Overview
Pathway-Analyst: One-click proteomic knowledge generation
Pathway-Analyst is an easy-to-use web application developed to perform pathway enrichment analysis on the differential protein/RNA expression data.
Mainly to do the final step of each of analyst suites tool like LFQ-Analyst, Phospho-Analyst, FragPipe-Analyst or TMT-Analyst, but also can take the RNAseq gene expression output from Degust.
; ?>)
Sidebar tabs
- Analysis: perform your own analysis
User Guide
Pathway-Analyst User Guide
Pathway-Analyst is a one-click pathway enrichment tool built to pick up exactly where the Analyst-Suites tools (LFQ-Analyst, Phospho-Analyst, FragPipe-Analyst, TMT-Analyst, DIA-Analyst) leave off, and it also accepts RNA-seq output from Degust, or a plain list of genes/proteins with no statistics at all.
Upload your result file, click Start Analysis, and explore over-representation, gene set enrichment, and protein-protein interaction results side by side — no coding required.
1. Choose your species and data type
In the Analysis tab of the sidebar, start with two dropdowns:
- Species – Pathway-Analyst currently supports 19 organisms, including Human, Mouse, Rat, Zebrafish, Fruit Fly, Yeast, C. elegans, Pig, Chicken, Cattle, Dog, and several others. Pick the species your data comes from — this determines which gene annotation database is used everywhere downstream.
- Data Type – tells the app what kind of file you're uploading:
- Analyst Suites – a differential expression table exported from LFQ-Analyst, Phospho-Analyst, FragPipe-Analyst, TMT-Analyst, or DIA-Analyst. Needs protein IDs and a fold change/p-value per comparison.
- Degust – an RNA-seq differential expression table exported from Degust. When exporting from Degust, keep only two conditions selected per comparison.
- Gene/Protein List – just a plain list of genes or proteins you're already interested in — no fold change or p-values needed. Useful when you already have a curated hit list from elsewhere. Optionally upload a second file as a custom background set. This option feeds ORA, g:Profiler and STRING, but not GSEA, since GSEA needs a ranked list to work with.
2. Upload your file
Once a data type is selected, an upload box appears with an example file you can download to see the expected format. A few pointers:
- Analyst Suites / Degust: after upload, a Comparisons dropdown appears listing every contrast found in your file (e.g. Treatment_vs_Control) — pick the one you want to analyse. For Degust files, you'll also confirm which columns hold the gene ID, gene name, and fold change.
- Gene/Protein List: upload your list (one ID per line), and optionally a background list. IDs can be UniProt, gene symbol, Entrez, or Ensembl — the app auto-detects the type, and a short summary shows how many IDs were successfully recognised.
3. Set your options and run the analysis
Under Global Options, set:
- Log2 fold change cutoff – the minimum absolute fold change (log2) for a gene to count as significant.
- Adjusted p-value cutoff – the significance threshold (FDR/adjusted p-value).
(These two don't apply to the Gene/Protein List data type, since that input has no statistics to filter on.)
Once everything looks right, click Start Analysis. The results dashboard will appear with several tabs, described below.
Most analysis tabs let you choose a Background:
- Local (Research-level) – tests your significant genes against only the genes actually detected/quantified in your own experiment.
- Universal (Species-level) – tests against the whole genome for that species.
These two settings can give genuinely different results, and neither is "more correct" — they answer different questions. Local background is usually the more appropriate choice for proteomics data, since your instrument only ever detects a subset of all proteins, and comparing against the whole genome can make enrichment look stronger than it really is relative to what you could have detected. If a database (e.g. Reactome) shows no results under Local, try switching to Universal before assuming something is wrong.
4. Understanding your results
ORA — GO / KEGG / Reactome
Asks: "are my significant genes over-represented in any known pathway or function, more than you'd expect by chance?" Choose a Database — Gene Ontology (GO) terms, KEGG pathways (with a pathway diagram drawing your fold-change data onto the official map), Reactome pathways, or DOSE disease-gene associations. Results: Dot plot, Bar plot, Gene-term network, Tree plot, and a full Enrichment table.
ORA — g:Profiler
A second, independent take on over-representation analysis using the g:Profiler web tool, shown as a Manhattan plot across multiple databases at once, with a link to open the same query directly on the g:Profiler website.
Gene Set Enrichment Analysis (GSEA)
Unlike ORA, GSEA needs no significance cutoff — it uses every gene's fold change to see whether a gene set is skewed toward the top or bottom of your entire ranked list. Choose an MSigDB Category/Subcategory, then select table rows to draw multi-set, Jaccard, or leading-edge correlation views.
STRING Network
Maps your significant genes/proteins onto STRING to visualise known and predicted protein-protein interactions. Adjust confidence threshold, node count, network view, and clustering method — each cluster can be inspected individually, with a link to open the same network on the STRING website and a full interaction table underneath.
5. Downloading plots and tables
Every plot has its own download controls — choose a file format (PNG/SVG/etc.), image size, and click Download plot. Every results table has its own Download button () to export the full table as CSV.
6. Tips & troubleshooting
"No enriched terms were found for the selected filters"
A message about identifier/BioMart annotation
GSEA or STRING seem slow to load
Switching species, cutoffs, or the comparison after running once
Questions, bugs, or feature requests? Get in touch with the Pathway-Analyst team.
Getting Started
Quick Start
- Select organism and data type in the sidebar.
- Upload your result file and confirm the contrast/columns.
- Set global cutoffs and click Start Analysis.
- Explore ORA, GSEA and STRING Network results from the tabs.
-
For differential Protein expression analysis, select
Analyst-Suites
under
Data Type
and upload result file generated by any tool from
Analyst-Suites
, Including:
-
For differential RNAseq expression analysis, select
Degust
under
Data Type
and upload RNA-seq result file from
Degust
.
Note: when downloading csv file from degust, make sure only two conditions are selected per analysis.
You also need to properly identify which column is the gene name under Select Gene Name column .
ORA: GO / KEGG / Reactome
Explore over-represented biological pathways and functional terms from significant genes.
- Use the options panel to choose database, regulation direction and background.
- If no terms are returned, relax cutoffs or check identifier mapping.
Enrichment settings
Enrichment table
KEGG Pathway by Pathview
What is a KEGG pathway plot?
The pathway plot depicts a KEGG pathway graph, which has been processed using pathview to incorporate user data.
This combination results in a visual representation where the user's data is integrated into the pathway graph. This visualization effectively portrays the chronological series of molecular occurrences and intricate interactions that constitute a specific biological process.
ORA: g:Profiler
Run g:Profiler enrichment and open matching results in the g:Profiler web interface.
- Use local background for uploaded/mapped genes or universal background for broad exploration.
- Network/database connection issues are shown as red messages instead of plot failures.
g:Profiler settings
How to read the g:Profiler plot
The Manhattan plot groups enriched terms by source such as GO, KEGG, Reactome and WikiPathways. More significant terms appear higher on the plot.
Use the g:Profiler link in the settings panel to inspect the same result in the source web interface.
How to read the table
The table contains the term name, source, size and adjusted significance values for each enriched term.
g:Profiler Table
Gene Set Enrichment Analysis (GSEA)
Explore enrichment across a ranked gene list using MSigDB categories and subcategories.
- Choose a category first; categories with subcategories wait until a valid subcategory is selected.
- Select rows in the GSEA table before drawing multi-set, Jaccard or heatmap views.
GSEA settings
How to read GSEA plots
The GSEA plot shows where genes from a selected set appear across the ranked list and how the enrichment score changes along that ranking.
Select two or more table rows before using multi-set, Jaccard or leading-edge correlation views.
GSEA results table
STRING Network
Map significant genes/proteins to STRING and inspect interaction networks and clusters.
- Higher required scores show more confident but usually smaller networks.
- If STRING cannot download or map identifiers, check network access and try again later.