Because GPSAdb contains 6,245 perturbation datasets with pre-computed differential-expression analyses and GSEA results, it can be used to back-trace the upstream regulators of a single gene or an entire gene set.

Querying Upstream Regulators of a Single Gene

Taking TP53 as an example, the Query module instantly returns all perturbations that significantly change TP53 expression.

For every “perturbed gene A → target gene TP53” pair, GPSAdb provides a ready-made expression plot that visualises the magnitude and direction of the change.

Querying Upstream Regulators of a Gene Set

If you want to discover which genes regulate a specific pathway or signature, eight built-in gene-set libraries are available.
This is a unique feature of GPSAdb: thanks to the large number of high-quality perturbation datasets, it can reliably identify upstream regulators for entire gene sets—an otherwise difficult task.

Custom Gene Sets

Need to analyse a user-defined gene set instead of our built-ins?
Simply switch to the BioTrigger page and upload your own list.

If you have a gene set of interest and want to identify which perturbations modulate the activity of that set, the BioTrigger module is designed for exactly this task.

Data Input Options

BioTrigger accepts two convenient input methods:

1. Copy & Paste
   Simply paste a newline-separated list of gene symbols into the text box.

   

2. File Upload
   Upload a one-column table (CSV/TSV) that contains your gene list.
   Make sure the file has a header—e.g. a column named geneSet.

   

Running BioTrigger

Click Run BioTrigger and wait ~20 seconds while the analysis is executed.
A new results interface will then appear.

• RankPlot displays the top-ranked genes whose perturbation most strongly affects your uploaded gene set.
• Select any row in the table to focus on a specific dataset, then click Plot to visualise:
  • an updated RankPlot limited to that gene (multiple datasets are shown if available), and
  • a GSEA plot illustrating how your gene set is enriched within the chosen dataset.

When you knock down a gene in a cell line—or treat the cells with a drug—you often want to know which other genes mediate the observed effect. Standard GSEA is not designed for this task.
fastGPSA fills this gap by finding perturbations whose transcriptional signatures resemble your own experiment.

How fastGPSA Works ⚙️

1. Input Upload the differential-expression results of your experiment (typically produced by DESeq2 or edgeR).
2. Gene-set construction fastGPSA automatically takes the top 150 up-regulated genes and the top 150 down-regulated genes and treats them as two custom gene sets.
3. Signature matching These two gene sets are then queried—via GSEA—against all 6,245 perturbation profiles in GPSAdb.
4. Ranking Perturbations that yield similar enrichment patterns are ranked at the top, revealing genes whose knock-out/knock-down produce effects most similar to yours.

Data Input

If your data can be used for GSEA, it can be used for fastGPSA—the required input is the same: an ordered geneList.

1. Copy & Paste

Paste two columns of data:
gene and ranking metric (usually log₂ FC).

2. File Upload

Upload a two-column table in CSV/TSV format.

Tip The table downloaded from the Browse page can be used here without any modification.

Running fastGPSA

Click Run fastGPSA. A notification will appear; the analysis takes 30–60 seconds.
You can freely explore other GPSAdb functions while you wait.

Result Interface

When the run finishes, a new dashboard opens:

• Left panel Top-ranked genes whose perturbation mimics your signature.
• Right panel Full list of genes that pass the similarity threshold.

Select any dataset you are interested in and click the green Details button.

Visualisations

• Rank Plot Shows where the selected gene(s) sit in the similarity ranking—multiple datasets for the same gene are displayed together.

• GPSA Plot Overlays the enrichment curves for your up and down gene sets within the selected dataset (e.g. LATS1 knock-out in MCF-7).

  • Green = genes up-regulated in both your data and the reference dataset.
  • Purple = genes down-regulated in both.

  This indicates a high resemblance between your perturbation and the reference (LATS1 KO), suggesting a potential regulatory relationship between LATS1 and ESR1.

• FuncPlot Compares GSEA results for the 50 Hallmark pathways between your dataset and the selected GPSAdb dataset.

  • Red dots = pathways enriched in both.
  • Grey dots = the remaining Hallmark pathways.

fastGPSA therefore gives you a brand-new way to dissect your data and uncover biologically meaningful connections. Whenever you can run GSEA, you should also try fastGPSA!