Reactome vs. KEGG for Pathway Analysis of RNA-seq Data: Which Database Should You Trust?

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-20

Key Takeaways

• KEGG excels in detailed metabolic pathway analysis, providing enzyme commission numbers and compound structures, making it ideal for studies of metabolism, detoxification, and drug metabolism, as demonstrated in studies of dietary interventions and environmental exposures.
• Reactome offers granular, reaction-level detail for signaling and regulatory mechanisms, including protein modifications and translocation events, which is crucial for dissecting complex molecular cascades in immune responses and cell signaling.
• For disease mechanism and biomarker discovery, utilizing both KEGG and Reactome in parallel is recommended due to their complementary coverage, with KEGG offering extensive disease pathway maps and Reactome providing detailed molecular annotations.
• KEGG's organism-specific pathway maps are advantageous for non-model organisms and comparative genomics, enabling pathway enrichment analysis in species beyond human and mouse, as seen in studies of livestock breeds.
• The choice between Reactome and KEGG should be driven by the specific research question, with KEGG favored for metabolic and translational research, and Reactome for in-depth mechanistic studies of molecular interactions.
• Both databases have limitations, including incomplete genome annotation and static representations of dynamic biological processes, necessitating careful interpretation of enrichment results and consideration of database-specific coverage differences.

Researchers analyzing RNA-seq data face a practical decision when choosing a pathway database for enrichment analysis. Reactome and KEGG are the two most frequently used resources, yet they differ substantially in curation philosophy, content structure, and update frequency. This article compares both databases across these dimensions and provides concrete guidance for selecting the appropriate resource for specific research questions. The comparison draws on published RNA-seq studies that used both databases, official documentation from bioinformatics training resources, and the operational characteristics of each database as described in the primary literature.

The Core Difference Between Reactome and KEGG

Reactome and KEGG represent two distinct approaches to organizing biological pathway knowledge. KEGG, the Kyoto Encyclopedia of Genes and Genomes, organizes pathways as graphical maps that show molecular interactions and reactions in a standardized diagram format. Reactome organizes pathways as a hierarchical collection of reactions, where each reaction is supported by literature evidence and curated by expert biologists.

The practical consequence of this structural difference appears in enrichment results. A study of human dermal fibroblasts treated with polarized photobiomodulation identified 21 significantly enriched KEGG pathways and 24 significantly enriched Reactome pathways from the same set of 71 differentially expressed genes (PubMed 36958088). The overlap between the two databases was partial, meaning some biological signals appeared in one database but not the other. This pattern repeats across many published studies and reflects the underlying content differences instead of a defect in either resource.

For researchers, the implication is straightforward. A pathway analysis that uses only one database will miss biological signals that the other database captures. The choice of database changes the interpretation of the experiment, and the choice should be made deliberately based on the research question.

Database Content and Curation Standards

KEGG Content Structure

KEGG pathways are manually drawn maps that integrate genomic, chemical, and systemic functional information. The database includes reference pathways for representative organisms and organism-specific pathway maps that show which genes are present in a particular species. This organism-specific mapping is a distinctive feature. When a researcher runs KEGG enrichment on human RNA-seq data, the analysis uses human-specific pathway maps that indicate which genes in the pathway are present in the human genome.

The pathway maps themselves are the analytical unit. Each map has a stable identifier, such as hsa04141 for protein processing in the endoplasmic reticulum or hsa04310 for Wnt signaling. These identifiers appear consistently in the literature. A study of oil dispersant effects on human airway epithelial cells reported upregulation of ribosomal biosynthesis (hsa03008), protein processing (hsa04141), Wnt signaling (hsa04310), neurotrophin signaling (hsa04722), and insulin signaling (hsa04910) pathways under Corexit 9527 treatment (ScienceDirect). The use of stable pathway identifiers allows direct comparison across studies.

KEGG content is organized into categories including metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems, and human diseases. The disease and drug categories are particularly well developed, which makes KEGG useful for translational research questions.

Reactome Content Structure

Reactome is a curated database of human biological processes organized as a hierarchy of reactions. Each reaction has associated literature evidence, and the database includes orthology-based projections to other species. The hierarchical structure means that pathways are nested. A broad pathway such as immune system contains sub-pathways for specific processes, and each sub-pathway contains individual reactions.

The curation model for Reactome involves expert biologists who annotate reactions based on published experimental evidence. This literature-based curation means that Reactome content reflects the current state of molecular understanding, with each reaction traceable to specific publications. The database is updated regularly as new evidence accumulates.

The hierarchical organization affects enrichment analysis output. When a researcher runs Reactome enrichment, the results often include both broad parent pathways and specific child pathways. This can produce a longer list of enriched terms compared to KEGG, as observed in the photobiomodulation study where Reactome returned 24 enriched pathways versus 21 for KEGG from the same gene set (PubMed 36958088).

Update Frequency and Maintenance

KEGG and Reactome differ in their update cycles. Reactome releases are versioned and occur several times per year, with each release adding new reactions and pathways based on recent literature. KEGG updates are continuous but the pathway maps are revised less frequently, with some maps remaining stable for extended periods.

The update frequency matters for research in rapidly moving fields. A study published in 2025 on microRNA biomarkers in candidemia used both KEGG and Reactome enrichment analyses to predict miRNA targets and identify perturbed pathways (PubMed 41135862). The researchers integrated miRNA-mRNA expression data and found that the two databases provided complementary information about cell-cycle and DNA-replication programs. Had the analysis relied on a single database, the interpretation of the miRNA regulatory networks would have been incomplete.

Coverage Differences Across Biological Domains

Metabolic Pathways

KEGG has historically been the stronger resource for metabolic pathway analysis. The metabolic pathway maps in KEGG are detailed and include enzyme commission numbers, compound structures, and reaction equations. This makes KEGG particularly suitable for studies of metabolism, including the type of analysis performed in the grape consumption study in mice, where differentially expressed genes were enriched in drug metabolism, glutathione, detoxification, and oxidative stress pathways (PubMed 35804799).

Reactome also covers metabolism but with a different organizational structure. Reactome reactions include detailed molecular participants and literature citations, but the visual representation of metabolic pathways is less map-like than KEGG. For researchers who want to visualize a complete metabolic pathway with all its branches and connections, KEGG maps are often more useful.

Signaling Pathways

Both databases cover signaling pathways extensively, but their representations differ. KEGG signaling pathway maps show the major components and their interactions in a standardized diagram. Reactome signaling pathways include more detailed reaction steps, including protein modifications, translocation events, and complex assembly.

The choice between databases for signaling pathway analysis depends on the research question. If the question is which signaling pathways are enriched in a gene set, both databases will provide answers. If the question is which specific molecular events within a signaling pathway are affected, Reactome provides more granular detail.

Disease and Drug Pathways

KEGG includes a substantial collection of disease pathways and drug-related pathways. The human disease category includes cancer pathways, neurodegenerative disease pathways, and infectious disease pathways. The drug category includes pathways for drug metabolism and mechanisms of action.

Reactome includes disease annotations but organizes them differently. Reactome pathways can be annotated with disease associations, and the database includes some disease-specific pathways. However, the disease coverage in Reactome is less extensive than in KEGG.

A study of colorectal cancer biomarkers used KEGG pathway enrichment analysis and identified RNA binding and RNA transport as the most enriched functions among upregulated genes, with mineral absorption pathways enriched among downregulated genes (Scientific Reports). The researchers used the enrichR package for this analysis, which accesses KEGG pathway annotations. The study demonstrates the continued utility of KEGG for cancer research applications.

At a Glance: Reactome vs. KEGG Comparison

Practical Workflow for Pathway Analysis

Step 1: Define the Research Question

The choice between Reactome and KEGG should begin with the research question, not with the available software. A researcher studying metabolic reprogramming in a disease model will likely need KEGG for its detailed metabolic maps. A researcher studying immune signaling networks may find Reactome more informative due to its detailed reaction-level curation.

The allergic rhinitis study provides an example of a research question that benefited from multiple pathway resources (Frontiers in Immunology). The researchers combined metabolomic analysis, single-cell transcriptomics, and bulk RNA sequencing to investigate glutamine metabolism in allergic rhinitis. Their pathway analyses positioned fibroblast growth factor receptor 1 within differentially enriched signaling networks. The integration of multiple data types and pathway resources allowed them to build an associative model linking metabolic alterations to immune signaling.

Step 2: Prepare the Gene List

Pathway enrichment analysis requires a defined gene list. For RNA-seq data, this typically means the list of differentially expressed genes from a comparison of interest. The gene list should be filtered for statistical significance and effect size according to the study design.

The format of gene identifiers matters for enrichment analysis. Both Reactome and KEGG enrichment tools accept common gene identifiers including Entrez Gene IDs, Ensembl IDs, and gene symbols. The NCBI provides official descriptions of gene identifiers and their relationships through its data resources. Researchers should verify that their gene list uses consistent identifiers before running enrichment analysis.

Step 3: Run Enrichment Analysis

Several software options exist for pathway enrichment analysis. The Bioconductor project provides R packages for enrichment analysis, including packages that access both Reactome and KEGG databases. These packages follow reproducible analysis standards and integrate with the broader Bioconductor ecosystem for RNA-seq analysis.

The Galaxy Training Network provides accessible workflow training for bioinformatics analysis, including pathway enrichment. These training materials are useful for researchers who are new to pathway analysis or who want to verify that their analysis approach follows established practices.

For researchers using community pipelines, the nf-core documentation describes standards for reproducible bioinformatics workflows. These pipelines often include pathway analysis steps and provide configuration options for choosing between Reactome and KEGG.

Step 4: Interpret Results in Context

Enrichment analysis results should be interpreted with attention to the database structure. A pathway that appears enriched in Reactome but not KEGG may reflect a genuine biological signal that is only annotated in one database, or it may reflect differences in how the databases define pathway boundaries.

The photobiomodulation study illustrates this interpretation challenge (PubMed 36958088). The researchers found that most differentially expressed genes were downregulated in the polarized light treatment group compared to controls. The KEGG and Reactome enrichment analyses both identified significant pathways, but the specific pathways differed between databases. The researchers interpreted the downregulation as potentially reflecting decreased cellular stress under treatment conditions, with the pathway differences between databases providing complementary perspectives.

Choosing the Right Database for Your Research Question

When to Use KEGG

KEGG is the appropriate choice when the research question involves metabolism, disease mechanisms, or drug responses. The detailed metabolic maps in KEGG allow researchers to visualize complete pathways and identify the specific enzymes and compounds affected by their experimental conditions.

The oil dispersant study provides a clear example of KEGG-focused analysis (ScienceDirect). The researchers used KEGG pathway-based tools to analyze transcriptomic perturbations in human airway epithelial cells exposed to crude oil and dispersants. Their analysis identified upregulation of ribosomal biosynthesis, protein processing, Wnt signaling, neurotrophin signaling, and insulin signaling pathways. The KEGG pathway identifiers allowed them to report specific pathway perturbations with precision.

KEGG is also useful when the research involves non-model organisms. The organism-specific pathway maps in KEGG allow researchers to analyze pathway enrichment in species beyond human and mouse. A study of differentially expressed genes in the longissimus dorsi muscle of three pig breeds used KEGG pathway enrichment analysis to identify pathways associated with muscle growth (Frontiers in Veterinary Science). The researchers compared two indigenous Chinese pig breeds with an introduced Landrace breed and identified hundreds of differentially expressed genes, with KEGG enrichment providing functional context.

When to Use Reactome

Reactome is the appropriate choice when the research question involves detailed molecular mechanisms, protein interactions, or signaling cascades. The reaction-level curation in Reactome provides granular detail about the specific molecular events within a pathway.

The sepsis study provides an example of Reactome-complementary analysis (Frontiers in Immunology). The researchers integrated single-cell and bulk transcriptomic data to identify ubiquitination-related gene networks in sepsis. Their analysis identified conventional dendritic cells as the most transcriptionally perturbed immune population and found that type-1 conventional dendritic cells specifically exhibited activation of ubiquitination signatures. The pathway analysis positioned these findings within TNF signaling networks, with the detailed reaction-level information from Reactome supporting the mechanistic interpretation.

Reactome is also useful when the research question involves immune signaling or cell-cell communication. The hierarchical organization of Reactome pathways allows researchers to identify both broad biological programs and specific molecular events within those programs.

When to Use Both Databases

The strongest analytical approach for most research questions is to use both databases and compare the results. This approach provides complementary perspectives and reduces the risk of missing biological signals that only one database captures.

The candidemia study used integrated KEGG and Reactome enrichment analyses to predict miRNA targets and identify perturbed pathways (PubMed 41135862). The researchers found that cell-cycle and DNA-replication programs were affected in both Candida albicans and non-albicans species groups, while histidine and phenylalanine metabolism were uniquely affected in C. albicans infections. The use of both databases allowed the researchers to identify both shared and species-specific pathway perturbations.

The baicalein study similarly used GO, KEGG, and Reactome enrichment analyses to investigate the protective effects of baicalein against LPS-induced endothelial cell dysfunction (PubMed 41783153). The researchers found that differentially expressed genes were primarily enriched in TNF signaling, NF-κB signaling, and immune regulation pathways. The convergence of results across multiple databases strengthened the confidence in the pathway interpretation.

Software and Tools for Pathway Analysis

R Packages and Bioconductor

The Bioconductor project provides the most widely used R packages for pathway enrichment analysis. These packages include clusterProfiler, which supports both KEGG and Reactome enrichment, and ReactomePA, which is specifically designed for Reactome analysis. The Bioconductor documentation provides installation instructions and usage examples for these packages.

The Bioconductor ecosystem emphasizes reproducible analysis. Packages are versioned, and analyses can be documented using R Markdown or similar tools. This reproducibility is important for pathway analysis because database versions change over time, and results can vary between database releases.

Web-Based Tools

Both Reactome and KEGG provide web-based analysis tools. The Reactome Pathway Browser allows researchers to upload gene lists and visualize enrichment results in the context of the pathway hierarchy. The KEGG Mapper tool allows researchers to upload gene lists and map them onto KEGG pathway diagrams.

Web-based tools are useful for quick analyses and for researchers who do not use R. However, web-based tools have limitations for large-scale or reproducible analyses. The Galaxy Training Network provides tutorials for pathway analysis that can be run in the Galaxy platform, which provides a reproducible web-based environment for bioinformatics analysis.

Command-Line Tools

For researchers working with large datasets or integrating pathway analysis into automated pipelines, command-line tools provide the most flexibility. The nf-core documentation describes community pipelines that include pathway analysis steps and can be configured to use either Reactome or KEGG.

Command-line tools require more technical expertise but provide greater control over the analysis. Researchers who use command-line tools should document their analysis parameters carefully to ensure reproducibility.

Quality Control and Reproducibility

Database Version Documentation

Pathway enrichment results depend on the database version used for the analysis. Both Reactome and KEGG update their content over time, and these updates can change enrichment results. Researchers should document the database version in their methods and report it in their publications.

The Bioconductor packages for pathway analysis typically specify the database version in their documentation. Researchers should record this information along with the software version and analysis parameters.

Gene Identifier Consistency

Gene identifier consistency is a common source of errors in pathway analysis. Researchers should verify that their gene list uses the same identifier type throughout and that the identifiers are compatible with the enrichment tool being used.

The NCBI provides resources for converting between gene identifier types and for verifying gene identity. Researchers should use these resources to ensure that their gene lists are correctly formatted before running enrichment analysis.

Multiple Testing Correction

Pathway enrichment analysis involves testing many pathways simultaneously, which requires multiple testing correction. The choice of correction method affects the results, and researchers should report their correction method in their methods section.

Common correction methods include the Benjamini-Hochberg false discovery rate and the Bonferroni correction. The false discovery rate is more commonly used in pathway analysis because it provides a balance between sensitivity and specificity.

Common Failure Patterns in Pathway Analysis

Overinterpretation of Enrichment Results

A common failure pattern is overinterpreting enrichment results without considering the database structure. An enriched pathway does not necessarily mean that the pathway is activated or inhibited. It means that the pathway contains more differentially expressed genes than expected by chance.

The photobiomodulation study provides a cautionary example (PubMed 36958088). The researchers found that most differentially expressed genes were downregulated in the treatment group. The enrichment analysis identified significant pathways, but the interpretation required careful consideration of the direction of gene expression changes and the biological context.

Ignoring Database Coverage Differences

Another common failure pattern is ignoring the coverage differences between databases. A pathway that appears enriched in one database but not another may reflect genuine biological signal or may reflect differences in how the databases annotate the pathway.

Researchers should compare results across databases and investigate discrepancies. The comparison can reveal biological insights that would be missed by using a single database.

Using Inappropriate Gene Identifiers

Using gene identifiers that are not compatible with the enrichment tool is a common source of errors. Researchers should verify that their gene identifiers are correctly formatted and that they match the identifier type expected by the tool.

Failing to Document Analysis Parameters

Failing to document analysis parameters makes it impossible to reproduce the analysis. Researchers should record the database version, software version, gene identifier type, statistical methods, and multiple testing correction method.

Limitations of Pathway Databases

Incomplete Annotation

Both Reactome and KEGG have incomplete annotation of the genome. Many genes are not yet assigned to pathways, and many pathways are not fully annotated. This incompleteness means that enrichment analysis can miss biological signals that involve unannotated genes.

The pig muscle study identified 11,213 genes in the longissimus dorsi muscle tissue, of which 7,127 were co-expressed across three breeds (Frontiers in Veterinary Science). The pathway enrichment analysis could only use the subset of these genes that were annotated in the pathway databases. Genes without pathway annotations were excluded from the analysis, potentially missing biological signals.

Species-Specific Differences

Pathway databases have variable coverage across species. Human and mouse pathways are the most extensively annotated, while other species have less complete coverage. Researchers working with non-model organisms should verify that their species of interest has adequate pathway coverage.

The pig muscle study used KEGG pathway enrichment analysis for a porcine study (Frontiers in Veterinary Science). The researchers were able to identify enriched pathways, but the coverage of porcine pathways is less complete than human or mouse pathways.

Static Representation of Dynamic Processes

Pathway databases represent biological processes as static diagrams or reaction hierarchies. This representation cannot capture the dynamic nature of biological processes, including temporal changes in pathway activity, spatial organization, or context-dependent regulation.

The ATAC-seq and RNA-seq study of synovium-derived mesenchymal stem cells found that chromatin accessibility changes preceded transcriptional changes during osteogenic induction (Epigenetics & Chromatin). The pathway analysis could identify the biological programs involved, but it could not capture the temporal dynamics of the regulatory process.

Records and Documentation for Pathway Analysis

Analysis Records

Researchers should maintain records of their pathway analysis, including the input gene list, the database version, the software version, the analysis parameters, and the output results. These records should be sufficient to reproduce the analysis at a later time.

The Carpentries lessons provide training on reproducible research practices, including version control and documentation. These practices are directly applicable to pathway analysis and other bioinformatics workflows.

Publication Reporting

Publications should report the pathway analysis methods in sufficient detail for readers to understand and reproduce the analysis. This includes the database used, the database version, the enrichment method, the statistical threshold, and the multiple testing correction method.

The published studies cited in this article provide examples of pathway analysis reporting. The candidemia study reported using integrated KEGG and Reactome enrichment analyses (PubMed 41135862). The photobiomodulation study reported the number of enriched pathways in each database (PubMed 36958088). The baicalein study reported using GO, KEGG, and Reactome enrichment analyses (PubMed 41783153).

Professional Escalation Criteria

When to Seek Expert Assistance

Researchers should consider seeking expert assistance with pathway analysis when they encounter specific challenges. These include difficulty interpreting enrichment results in a biological context, uncertainty about which database is appropriate for a research question, or challenges with software installation or configuration.

Bioinformatics training resources can provide assistance. The EMBL-EBI Training program offers courses on pathway analysis and related topics. The Galaxy Training Network provides tutorials that can be followed at the researcher's own pace. The Carpentries lessons provide foundational training in computing and data analysis skills.

When to Consult a Bioinformatics Core

Researchers working with large datasets or complex experimental designs should consider consulting a bioinformatics core facility. These facilities provide expert assistance with analysis design, software configuration, and result interpretation.

The nf-core documentation describes community standards for bioinformatics pipelines. Researchers who plan to use community pipelines for pathway analysis should review this documentation to understand the pipeline configuration options and quality control measures.

Safety and Regulatory Context

Data Management

RNA-seq data and pathway analysis results should be managed according to institutional and funder requirements. This includes data storage, data sharing, and data retention policies. The NCBI provides data repositories for sequence data and related analysis results.

Reproducibility Requirements

Many journals and funding agencies require reproducible analysis methods. Researchers should ensure that their pathway analysis is reproducible by documenting all analysis parameters and using versioned software and databases.

Ethical Considerations

Pathway analysis of human data raises ethical considerations related to privacy and consent. Researchers should ensure that their analysis complies with institutional review board requirements and applicable regulations.

A Practical Decision Framework for Selecting Reactome, KEGG, or Both

Choosing between Reactome and KEGG does not need to be a binary decision made once at the start of a project. A structured decision framework that evaluates the research question, the biological domain under investigation, the species being studied, and the intended downstream use of the results can reduce the risk of missing biologically meaningful signals. This section provides a concrete framework that researchers can apply before running enrichment analysis, along with a record system for documenting database choices and a troubleshooting method for reconciling discrepant results.

Step 1: Classify the Primary Biological Question

The first decision point is to classify the research question into one of four categories. This classification determines which database should be the primary analysis resource and which should be used as a secondary validation resource.

Category A: Metabolic and biochemical questions. If the study investigates metabolic reprogramming, nutrient utilization, detoxification, drug metabolism, or biosynthetic pathways, KEGG should be the primary database. The KEGG metabolic maps include enzyme commission numbers, compound structures, and reaction equations that are not available in the same detail in Reactome. A study of dietary grape consumption in mice used KEGG enrichment to identify drug metabolism, glutathione, detoxification, and oxidative stress pathways in the liver (PubMed 35804799). The researchers were able to connect differentially expressed genes such as Gstp1, Gpx4, Gpx7, Gpx8, Gss, and Sod1 to specific metabolic and detoxification pathways. This level of metabolic detail is a KEGG strength.

Category B: Signaling and regulatory mechanism questions. If the study investigates signal transduction cascades, immune regulation, transcriptional control, or protein interaction networks, Reactome should be the primary database. The reaction-level curation in Reactome captures protein modifications, translocation events, and complex assembly steps that KEGG pathway diagrams do not represent at the same granularity. A study of baicalein effects on LPS-induced endothelial cell dysfunction used GO, KEGG, and Reactome enrichment analyses and found that differentially expressed genes were primarily enriched in TNF signaling, NF-κB signaling, and immune regulation pathways (PubMed 41783153). The Reactome analysis provided reaction-level detail for the signaling cascades that supported the mechanistic interpretation.

Category C: Disease mechanism and biomarker discovery questions. If the study aims to identify disease-associated pathways, diagnostic biomarkers, or therapeutic targets, both databases should be used in parallel. Disease research benefits from the complementary coverage of the two databases. KEGG provides extensive disease pathway maps, while Reactome provides detailed molecular mechanism annotations. A study of colorectal cancer biomarkers used KEGG pathway enrichment and identified RNA binding and RNA transport as enriched functions among upregulated genes (Scientific Reports). A study of sepsis used integrated single-cell and bulk transcriptomic analyses and identified ubiquitination-related gene networks with TNF signaling as a sepsis-specific pathway (Frontiers in Immunology). Both studies illustrate that disease research questions benefit from the combined coverage of both databases.

Category D: Species-specific or comparative genomics questions. If the study involves non-model organisms, comparative analysis across species, or questions about pathway conservation, KEGG should be the primary database because of its organism-specific pathway maps. A study of differentially expressed genes in the longissimus dorsi muscle of three pig breeds used KEGG pathway enrichment analysis to identify pathways associated with muscle growth (Frontiers in Veterinary Science). The researchers compared two indigenous Chinese pig breeds with an introduced Landrace breed and identified hundreds of differentially expressed genes. The KEGG organism-specific pathway maps allowed the researchers to analyze pathway enrichment in a non-model species. Reactome also provides orthology-based projections to other species, but the coverage is less complete than KEGG for many non-model organisms.

Step 2: Assess the Gene List Composition

The second decision point is to examine the composition of the differentially expressed gene list before running enrichment analysis. This assessment can reveal which database is likely to provide more informative results.

Check the proportion of genes with pathway annotations. Both databases have incomplete annotation of the genome. Many genes are not yet assigned to pathways in either database. If a large proportion of the differentially expressed genes lack pathway annotations in one database, the enrichment results from that database will be less informative. Researchers can check annotation coverage by uploading their gene list to each database and examining the proportion of genes that map to at least one pathway.

Check for genes with known database-specific annotations. Some genes are annotated in one database but not the other. For example, genes involved in specific metabolic reactions may be annotated in KEGG but not in Reactome, while genes involved in specific signaling complexes may be annotated in Reactome but not in KEGG. Researchers can identify these database-specific annotations by comparing the pathway assignments for their gene list across both databases.

Check the direction of gene expression changes. The direction of differential expression matters for interpretation. A study of polarized photobiomodulation on human dermal fibroblasts found that most differentially expressed genes were downregulated in the treatment group (PubMed 36958088). The researchers identified 71 differentially expressed genes, with 10 upregulated and 61 downregulated. The KEGG analysis revealed 21 significantly enriched pathways, and the Reactome analysis revealed 24 significantly enriched pathways. The interpretation of these results required careful consideration of the direction of gene expression changes. If most differentially expressed genes are downregulated, the enrichment results may reflect decreased pathway activity or decreased cellular stress instead of activation of the pathway.

Step 3: Determine the Analysis Strategy

The third decision point is to determine whether to run a single database analysis or a dual database analysis. This decision depends on the research question classification from Step 1 and the gene list assessment from Step 2.

Single database strategy. Use a single database when the research question falls clearly into Category A or Category D, and when the gene list has adequate annotation coverage in the chosen database. For metabolic questions, use KEGG. For species-specific questions, use KEGG. Document the choice and the rationale in the analysis records.

Dual database strategy. Use both databases when the research question falls into Category B or Category C, or when the gene list has substantial annotation coverage in both databases. Run enrichment analysis separately for each database and compare the results. This strategy was used in the candidemia study, which integrated KEGG and Reactome enrichment analyses to predict miRNA targets and identify perturbed pathways (PubMed 41135862). The researchers found that cell-cycle and DNA-replication programs were affected in both Candida albicans and non-albicans species groups, while histidine and phenylalanine metabolism were uniquely affected in C. albicans infections. The dual database strategy allowed the researchers to identify both shared and species-specific pathway perturbations.

Sequential strategy. Use a sequential approach when the research question is exploratory. Start with KEGG to identify broad pathway categories, then use Reactome to examine the specific molecular events within the enriched KEGG pathways. This approach is useful when the research question is not well defined at the outset and the researcher wants to explore the data from multiple angles.

Step 4: Document the Decision and Analysis Parameters

The fourth step is to document the database choice, the rationale, and all analysis parameters in a structured record. This documentation is essential for reproducibility and for interpreting results in the context of database version differences.

Database version documentation. Record the exact database version used for the analysis. Reactome releases are versioned and occur several times per year. KEGG updates are continuous but pathway maps are revised less frequently. The database version can affect enrichment results because content changes between releases. Researchers should record the version number and the date of access.

Software and parameter documentation. Record the software used for enrichment analysis, including the package name, version, and all analysis parameters. The Bioconductor project provides R packages for enrichment analysis, including clusterProfiler and ReactomePA. These packages have specific parameters for statistical methods, multiple testing correction, and gene identifier mapping. All parameters should be recorded in the analysis records.

Gene identifier documentation. Record the gene identifier type used in the analysis. Common gene identifiers include Entrez Gene IDs, Ensembl IDs, and gene symbols. The NCBI provides resources for converting between identifier types and verifying gene identity. Inconsistent or incorrect identifiers are a common source of errors in pathway analysis.

Troubleshooting Discrepant Results Between Databases

When the dual database strategy produces discrepant results, the discrepancies should be investigated systematically instead of ignored. The following troubleshooting method can help researchers understand why a pathway appears enriched in one database but not the other.

Step 1: Examine the overlapping genes. Identify the genes that contribute to the enrichment signal in each database. Determine whether the same genes drive the enrichment in both databases or whether different genes contribute to the enrichment in each database. If different genes drive the enrichment, the discrepancy reflects genuine differences in how the databases annotate the pathway.

Step 2: Check the pathway boundaries. Examine how each database defines the boundaries of the pathway in question. KEGG pathways are defined as graphical maps with specific gene sets. Reactome pathways are defined as hierarchies of reactions with specific molecular participants. The same biological process may be divided into different pathway categories in the two databases. A gene that is assigned to one pathway in KEGG may be assigned to a different pathway in Reactome.

Step 3: Verify the annotation status of the contributing genes. Check whether the genes that contribute to the enrichment signal in one database are annotated in the other database. If a gene is not annotated in one database, it cannot contribute to the enrichment signal in that database. This annotation gap can explain why a pathway appears enriched in one database but not the other.

Step 4: Consider the biological interpretation. After completing the technical investigation, consider the biological interpretation of the discrepant results. The discrepancy may reflect a genuine biological signal that is only captured by one database. The photobiomodulation study provides an example where the two databases returned different but complementary pathway results from the same gene set (PubMed 36958088). The researchers interpreted the differences as reflecting the complementary coverage of the two databases instead of an error in either database.

Common Failure Patterns in Database Selection

Failure pattern 1: Choosing a database based on familiarity instead of research question. Researchers often use the database they have used before, regardless of whether it is appropriate for the current research question. This pattern can lead to missed biological signals. The decision framework in this section provides a structured approach to database selection that reduces the risk of this failure pattern.

Failure pattern 2: Running only one database without justification. Some researchers run only one database because it is the default in their analysis pipeline. This pattern can miss biological signals that are only captured by the other database. The candidemia study demonstrated that integrated KEGG and Reactome analyses identified both shared and species-specific pathway perturbations (PubMed 41135862). A single database analysis would have missed one of these signals.

Failure pattern 3: Ignoring database version differences. Some researchers do not document the database version used for their analysis. This omission makes it impossible to reproduce the analysis or to compare results across studies. The database version should be recorded in the analysis records and reported in publications.

Failure pattern 4: Overinterpreting enrichment results without considering database structure. Some researchers interpret an enriched pathway as evidence that the pathway is activated or inhibited, without considering the database structure or the direction of gene expression changes. An enriched pathway means that the pathway contains more differentially expressed genes than expected by chance. It does not necessarily mean that the pathway is activated or inhibited. The photobiomodulation study provides a cautionary example where most differentially expressed genes were downregulated, and the interpretation required careful consideration of the direction of gene expression changes (PubMed 36958088).

Records and Measurements for Database Selection

Researchers should maintain a structured record of their database selection process and analysis parameters. The following record fields are recommended:

Research question classification. Record the category of the research question (metabolic, signaling, disease, or species-specific) and the rationale for the classification.

Gene list assessment. Record the number of differentially expressed genes, the proportion of genes with pathway annotations in each database, and any database-specific annotations identified.

Database selection decision. Record which database was selected as the primary resource, which was selected as the secondary resource, and the rationale for the decision.

Analysis parameters. Record the database version, software version, gene identifier type, statistical method, multiple testing correction method, and significance threshold.

Discrepancy investigation. If discrepant results were observed between databases, record the investigation steps and the conclusions.

The Carpentries lessons provide training on reproducible research practices, including version control and documentation. These practices are directly applicable to pathway analysis and other bioinformatics workflows. The EMBL-EBI Training program offers courses on pathway analysis and related topics. The Galaxy Training Network provides tutorials that can be followed at the researcher's own pace. The nf-core documentation describes community standards for bioinformatics pipelines, including configuration options for pathway analysis steps.

Professional Escalation Criteria

Researchers should consider seeking expert assistance when they encounter specific challenges in database selection or pathway analysis. These include difficulty classifying the research question into the decision framework categories, uncertainty about the annotation coverage of their gene list in either database, or challenges reconciling discrepant results between databases.

Bioinformatics core facilities can provide expert assistance with analysis design, software configuration, and result interpretation. The Bioconductor support site provides a forum for questions about R packages for pathway analysis. The NCBI provides documentation and support for gene identifier conversion and verification.

Frequently Asked Questions

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

Reactome organizes pathways as a hierarchy of literature-curated reactions, while KEGG organizes pathways as graphical maps with stable identifiers. This structural difference affects the type of results produced by enrichment analysis. Reactome provides more granular reaction-level detail, while KEGG provides comprehensive pathway maps with organism-specific annotations. Studies that use both databases often find complementary results, as demonstrated in the photobiomodulation study where 21 KEGG pathways and 24 Reactome pathways were enriched from the same gene set (PubMed 36958088).

Which database should I use for metabolic pathway analysis?

KEGG is generally the stronger choice for metabolic pathway analysis because its metabolic maps include detailed enzyme, compound, and reaction information. The grape consumption study in mice used KEGG enrichment to identify drug metabolism, glutathione, detoxification, and oxidative stress pathways (PubMed 35804799). The oil dispersant study used KEGG pathway identifiers to report specific metabolic and signaling pathway perturbations (ScienceDirect). For research questions centered on metabolism, KEGG provides the most comprehensive coverage.

Which database should I use for signaling pathway analysis?

Both databases cover signaling pathways, but they represent them differently. KEGG provides standardized pathway diagrams showing major signaling components and their interactions. Reactome provides detailed reaction steps including protein modifications, translocation events, and complex assembly. The choice depends on the level of detail needed. For identifying which signaling pathways are enriched, either database works. For understanding specific molecular events within a signaling pathway, Reactome provides more granular information.

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

Yes, using both databases is often the strongest approach. The candidemia study used integrated KEGG and Reactome enrichment analyses to predict miRNA targets and identify perturbed pathways (PubMed 41135862). The baicalein study used GO, KEGG, and Reactome enrichment analyses to investigate endothelial cell dysfunction (PubMed 41783153). Using both databases provides complementary perspectives and reduces the risk of missing biological signals that only one database captures.

How do database updates affect my enrichment results?

Database updates can change enrichment results because both Reactome and KEGG modify their content over time. Reactome releases are versioned and occur several times per year. KEGG updates are continuous but pathway maps are revised less frequently. Researchers should document the database version in their methods and report it in publications. This documentation is essential for reproducibility.

What gene identifiers should I use for pathway enrichment analysis?

Common gene identifiers including Entrez Gene IDs, Ensembl IDs, and gene symbols are accepted by most enrichment tools. The NCBI provides resources for converting between identifier types and verifying gene identity. Researchers should verify that their gene list uses consistent identifiers before running enrichment analysis. Inconsistent or incorrect identifiers are a common source of errors.

How should I interpret enrichment results from Reactome and KEGG?

Enrichment results should be interpreted with attention to the database structure and the biological context. An enriched pathway means that the pathway contains more differentially expressed genes than expected by chance. It does not necessarily mean that the pathway is activated or inhibited. Researchers should consider the direction of gene expression changes and compare results across databases to identify consistent biological signals.

What should I do if my enrichment results differ between Reactome and KEGG?

Differences between Reactome and KEGG enrichment results are expected and reflect the content and structural differences between the databases. Researchers should investigate the discrepancies by examining the specific genes that contribute to enrichment in each database. The comparison can reveal biological insights that would be missed by using a single database. The photobiomodulation study provides an example where the two databases returned different but complementary pathway results from the same gene set (PubMed 36958088).

Related Bioinformatics Guides

• RNA-Seq Databases: Accessing and Using Public RNA-Seq Data
• RNA-Seq Data Analysis in Galaxy: A User-Friendly Platform
• RNA-Seq Data Analysis Workflow: From Raw Reads to Insights
• Pathway Enrichment Analysis for Proteomics: Tools and Interpretation
• Genomic Data Analysis Tools: A Comparative Guide for Researchers

Related Clinical & Scientific Guides

• A Practical Guide to Detecting Antimicrobial Resistance Genes in Shotgun Metagenomic Data
• Computational Immunology: Modeling the Immune System
• How to Set Hard Filters for Germline Variant Calling: A Practical Guide to GATK Best Practices

References and Further Reading

• NCBI Data Resources. National Center for Biotechnology Information.
• EMBL-EBI Training. European Bioinformatics Institute.
• Bioconductor. Bioconductor Project.
• Galaxy Training Network. Galaxy Project.
• nf-core Documentation. nf-core.
• The Carpentries Lessons. The Carpentries.
• MicroRNA whole-blood profiling in hospitalized patients with candidemia identified miR-125a-5p and miR-99b-5p as potential biomarkers for Candida albicans bloodstream infection.. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2025.
• Transcriptome analysis of the effects of polarized photobiomodulation on human dermal fibroblasts.. Journal of photochemistry and photobiology. B, Biology, 2023.
• Consumption of Grapes Modulates Gene Expression, Reduces Non-Alcoholic Fatty Liver Disease, and Extends Longevity in Female C57BL/6J Mice Provided with a High-Fat Western-Pattern Diet.. Foods (Basel, Switzerland), 2022.
• Bioinformatic Analysis and Experimental Verification Reveal the Protective Role of Baicalein Against LPS-Induced Endothelial Cell Dysfunction.. Journal of inflammation research, 2026.
• Nuclear Transfer Perturbs Genomic Balance.. 2026.
• Time-course ATAC-seq and RNA-seq analysis of porcine synovium-derived mesenchymal stem cells under in vitro osteogenic induction.. 2026.
• Metabolic reprogramming of glutamine is associated with M2 macrophage polarization in allergic rhinitis.. 2026.
• Integrated single-cell and bulk transcriptomic analyses reveal cDC1-centered ubiquitination dysregulation and identify UBE2F as a critical regulator in sepsis.. 2026.
• Discovery and validation of a robust 11-gene prognostic signature via integrative multi-omics profiling in VD-CAG-treated non-M3 AML.. 2026.
• Comparison of differentially expressed genes in longissimus dorsi muscle of Diannan small ears, Wujin and landrace pigs using RNA-seq. Frontiers in Veterinary Science, 2024.
• Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation. Scientific Reports, 2023.
• Integrated analysis of ATAC-seq and RNA-seq reveals the transcriptional regulation network in SLE.. International Immunopharmacology, 2023.
• Carcinogenic Effects of Oil Dispersants: a KEGG Pathway-based RNA-seq Study of Human Airway Epithelial Cells. Gene, 2016.
• Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling. Scientific Reports, 2021.
• RNA-Seq-based transcriptomics analysis during the photodynamic therapy of primary cells in secondary hyperparathyroidism. Photochemical and Photobiological Sciences, 2023.
• Analysis of differentially expressed genes in Verruca vulgaris vs. adjacent normal skin by RNA-sequencing. Biocell, 2023.

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