most detailed mechanistic annotation available. For non-human organisms, the orthology projections are useful but may miss species-specific pathways or regulatory mechanisms.

### Curation Philosophy and Update Frequency

KEGG pathway maps are manually curated but are updated less frequently than Reactome. The KEGG database also integrates multiple data types, including genomic, chemical, and systemic functional information, which means that pathway maps sometimes lag behind the latest literature. This is particularly relevant for rapidly evolving fields such as immunology or cancer biology.

Reactome employs a team of expert curators who continuously review the literature and update pathway annotations. The database is released in versioned increments, and each release includes new pathways, revised reactions, and updated annotations. For researchers studying well-characterized human biological processes, Reactome typically provides more current and more detailed information than KEGG.

The curation differences have practical consequences. A protein that was recently discovered to participate in a specific signaling cascade may appear in Reactome within months of publication but may not appear in KEGG for years. Conversely, KEGG pathway maps often include metabolic details that are not fully represented in Reactome's reaction-based format.

## Database Coverage and Annotation Depth

### Metabolic Pathway Coverage

KEGG has historically been the preferred database for metabolic pathway analysis. The KEGG pathway maps for central metabolism, amino acid biosynthesis, lipid metabolism, and xenobiotic degradation are comprehensive and well-annotated. The integration of chemical compound information into KEGG pathway maps allows researchers to connect protein expression changes to metabolic intermediates and enzyme substrates.

Reactome also covers metabolic pathways, but the representation is different. Reactome reactions include detailed information about the molecular entities involved, including post-translational modifications, subcellular localization, and regulatory mechanisms. For a researcher interested in the mechanistic details of a metabolic pathway, Reactome provides more granular information. For a researcher interested in the overall structure of metabolic networks, KEGG pathway maps are often more intuitive.

A study of bortezomib resistance in prostate cancer cells illustrates how both databases contribute to metabolic pathway analysis. The researchers identified 299 differentially expressed proteins and found that KEGG enrichment highlighted metabolic pathways, amino acid biosynthesis, and chemical carcinogenesis pathways. Reactome enrichment highlighted metabolism, translation, and nonsense-mediated decay. The two databases provided complementary views of the same proteomic dataset, with KEGG emphasizing metabolic network structure and Reactome emphasizing reaction-level mechanisms ([PubMed: Comparative Analysis of Acquired Resistance to Bortezomib in Prostate Cancer Cells](https://pubmed.ncbi.nlm.nih.gov/39799471)).

### Signaling Pathway Coverage

Both KEGG and Reactome provide extensive coverage of signaling pathways, but the annotation styles differ. KEGG signaling pathway maps are drawn as linear or branching cascades that show the major components of each pathway. Reactome signaling pathways are organized as reaction networks that include detailed information about protein modifications, complex formation, and downstream effects.

For example, the PI3K/Akt signaling pathway is represented in both databases. KEGG provides a pathway map that shows the major components and their interactions. Reactome provides a series of reactions that describe the phosphorylation events, protein complex formations, and downstream targets in molecular detail. A researcher studying the mechanistic basis of PI3K/Akt activation would find Reactome more informative. A researcher screening for pathway enrichment across a large proteomics dataset would find KEGG maps easier to interpret.

The choice between KEGG and Reactome for signaling pathway analysis depends on your analytical goal. If you need to identify which signaling pathways are active in your dataset, either database will work. If you need to understand the specific molecular events within a pathway, Reactome provides more detail.

### Disease and Drug Associations

KEGG includes a dedicated disease pathway category that maps genes and proteins to human diseases. This is useful for proteomics researchers studying disease mechanisms because it allows direct connection between differentially expressed proteins and disease pathways. KEGG also includes drug pathway information that links drugs to their molecular targets and downstream effects.

Reactome does not have a dedicated disease pathway category, but the detailed reaction annotations often include disease-relevant information. For example, a Reactome reaction may note that a specific mutation in a protein causes a particular disease. This information is embedded in the reaction annotation instead of organized into separate disease pathways.

The Integrated Pathway Analysis Database (IPAD) was developed to address the need for disease, drug, and organ specificity information in pathway analysis. The developers noted that most pathway resources do not contain disease-pathway, drug-pathway, or organ-pathway associations. This limitation applies to both KEGG and Reactome, although KEGG's disease pathway category provides some disease-specific information ([PubMed: IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis](https://pubmed.ncbi.nlm.nih.gov/23046449)).

## Statistical Methods for Enrichment Analysis

### Over-Representation Analysis

Over-representation analysis (ORA) is the most common statistical method for pathway enrichment. The approach compares the proportion of differentially expressed proteins in a pathway to the proportion expected by chance. The hypergeometric distribution or Fisher's exact test is typically used to calculate statistical significance.

Both KEGG and Reactome support ORA through various software tools. The clusterProfiler package in Bioconductor provides functions for both KEGG and Reactome enrichment analysis. The ReactomePA package is specifically designed for Reactome pathway enrichment. KOBAS is a web-based tool that supports KEGG enrichment analysis. The [Bioconductor project](https://bioconductor.org/) provides official documentation for these packages, including installation instructions and workflow examples.

The statistical results from ORA depend on the background set of proteins used for comparison. If you use all detected proteins in your mass spectrometry experiment as the background, the enrichment results reflect the pathways that are over-represented in your differentially expressed protein list relative to your detectable proteome. If you use the entire genome as the background, the results reflect enrichment relative to all possible proteins, which can introduce bias if your mass spectrometry platform has limited detection depth.

### Gene Set Enrichment Analysis

Gene set enrichment analysis (GSEA) is an alternative approach that does not require a threshold for defining differentially expressed proteins. Instead, GSEA ranks all proteins by their differential expression statistic and tests whether proteins in a given pathway are enriched at the top or bottom of the ranked list.

GSEA is particularly useful for proteomics data because it uses the full quantitative information from the experiment instead of a binary differential expression call. Both KEGG and Reactome pathway databases can be used with GSEA. The fgsea package in Bioconductor is a popular implementation that supports both databases.

A study of glioblastoma response to chemoirradiation used GSEA with Reactome pathways to identify biological processes associated with survival. The researchers also used KOBAS to identify KEGG pathways associated with differentially expressed proteins. The combination of GSEA with Reactome and ORA with KEGG provided complementary information about the biological processes underlying treatment response ([PubMed: Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation](https://pubmed.ncbi.nlm.nih.gov/37637066)).

### Multiple Testing Correction

Pathway enrichment analysis involves testing many pathways simultaneously, which requires correction for multiple testing. The Benjamini-Hochberg procedure for controlling the false discovery rate is the most commonly used method. Both KEGG and Reactome enrichment tools implement this correction by default.

The number of pathways tested affects the stringency of multiple testing correction. KEGG contains fewer pathway maps than Reactome contains pathways and sub-pathways. This means that Reactome enrichment analysis involves more statistical tests, which can result in more stringent corrected p-values. A pathway that appears significant with KEGG enrichment may not survive multiple testing correction with Reactome enrichment simply because Reactome tests more pathways.

This statistical difference has practical implications. If you are screening for any enriched pathway in an exploratory analysis, KEGG may be more sensitive because it tests fewer hypotheses. If you are testing a specific mechanistic hypothesis, Reactome may be more informative because it provides more detailed pathway annotations.

## Practical Workflow for Proteomics Pathway Analysis

### Step 1: Define Your Research Question

Before choosing a pathway database, define what you want to learn from your proteomics data. Are you asking which biological processes are active in your experimental condition? Are you asking how specific signaling pathways are regulated? Are you comparing your results across species?

For broad biological process identification, either database will work. For detailed mechanistic understanding, Reactome is generally more informative. For cross-species comparisons, KEGG has an advantage because of its orthology-based pathway maps.

### Step 2: Prepare Your Protein List

The quality of your enrichment analysis depends on the quality of your protein list. Use consistent protein identifiers across your dataset. UniProt accession numbers or gene symbols are commonly used for pathway enrichment analysis. Ensure that your protein list is filtered for confident identifications and that your quantitative comparisons are statistically robust.

The background set for enrichment analysis should reflect your experimental design. If you are using a mass spectrometry platform with limited detection depth, use the detected proteins as the background. If you are using a comprehensive proteomics approach, the entire proteome may be an appropriate background.

### Step 3: Run Enrichment Analysis with Both Databases

Run enrichment analysis with both KEGG and Reactome using the same protein list and the same statistical parameters. This allows you to compare the results and identify pathways that are consistently enriched across both databases. Pathways that appear in both results are likely robust biological signals. Pathways that appear in only one database may reflect database-specific annotation differences.

The clusterProfiler package in Bioconductor provides a unified interface for both KEGG and Reactome enrichment analysis. This allows you to run both analyses with consistent parameters and compare the results directly. The [Bioconductor project](https://bioconductor.org/) provides documentation and workflows for reproducible genomic analysis, including pathway enrichment.

### Step 4: Compare and Interpret Results

When comparing KEGG and Reactome enrichment results, focus on the biological interpretation instead of the statistical details. A pathway that is enriched in both databases is a strong signal. A pathway that is enriched in only one database may still be biologically meaningful, but you should examine the specific proteins driving the enrichment to understand why the databases differ.

For example, a study of exosome proteomics in small-for-gestational-age infants identified 91 differentially expressed proteins. Enrichment analysis revealed complement and coagulation cascades, lipid metabolism, neural development, PI3K/Akt signaling, and focal adhesion pathways. The researchers used both KEGG and Reactome for protein-protein interaction analysis and identified 39 differentially expressed proteins involved in the enriched pathways. The combination of both databases provided a more complete picture than either database alone ([PubMed: The Proteome of Exosomes at Birth Predicts Insulin Resistance, Adrenarche and Liver Fat in Childhood](https://pubmed.ncbi.nlm.nih.gov/40004184)).

### Step 5: Validate with Protein-Protein Interaction Analysis

Protein-protein interaction (PPI) analysis can complement pathway enrichment by identifying functional modules within your protein list. Tools such as STRING or Cytoscape can be used to construct interaction networks and identify densely connected protein clusters. These clusters often correspond to biological processes that may not be fully captured by pathway enrichment.

The exosome proteomics study used PPI analysis to identify 39 differentially expressed proteins involved in pathways enriched by both KEGG and Reactome. These proteins were associated with measures of adiposity, insulin resistance, and liver fat at age 7. The PPI analysis provided additional confidence in the pathway enrichment results by showing that the enriched proteins formed coherent interaction networks ([PubMed: The Proteome of Exosomes at Birth Predicts Insulin Resistance, Adrenarche and Liver Fat in Childhood](https://pubmed.ncbi.nlm.nih.gov/40004184)).

## Choosing the Right Database for Your Research Question

### When to Use KEGG

KEGG is the preferred choice when your research question involves metabolic pathways, cross-species comparisons, or disease pathway mapping. The KEGG pathway maps are comprehensive for central metabolism and provide a clear visual representation of metabolic networks. The orthology-based pathway maps allow direct comparison of pathway activity across species.

KEGG is also useful when you need to connect your proteomics results to other omics data types. The KEGG database integrates genomic, transcriptomic, proteomic, and metabolomic information, which facilitates multi-omics analysis. If you are studying metabolic diseases, KEGG disease pathways can help connect protein expression changes to disease mechanisms.

A study of liver steatosis and fibrosis in people living with HIV used both KEGG and Reactome for pathway enrichment. The researchers identified differentially expressed proteins associated with steatosis and fibrosis and found enrichment in mostly metabolic pathways. The KEGG pathway maps were particularly useful for interpreting the metabolic pathway enrichment in this context ([PubMed: Plasma proteomic signatures of liver steatosis and fibrosis in people living with HIV](https://pubmed.ncbi.nlm.nih.gov/39426127)).

### When to Use Reactome

Reactome is the preferred choice when your research question involves detailed mechanistic understanding of human biological processes. The reaction-based format provides information about specific molecular events, including post-translational modifications, protein complex formation, and subcellular localization. This level of detail is valuable for understanding the molecular mechanisms underlying your proteomics results.

Reactome is also useful when you are studying signaling pathways that involve complex regulatory mechanisms. The hierarchical organization of Reactome allows you to drill down from broad pathway categories to specific reactions, which can reveal regulatory details that are not visible in KEGG pathway maps.

The bortezomib resistance study illustrates the value of Reactome for mechanistic analysis. The researchers identified enrichment in translation and nonsense-mediated decay pathways using Reactome, which provided mechanistic insight into the acquired resistance phenotype. These pathways were not highlighted in the KEGG enrichment results, demonstrating the complementary value of Reactome ([PubMed: Comparative Analysis of Acquired Resistance to Bortezomib in Prostate Cancer Cells](https://pubmed.ncbi.nlm.nih.gov/39799471)).

### When to Use Both Databases

For most proteomics experiments, using both KEGG and Reactome provides the most complete biological interpretation. The two databases capture different aspects of biological knowledge, and combining them reduces the risk of missing important pathways due to database-specific annotation gaps.

The glioblastoma study used both databases to identify pathways associated with survival after chemoirradiation. KEGG pathways were identified using KOBAS, and Reactome pathways were identified using GSEA. The combination of both databases provided a more complete picture of the biological processes underlying treatment response than either database alone ([PubMed: Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation](https://pubmed.ncbi.nlm.nih.gov/37637066)).

When using both databases, present the results as complementary instead of redundant. KEGG enrichment identifies broad pathway categories, and Reactome enrichment provides mechanistic detail. The combination of both perspectives strengthens the biological interpretation of your proteomics data.

## Common Failure Patterns in Pathway Analysis

### Failure Pattern 1: Ignoring Database-Specific Biases

Researchers often assume that pathway enrichment results are database-independent. This assumption is incorrect. KEGG and Reactome have different annotation coverage, and the same protein list can produce different enrichment results depending on the database used.

The solution is to run enrichment analysis with both databases and interpret the results in the context of database-specific biases. If a pathway is enriched in both databases, it is likely a robust biological signal. If a pathway is enriched in only one database, examine the specific proteins driving the enrichment to understand why the databases differ.

### Failure Pattern 2: Using an Inappropriate Background Set

The background set used for enrichment analysis has a major impact on the results. Using the entire genome as the background when your mass spectrometry platform detects only a fraction of the proteome can introduce bias. Pathways containing proteins that are not detectable by your platform will appear artificially depleted.

The solution is to use the detected proteins in your experiment as the background set. This ensures that the enrichment analysis compares your differentially expressed proteins to the proteins you could have detected, instead of to all possible proteins.

### Failure Pattern 3: Over-Interpreting Marginal Enrichment

Pathway enrichment analysis produces p-values that are often interpreted as evidence of biological significance. However, a statistically significant enrichment does not necessarily mean that the pathway is biologically important. The enrichment may be driven by a small number of proteins, or the pathway may be enriched because of technical artifacts in the data.

The solution is to examine the specific proteins driving the enrichment and consider the effect sizes in addition to the statistical significance. A pathway enriched with a large number of proteins and large effect sizes is more biologically meaningful than a pathway enriched with a few proteins and small effect sizes.

### Failure Pattern 4: Ignoring Pathway Redundancy

Many biological processes are represented in multiple pathways, and the same protein can appear in multiple pathways. This redundancy can lead to overlapping enrichment results that are difficult to interpret. For example, a protein involved in both glycolysis and gluconeogenesis will appear in both pathway maps, and enrichment analysis may report both pathways as significant.

The solution is to examine the overlap between enriched pathways and identify the core biological processes represented by the overlapping proteins. Tools such as EnrichmentMap in Cytoscape can visualize pathway redundancy and identify functional modules.

### Failure Pattern 5: Neglecting Quality Control

The quality of your proteomics data directly affects the quality of your pathway enrichment results. Poor protein identification, inconsistent quantification, and inadequate statistical power can all lead to spurious enrichment results.

The solution is to implement rigorous quality control at every stage of the proteomics workflow. This includes validating protein identifications, checking quantitative reproducibility, and ensuring adequate sample size for statistical power. The [Galaxy Training Network](https://training.galaxyproject.org/) provides accessible workflow training for proteomics data analysis, including quality control steps.

## Records and Documentation for Reproducible Pathway Analysis

### Documenting Database Versions

Pathway databases are updated regularly, and the version of the database used for enrichment analysis affects the results. Document the database version and the date of analysis in your methods section. This allows other researchers to reproduce your analysis and understand the context of your results.

KEGG releases are versioned, and Reactome releases are versioned with specific release numbers. Record the exact version used for your analysis. The Bioconductor packages for pathway enrichment typically specify the database version in the package documentation.

### Documenting Analysis Parameters

The statistical parameters used for enrichment analysis should be documented in your methods. This includes the enrichment method (ORA or GSEA), the multiple testing correction method, the significance threshold, and the background set. These parameters affect the results, and documenting them allows other researchers to reproduce your analysis.

The [nf-core documentation](https://nf-co.re/docs) provides standards for reproducible workflow configuration, which can be applied to pathway enrichment analysis. Following these standards ensures that your analysis is reproducible and transparent.

### Storing Raw and Processed Data

Store both the raw mass spectrometry data and the processed protein lists used for enrichment analysis. The raw data should be deposited in a public repository such as the ProteomeXchange consortium. The processed protein lists and enrichment results should be stored in a structured format that allows re-analysis.

The [NCBI](https://www.ncbi.nlm.nih.gov/) provides data resources for storing and accessing biological data, including proteomics data. Depositing your data in public repositories ensures that your results can be verified and re-analyzed by other researchers.

## Limitations and Interpretation Boundaries

### KEGG Limitations

KEGG pathway maps are manually curated and may not reflect the latest literature. The maps are also simplified representations of biological processes, and they do not capture all the regulatory details that are known about a pathway. For example, KEGG pathway maps typically do not include information about post-translational modifications or subcellular localization.

KEGG also has limited coverage of some biological processes. The database is strongest for metabolic pathways and well-characterized signaling pathways. Less well-characterized processes, such as emerging areas of cell biology, may have limited KEGG pathway coverage.

### Reactome Limitations

Reactome is primarily human-centric, and the orthology-based projections to other species may not capture species-specific biology. For non-human organisms, KEGG may provide more accurate pathway annotations because the pathway maps are based on orthology groups that are more broadly applicable across species.

Reactome also has a complex hierarchical structure that can be difficult to navigate. The detailed reaction annotations are valuable for mechanistic understanding, but they can be overwhelming for researchers who are primarily interested in identifying broad biological processes.

### Statistical Limitations

Pathway enrichment analysis has inherent statistical limitations. The analysis assumes that proteins are independent, but proteins within a pathway are often co-regulated. This can lead to inflated significance estimates. The analysis also assumes that the background set is representative, which may not be true for mass spectrometry data with limited detection depth.

The multiple testing correction used in enrichment analysis controls the false discovery rate, but it does not guarantee that all significant results are biologically meaningful. The enrichment results should be interpreted in the context of your experimental design and biological knowledge.

## Safety and Regulatory Context for Clinical Proteomics

### Clinical Proteomics Applications

When proteomics pathway analysis is used for clinical research, additional considerations apply. The results may inform biomarker discovery, patient stratification, or treatment response prediction. The study of liver steatosis and fibrosis in people living with HIV used plasma proteomics for biomarker discovery, and the pathway enrichment results provided insight into the biological mechanisms underlying the disease ([PubMed: Plasma proteomic signatures of liver steatosis and fibrosis in people living with HIV](https://pubmed.ncbi.nlm.nih.gov/39426127)).

Clinical proteomics studies must adhere to regulatory requirements for human subjects research. This includes informed consent, ethical approval, and data protection. The pathway enrichment results should be interpreted in the context of the clinical study design and the limitations of the proteomics platform.

### Reporting Requirements

When reporting pathway enrichment results from clinical proteomics studies, include the database versions, analysis parameters, and statistical methods in the methods section. This allows other researchers to evaluate the robustness of the results and compare them with other studies.

The study of exosome proteomics in small-for-gestational-age infants reported the pathway enrichment results with specific details about the databases used and the statistical methods applied. This level of reporting transparency is essential for clinical proteomics research ([PubMed: The Proteome of Exosomes at Birth Predicts Insulin Resistance, Adrenarche and Liver Fat in Childhood](https://pubmed.ncbi.nlm.nih.gov/40004184)).

### Professional Escalation Criteria

If your pathway enrichment results are being used for clinical decision-making, consult with a bioinformatics specialist or a clinical researcher with expertise in pathway analysis. The interpretation of pathway enrichment results requires specialized knowledge, and errors in interpretation can have clinical consequences.

If you observe unexpected or contradictory pathway enrichment results, escalate the issue to a bioinformatics specialist. The discrepancy may indicate a technical issue with the data, a database annotation error, or a biological phenomenon that requires further investigation.

## A Practical Decision Framework for KEGG versus Reactome in Proteomics Studies

Choosing between KEGG and Reactome does not have to be a matter of preference or habit. A structured decision framework based on your experimental context, organism, and biological question can reduce the risk of selecting a database that poorly matches your data. The framework below uses four checkpoints that you can apply before running any enrichment analysis.

### Checkpoint 1: Define the Biological Question Type

Your primary research question determines which database will provide the most interpretable output. Write down your question in one sentence before selecting a database. If your question asks which broad biological processes are altered in your condition, KEGG pathway maps provide a rapid and visually interpretable answer. If your question asks how a specific process is regulated at the molecular level, Reactome reactions provide the mechanistic detail you need.

A study of bortezomib resistance in prostate cancer cells illustrates this distinction. The researchers asked which biological processes changed in resistant cells. KEGG enrichment returned metabolic pathways, amino acid biosynthesis, and chemical carcinogenesis. Reactome enrichment returned metabolism, translation, and nonsense-mediated decay. Both answers were correct for the same protein list, but they answered different implicit questions. KEGG answered which pathway maps were over-represented. Reactome answered which reaction networks were over-represented ([PubMed: Comparative Analysis of Acquired Resistance to Bortezomib in Prostate Cancer Cells](https://pubmed.ncbi.nlm.nih.gov/39799471)).

### Checkpoint 2: Assess Organism and Annotation Confidence

KEGG maintains pathway maps for a broad range of species using orthology groups. Reactome is primarily human-centric with orthology projections to other species. For human proteomics data, both databases are appropriate. For non-human organisms, KEGG generally provides more reliable pathway annotations because the orthology-based maps are built for cross-species comparison.

For model organisms with well-curated Reactome projections, such as mouse or rat, Reactome can still be useful. For less common organisms, check whether your proteins of interest have Reactome orthology annotations before committing to that database. If a large fraction of your differentially expressed proteins lack Reactome annotations, the enrichment results will be biased toward the annotated subset. KEGG is the safer default for non-human proteomics data.

### Checkpoint 3: Evaluate Pathway Granularity Needs

Consider whether you need pathway-level or reaction-level resolution. KEGG returns pathway maps that group many reactions into a single visual diagram. Reactome returns individual reactions nested within hierarchical pathways. If your downstream analysis requires knowing which specific enzymatic steps or protein complexes are altered, Reactome provides that granularity. If you only need to know that glycolysis is altered, KEGG is sufficient.

The exosome proteomics study in small-for-gestational-age infants used both databases for protein-protein interaction analysis. The researchers identified 91 differentially expressed proteins and found enrichment in complement and coagulation cascades, lipid metabolism, neural development, PI3K/Akt signaling, phagocytosis, and focal adhesion. They then used both KEGG and Reactome to identify 39 proteins involved in the enriched pathways. The combination allowed them to move from pathway-level identification to protein-level mechanistic interpretation ([PubMed: The Proteome of Exosomes at Birth Predicts Insulin Resistance, Adrenarche and Liver Fat in Childhood](https://pubmed.ncbi.nlm.nih.gov/40004184)).

### Checkpoint 4: Determine Statistical Sensitivity Requirements

KEGG contains fewer pathway maps than Reactome contains pathways and sub-pathways. This difference affects multiple testing correction. When you test 300 Reactome pathways instead of 80 KEGG maps, the corrected p-value threshold becomes more stringent for Reactome. If your study is exploratory and you want to maximize sensitivity for detecting any enriched pathway, KEGG may identify signals that Reactome would filter out after correction. If your study is confirmatory and you want to minimize false positives, the more stringent Reactome correction is an advantage.

The glioblastoma study used KOBAS for KEGG pathway identification and GSEA against Reactome databases. The researchers identified 389 differentially expressed proteins and found several pathways relevant to cancer metabolism and progression. The lowest survival group was associated with proliferative pathways. Using both databases with different statistical approaches allowed the researchers to validate pathway hits internally ([PubMed: Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation](https://pubmed.ncbi.nlm.nih.gov/37637066)).

### Implementing the Framework in Practice

Apply the four checkpoints in order before running your enrichment analysis. Record your answers in a simple table with columns for the checkpoint, your decision, and the rationale. This record becomes part of your analysis documentation and helps other researchers understand why you selected a particular database.

For most proteomics experiments, the framework will lead to one of three outcomes. First, use KEGG alone when your organism is non-human, your question is about broad metabolic or signaling pathway activity, and you want maximum sensitivity. Second, use Reactome alone when your organism is human, your question requires mechanistic detail, and you can tolerate more stringent multiple testing correction. Third, use both databases when your organism is human or a well-annotated model organism, your question spans both broad process identification and mechanistic understanding, and you have sufficient statistical power to handle the additional testing burden.

### Recording Database Decisions for Reproducibility

Document the database version, the date of analysis, and the specific pathway library used. KEGG releases are versioned, and Reactome releases have specific version numbers. The Bioconductor packages for pathway enrichment specify the database version in their documentation. Record these details in your methods section alongside the enrichment method, multiple testing correction, significance threshold, and background set.

The [Bioconductor project](https://bioconductor.org/) provides official documentation for reproducible genomic analysis, including pathway enrichment workflows. Following these standards ensures that your database selection and analysis parameters are transparent and reproducible. The [nf-core documentation](https://nf-co.re/docs) also provides standards for reproducible workflow configuration that apply to pathway enrichment analysis.

### Troubleshooting Framework Failures

If your enrichment results are uninformative or contradictory, revisit the framework checkpoints. A common failure is selecting Reactome for a non-human organism without checking annotation coverage. Another common failure is using KEGG when your research question requires reaction-level detail that KEGG does not provide. A third failure is running both databases but interpreting the results as redundant instead of complementary.

If you observe that a pathway is enriched in one database but not the other, examine the specific proteins driving the enrichment. The discrepancy may reflect a genuine annotation difference. For example, a protein recently discovered to participate in a signaling cascade may appear in Reactome within months of publication but may not appear in KEGG for years. The [EMBL-EBI Training](https://www.ebi.ac.uk/training) resources provide practical education on interpreting pathway analysis results and understanding database-specific annotation differences.

### Escalation Criteria for Persistent Problems

If you have applied the framework and still cannot interpret your enrichment results, escalate to a bioinformatics specialist. Persistent problems include large fractions of your protein list lacking annotations in the selected database, enrichment results that contradict known biology without explanation, or statistical results that change dramatically when you alter the background set. A specialist can help you determine whether the issue is technical, database-related, or biological.

The [Galaxy Training Network](https://training.galaxyproject.org/) provides accessible workflow training for proteomics data analysis, including pathway enrichment and quality control steps. The [Carpentries lessons](https://carpentries.org/lessons) offer foundational computing and data skills that support reproducible analysis practices. These resources can help you build the skills needed to troubleshoot pathway analysis problems independently before seeking specialist consultation.

## Frequently Asked Questions

### What is the main difference between KEGG and Reactome for proteomics analysis?

KEGG organizes biological information into manually curated pathway maps that integrate genes, proteins, and chemical compounds. Reactome organizes biological processes into a hierarchical, reaction-based format with detailed molecular annotations. KEGG is stronger for metabolic pathway analysis and cross-species comparisons, while Reactome provides more detailed mechanistic information for human biological processes.

### Can I use both KEGG and Reactome in the same proteomics analysis?

Yes, using both databases is recommended for most proteomics experiments. The two databases capture different aspects of biological knowledge, and combining them reduces the risk of missing important pathways due to database-specific annotation gaps. Run enrichment analysis with both databases using the same protein list and statistical parameters, then compare the results.

### Which database is better for metabolic pathway analysis?

KEGG is generally preferred for metabolic pathway analysis because the pathway maps are comprehensive for central metabolism and provide a clear visual representation of metabolic networks. The integration of chemical compound information into KEGG pathway maps allows researchers to connect protein expression changes to metabolic intermediates and enzyme substrates.

### Which database is better for signaling pathway analysis?

Reactome is generally preferred for signaling pathway analysis because the reaction-based format provides detailed information about specific molecular events, including post-translational modifications, protein complex formation, and downstream effects. KEGG signaling pathway maps are useful for identifying which signaling pathways are active, but Reactome provides more mechanistic detail.

### How do I choose the background set for enrichment analysis?

Use the detected proteins in your mass spectrometry experiment as the background set. This ensures that the enrichment analysis compares your differentially expressed proteins to the proteins you could have detected, instead of to all possible proteins. Using the entire genome as the background can introduce bias if your mass spectrometry platform has limited detection depth.

### Why do KEGG and Reactome produce different enrichment results for the same protein list?

The two databases have different annotation coverage and curation philosophies. KEGG pathway maps are manually curated and updated less frequently, while Reactome is continuously updated by expert curators. The databases also organize information differently, with KEGG using pathway maps and Reactome using reaction-based hierarchies. These structural differences lead to different enrichment results.

### What statistical methods are used for pathway enrichment analysis?

The two main statistical methods are over-representation analysis (ORA) and gene set enrichment analysis (GSEA). ORA compares the proportion of differentially expressed proteins in a pathway to the proportion expected by chance using the hypergeometric distribution or Fisher's exact test. GSEA ranks all proteins by their differential expression statistic and tests whether proteins in a given pathway are enriched at the top or bottom of the ranked list.

### How should I report pathway enrichment results in my publications?

Document the database versions, analysis parameters, and statistical methods in your methods section. Include the enrichment method (ORA or GSEA), the multiple testing correction method, the significance threshold, and the background set. This allows other researchers to reproduce your analysis and understand the context of your results.

## Related Bioinformatics Guides

- [Pathway Enrichment Analysis for Proteomics: Tools and Interpretation](/knowledge/bioinformatics/pathway-enrichment-analysis-for-proteomics-tools-and-interpretation)
- [The KEGG Database and Pathway Analysis](/knowledge/bioinformatics/the-kegg-database-and-pathway-analysis)
- [Proteomics Analysis Tools: A Comparative Guide for Functional Interpretation](/knowledge/bioinformatics/proteomics-analysis-tools-a-comparative-guide-for-functional-interpretation)
- [Genomic Data Analysis Tools: A Comparative Guide for Researchers](/knowledge/bioinformatics/genomic-data-analysis-tools-a-comparative-guide-for-researchers)
- [How to Interpret Gene Set Enrichment Analysis Results](/knowledge/bioinformatics/how-to-interpret-gene-set-enrichment-analysis-results)

## References and Further Reading

- [NCBI Data Resources](https://www.ncbi.nlm.nih.gov/). National Center for Biotechnology Information.
- [EMBL-EBI Training](https://www.ebi.ac.uk/training). European Bioinformatics Institute.
- [Bioconductor](https://bioconductor.org/). Bioconductor Project.
- [Galaxy Training Network](https://training.galaxyproject.org/). Galaxy Project.
- [nf-core Documentation](https://nf-co.re/docs). nf-core.
- [The Carpentries Lessons](https://carpentries.org/lessons). The Carpentries.
- [Plasma proteomic signatures of liver steatosis and fibrosis in people living with HIV: a cross-sectional study.](https://pubmed.ncbi.nlm.nih.gov/39426127). EBioMedicine, 2024.
- [The Proteome of Exosomes at Birth Predicts Insulin Resistance, Adrenarche and Liver Fat in Childhood.](https://pubmed.ncbi.nlm.nih.gov/40004184). International journal of molecular sciences, 2025.
- [Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.](https://pubmed.ncbi.nlm.nih.gov/37637066). Frontiers in oncology, 2023.
- [Comparative Analysis of Acquired Resistance to Bortezomib in Prostate Cancer Cells Using Proteomic and Bioinformatic Tools.](https://pubmed.ncbi.nlm.nih.gov/39799471). Journal of cellular and molecular medicine, 2025.
- [IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis.](https://pubmed.ncbi.nlm.nih.gov/23046449). BMC bioinformatics, 2012.

> This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.