End‑to‑end multi‑omics workflow – upload, integrate, and analyse genomics, transcriptomics, epigenomics, proteomics and metabolomics

Modern omics technologies routinely deliver long lists of differentially expressed genes, methylation changes, or protein abundance shifts. However, these downstream readouts do not immediately explain which signaling cascades are responsible for the observed changes, or which upstream regulators should be targeted to modulate the system.

The upstream of TFs concept closes this gap. Starting from transcription factors (TFs) inferred from promoter/enhancer analysis, we trace the regulatory architecture backwards through signaling networks to the receptors, kinases, and other key nodes that drive these TFs. This reveals causal links between:

disease- or phenotype-specific gene expression signatures,
the transcription factors controlling those signatures, and
the signaling pathways and master regulators that act upstream of these TFs.

This “from TFs upstream to signaling” strategy is at the core of geneXplain’s pathway-centric analysis and provides a powerful bridge between regulatory genomics and systems-level signaling biology.

From gene expression changes to signaling cascades

Typical omics workflows start with one or more of the following layers:

Transcriptomics – differentially expressed genes (DEGs) describing the phenotype or pathology.
Epigenomics – changes in regulatory regions (e.g. ChIP-seq peaks, DNA methylation) that impact TF binding.
Genomics – variants in promoters, enhancers, or coding regions that affect regulation.
Proteomics / phosphoproteomics – altered abundance or phosphorylation of signaling proteins and TFs.
Metabolomics – end-point readouts of pathway activity that help to focus on the most functionally critical DEGs.

By integrating these layers, we obtain a robust gene set characterizing the condition of interest. The key question then becomes:

Which transcription factors and upstream signaling cascades are responsible for this gene set?

Answering this question requires connecting regulatory regions (where TFs act) with signaling pathways (where extracellular and intracellular signals are processed).

Key steps in upstream-of-TF analysis

Step 1 – Constructing the gene set describing the process

The analysis begins by constructing a gene set that best represents the studied biological process or pathology:

From transcriptomics, differential expression or clustering identifies up- and down-regulated genes.
From epigenomics, peak-to-gene or CpG-to-gene mapping connects regulatory changes to nearby genes.
From proteomics, protein identifiers are converted to corresponding genes.
From genomics, variants are mapped to affected genes, especially when they fall into regulatory regions.

The result is a set (or several sets) of genes that show coordinated behavior in the condition under study and serve as the entry point for regulatory and pathway analysis.

Step 2 – Promoter/enhancer analysis and TF identification

For each gene set, promoter and/or enhancer regions are analyzed to detect overrepresented transcription factor binding sites (TFBSs):

Regulatory regions are defined (e.g. ±1 kb around transcription start sites, or known enhancer coordinates).
These regions are scanned with high-quality TF binding models to detect enriched motifs.
Complexes and modules of co-acting TFs (combinatorial regulation) are identified to reflect realistic regulatory logic.

This step transforms a “flat” gene list into a regulatory profile: a small set of transcription factors and TF modules that are statistically most likely to control the observed gene expression changes.

Step 3 – Mapping TFs onto signaling networks

Once candidate TFs are identified, the analysis proceeds upstream into signaling space:

TFs are mapped onto a curated signaling and metabolic network.
Known regulatory relationships (e.g. which kinases activate a TF, which receptors feed into a pathway that converges on that TF) are retrieved.
Upstream interaction chains are reconstructed, linking TFs to receptors, kinases, phosphatases, adaptor proteins, and other signaling molecules.

This step builds signal transduction routes that explain how external or internal cues propagate through the network and converge on the TFs controlling the gene set.

Step 4 – Upstream network reconstruction and master regulators

The next goal is to identify master regulators – those key upstream nodes whose activity can influence large parts of the network:

Network algorithms are applied to trace shortest or most plausible signaling paths from TFs back to upstream nodes.
Nodes are ranked by criteria such as connectivity, frequency of occurrence across reconstructed paths, and their position in the hierarchy (e.g. receptors vs. downstream kinases).
The result is a compact set of candidate master regulators (receptors, kinases, TFs, other signaling proteins) that function as control points for the observed transcriptional program.

These master regulators provide a mechanistic explanation of the phenotype and serve as high-value hypotheses for further experimental validation.

Step 5 – Pathway and process interpretation

With TFs and upstream regulators defined, the analysis moves to biological interpretation:

The identified regulators and their downstream targets are mapped to signaling and metabolic pathways.
Enrichment analysis highlights pathways and processes most consistently affected in the condition.
Integrated visualization shows how signals flow from receptors, through signaling cascades, to TFs and finally to target genes.

This provides a systems-level view that connects:

the original omics readouts (e.g. DEGs),
the TFs inferred from regulatory region analysis, and
the upstream signaling modules that explain those TF activities.

Multi-omics context for upstream-of-TF analysis

The upstream-of-TF method becomes particularly powerful when combined with multiple omics layers:

Epigenomics refines which promoters/enhancers are truly active, improving TF inference.
Genomics helps to identify regulatory mutations that alter TF binding or signaling components.
Proteomics/phosphoproteomics confirm activation of key signaling proteins and TFs.
Metabolomics points to pathways where output changes are most critical, helping to prioritize regulators.

By integrating these data types, we not only predict which TFs and upstream nodes could be important, but also obtain independent evidence that they are active and relevant in the studied system.

What you gain from upstream-of-TF analysis

Applying the upstream-of-TF approach allows you to:

Move from long lists of altered genes to a small set of mechanistically justified regulators.
Link transcription factor activity directly to concrete signaling cascades and pathways.
Identify master regulators that act as bottlenecks or control points in the network.
Generate focused, experimentally testable hypotheses on how to modulate the system (e.g. by targeting specific receptors, kinases, or TFs).
Use multi-omics evidence to prioritize the most robust and biologically relevant regulators.

This upstream-of-TF strategy is implemented in geneXplain’s analysis environment, where promoter/enhancer analysis, TF motif enrichment, and signaling-network reconstruction are integrated into a coherent workflow, providing a clear mechanistic bridge from transcription factors to signaling cascades.