🧬 Promoter Analysis Toolkit

Extraction Β· Core-promoter analysis Β· TF-binding site prediction Β· Transcription-factor identification

πŸ“˜ New to promoter analysis? Read the tutorial to interpret motifs and TF predictions correctly.
Example (with embedded TATA boxes): CGCGTATATAAAGGCGGGGCTGCGGGCGGGCGGCCATTGGGCGGGATCCGGCTATAAAAGGCGCGCCACGTGTCACGTGACGTGCCGACGCGCCGACGGCCATTGCGGCTAGCTAGCTAGCTGCATGCATGCTAGCTAGCATCGATCGATATAATATAACCGGGCCCGGGCCCGGGCTTGACATGCATGCATGCAGCTAGCATCGATCGATCGATCGATCGATCGATCGATCGTACGATCGATCGATCGTAGCTAGCTAGCTAGCTAGCATCGATCGTAGCTAGCTAGCTAGC

🧬 1. What is a promoter?

A promoter is a DNA region β€” usually located upstream of a gene β€” where transcription is initiated. It contains:

  • Core promoter (~40 bp around the TSS) β€” binds RNA polymerase II and general TFs.
  • Proximal promoter (~250 bp upstream) β€” contains primary regulatory elements.
  • Distal enhancers / silencers (up to several kb away) β€” modulate expression in a tissue- and time-specific manner.
Key idea: Promoter activity is determined by the combinatorial binding of transcription factors (TFs) to short DNA motifs.

βœ‚ 2. Promoter Extraction β€” How and How Much?

You need to define which region to analyse. Common choices:

RegionTypical sizeWhen to use
Core promoterβˆ’50 to +50 bpStudying TSS, TATA box, Inr
Proximal promoterβˆ’500 to +100 bpStandard for most studies
Extended promoterβˆ’1,000 to +100 bpDefault in PlantCARE / PLACE
Long-rangeβˆ’2,000 to βˆ’3,000 bpEnhancer / silencer discovery

How to find the TSS

  • From annotation β€” use NCBI / Ensembl / Phytozome gene models (5' UTR start).
  • From experimental data β€” CAGE-seq, TSS-seq, or 5' RACE.
  • Predicted β€” look for a TATA box approximately 25–35 bp upstream.
Warning: If you extract the wrong strand or the wrong TSS, all downstream motif predictions will be wrong. Always verify the orientation of your gene.

πŸ”¬ 3. Core Promoter Elements β€” Interpretation

ElementConsensusPositionFunction
TATA boxTATAWAWβˆ’25 to βˆ’35Binds TBP; positions RNA Pol II. Presence β†’ focused TSS.
CAAT boxGGVCAATCTβˆ’70 to βˆ’80Enhances transcription; binds NF-Y / CBF.
GC boxGGGCGGVariableBinds Sp1; often in housekeeping genes.
Inr (Initiator)YYANWYYOverlaps TSSPositions the TSS precisely.
BREGSGCGβˆ’35 to βˆ’40Binds TFIIB; stabilises preinitiation complex.
DPERGWCGTG+25 to +30Binds TFIID; common in TATA-less promoters.

What to look for in results

  • Presence of a TATA box β€” indicates a regulated, tissue-specific promoter.
  • Absence of TATA box + presence of GC box / CpG island β€” housekeeping or broadly expressed gene.
  • Multiple CAAT boxes β€” strong, constitutive expression.
  • BRE / Inr clusters β€” precise TSS usage.

🎯 4. TF-Binding Site Prediction β€” Reading the Output

Each row in the TF-binding site table represents a predicted binding event for a transcription factor whose consensus motif matches the sequence.

Motif families included in this tool

MotifConsensusTFRole
ABREACGTGbZIP (ABF/AREB)ABA signalling, drought
DRE/CRTRCCGACDREB/CBFCold & dehydration response
MYBYAACKGMYBSecondary metabolism, stress
MYCCANNTGMYC (bHLH)JA signalling, development
G-boxCACGTGbZIP / bHLHLight, ABA, stress
W-boxTTGACWRKYPathogen defence, senescence
GT elementGRWAAWGT-1Light-responsive
I-boxGATAAGGATA / bZIPLight, nitrate
LTRCCGAAAICE1 / CBFCold response
HSEAGAAYHSFHeat-shock response
CArG boxCCWGGMADSFloral / developmental
AuxRRTGTCTCARFAuxin response
GARETAACRRAGAMYBGA response (seed)
E-boxCANNTGbHLHDevelopment, JA
SORLIPGCCACHY5 / bZIPLight signalling
Skn-1GTCATWRKY / DOFEndosperm expression
DOF coreAAAGDOFSeed, tissue-specific
NAC BSCATGTGNACStress, development
TGACGTGACGTGA (bZIP)SA / MeJA signalling
CGTCACGTCAbHLH / MYCMeJA signalling

Reading the results table

  • Motif β€” the element name (e.g. "W-box").
  • TF / Family β€” the predicted binding factor(s).
  • Position β€” 1-based coordinate in your input sequence.
  • Strand β€” "+" means the motif matches the forward strand; "βˆ’" means the reverse complement.
  • Match β€” the actual sequence bound.

What the summary tables mean

ObservationInterpretation
Many W-boxesLikely pathogen / defence responsive.
Multiple ABREs + DREsABA- and drought-responsive promoter.
Many G-boxes + GT elementsLight-regulated promoter.
Cluster of DOF / Skn-1Seed / endosperm-specific expression.
Mixed hormone motifs (ABRE, TGACG, CGTCA)Broad stress-responsive promoter.
Remember: These predictions are computational. A predicted TF-binding site does not prove that the TF binds in vivo. Validate with EMSA, ChIP-qPCR, or yeast one-hybrid assays.

πŸ”Ž 5. TF Identification β€” Confidence & Validation

The "TF identification" tab lists the actual transcription factors whose binding motifs appear in your sequence. Each hit is given a confidence level:

ConfidenceCriteriaRecommended action
HighMotif β‰₯ 6 nt with an exact consensusPrioritise for EMSA / ChIP.
MediumMotif β‰₯ 6 nt with degeneracyValidate with complementary tools.
LowShort or highly degenerate motifTreat as a weak hypothesis.

Recommended validation experiments

  • EMSA / gel shift β€” confirms TF–DNA binding in vitro.
  • ChIP-qPCR β€” confirms TF–DNA binding in vivo.
  • Yeast one-hybrid (Y1H) β€” screens cDNA library for binders.
  • Dual-LUC reporter assay β€” tests whether the promoter drives expression and whether the TF activates or represses it.
  • Mutagenesis β€” mutate the predicted motif; if activity changes, the site is functional.
Best practice: Combine in silico prediction (this tool) with in vitro (EMSA) and in vivo (ChIP, dual-LUC) validation before drawing conclusions.

🏝 6. CpG Islands & Methylation

A CpG island is a genomic region β‰₯ 200 bp with:

  • GC content β‰₯ 50%, and
  • observed/expected CpG ratio β‰₯ 0.6.

Why they matter

  • ~70% of human promoters are associated with CpG islands.
  • Unmethylated CpG islands β†’ active, housekeeping-type promoters.
  • Methylated CpG islands β†’ silenced genes.
  • In plants, CpG methylation also regulates transposons and imprinting.
Interpretation: A CpG island overlapping the TSS region is a strong indicator of a broadly expressed gene with multiple TSSs.

πŸš€ 7. Workflow & Pitfalls

Recommended workflow

  1. Retrieve the gene + upstream sequence (NCBI, Ensembl, Phytozome).
  2. Identify the TSS (annotation or TATA-box prediction).
  3. Extract βˆ’1,000 to +100 bp (Tab 1).
  4. Analyse core elements and CpG islands (Tab 2).
  5. Predict TF-binding sites (Tab 3).
  6. List candidate TFs (Tab 4) β€” sort by confidence.
  7. Cross-validate with PlantCARE / PLACE / JASPAR / MEME.
  8. Validate experimentally (EMSA, ChIP, dual-LUC).

Common pitfalls

Wrong strand: Extracting the reverse-complement of a gene gives meaningless motifs. Always verify orientation.
Short motifs: A 4–5 nt motif occurs by chance every ~256–1,024 bp. Don't over-interpret single hits.
Species specificity: Motifs trained on human/yeast may not hold in plants. Use plant-specific databases.
Motif β‰  function: A predicted binding site can be silent in vivo due to chromatin state, methylation, or competing factors.
Best practice: Combine multiple tools (this one + PlantCARE + JASPAR + MEME), and always confirm at least one prediction experimentally.

Recommended external resources

  • PlantCARE β€” plant cis-acting regulatory elements
  • PLACE β€” plant DNA motifs database
  • JASPAR β€” open-access TF-binding profiles
  • MEME Suite β€” motif discovery & scanning
  • PlantTFDB β€” plant TF classification
  • NCBI / Ensembl / Phytozome β€” sequence retrieval
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