Promoters & Terminators
Bacterial
SAPPHIRE
SAPPHIRE
Sequence Analyser for the Prediction of Prokaryote Homology Inferred
Regulatory Elements - is a neural network based classifier for σ70
promoter prediction in Pseudomonas
(Reference: Coppens L & Lavigne R (2020) BMC
Bioinformatics 21(1): 415).
PromoterHunter
PromoterHunter
- is part of phiSITE database which is a collection of phage gene
regulatory elements, genes, genomes and other related information, plus
tools.
(Reference: Klucar, L. et al. 2010. Nucleic Acids
Res. 38(Database Issue): D366-D370).
PhagePromoter
PhagePromoter
- is a tool for locating promoters in phage genomes, using machine
learning methods. This is the first online tool for predicting promoters
that uses phage promoter data and the first to identify both host and
phage promoters with different motifs. It is part of Galaxy.
(Reference: Sampaio M et al. (2019) Bioinformatics.
35(24): 5301-5302).
BacPP
BacPP: Bacterial Promoter Prediction - A tool for accurate
sigma-factor specific assignment in enterobacteria. Includes σ24, σ28,
σ32, σ38, σ54 and σ70 with 84-97% accuracy. Requires registration.
(Reference: S. de Avila e Silva et al. J. Theor.
Biol., 287 (2011): 92–99).
Sigma70Pred
Sigma70Pred is a web-server with the capability of identifying σ70 promoters - their SVM model, based on Dinucleotide Auto-Correlation, Dinucleotide Cross-Correlation, Dinucleotide Auto Cross-Correlation, Moran Auto-Correlation, Normalized Moreau-Broto Auto-Correlation, Parallel Correlation Pseudo Tri-Nucleotide Composition, etc., achieved a maximum accuracy 97.38%
iPro70-PseZN
iPro70-PseZNC - is
a tool for identifying σ70 promoters with novel pseudo nucleotide
composition
(Reference: Lai H-Y et al. (2019) Mol Ther Nucleic
Acids. 17: 337-346). I would recommend using <100 nt upstream from the
start codon.
iPro54-PseKNC
iPro54-PseKNC - is
a sequence-based predictor for identifying sigma-54 promoters in
prokaryote with pseudo k-tuple nucleotide composition.
(Reference: Lin H et al. (2014) Nucleic Acids Res
42(21): 12961-12972).
CNNPromoter_b
CNNPromoter_b
- Prediction of Bacterial Promoters by CNN models in genomic sequences.
(Reference: Umarov RK, & Solovyev VV (2017) PLoS
One. 12(2): e0171410).
Deep Learning Recognition using Convolutional Neural Networks
Deep Learning Recognition using Convolutional Neural Networks
(CNNPromoter & CNNProm) - Classification of Prokaryotic and Eukaryotic
Promoters and non-promoter sequences
(Reference: Umarov R.K & Solovyev V.V. (2017) PLoS
One.12(2): :e0171410).
Virtual Footprint
Virtual Footprint - offers two
types of analyses (a) Regulon Analysis - analysis of a whole prokaryotic
genome with one regulator pattern and (b) Promoter analysis - Analysis of
a promoter region with several regulator patterns
(Reference: R. Münch et al. 2005. Bioinformatics
2005 21: 4187-4189).
PePPER
PePPER
(University of Groningen, The Netherlands) is a webserver for prediction
of prokaryote promoter elements and regulons
(Reference: de Yong, A. et al. 2012. BMC Genomics
13:299).
ProPr54
ProPr54 - prediction of Sigma54 dependent Promoters and Regulon - offers the choice of an annotated bacteria genome or short sequences (Tristan Achterberg & Anne de Jong, University of Groningen, the Netherlands).
DOOR3
DOOR3 - Database of prOkaryotic OpeRons - offers
high-performance web service for online operon prediction on
user-provided genomic sequences; and, an intuitive genome browser to
support visualization of user-selected data. Plus a huge database of
transcriptional units.
(Reference: X. Mao et al. 2014. Nucleic Acids Res.
42(Database issue): D654-9).
MLDSPP
MLDSPP (Machine
Learning and Duplex Stability based Promoter prediction in Prokaryotes)
is another promoter prediction tool.
(Reference: Paul S et al. (2024) J Chem Inf Model.
64(7): 2705-2719).
Eukaryotic
Not being a eukaryotic molecular biologist I cannot comment on utility and accuracy of the following promoter- prediction programs (see EPD).
FindM
FindM (Find Motifs around Functional Sites) - choose Promoter Motifs from Motif Library
CNNPromoter_e
CNNPromoter_e
- Prediction of Eukaryotic Promoters by CNN models in genomic sequences.
(Reference: Umarov RK, & Solovyev VV (2017) PLoS
One. 12(2): e0171410).
YAPP
YAPP Eukaryotic Core Promoter Predictor - is a tool to scan for canonical core promoter elements - BREs, TATA boxes, INRs and DPEs, and synergistics combinations of these elements (more). The search results may be restricted to elements which lie within the functional range of a specified TSS.
Promoter2.0
Promoter2.0
- predicts transcription start sites of vertebrate PolII promoters in DNA
sequences. It has been developed as an evolution of simulated
transcription factors that interact with sequences in promoter regions. i
It builds on principles that are common to neural networks and genetic
algorithms.
(Reference: Knudsen S (1999) Bioinformatics 15:
356-361).
iProEP
iProEP - was developed
for five species: Homo sapiens, Drosophila melanogaster, Caenorhabditis
elegans, Bacillus subtilis, and Escherichia coli. The PseKNC and PCSF
features were employed to formulate promoter samples. We adopted mRMR
algorithm and IFS method to find out the optimal feature subsets. SVM
algorithm was used to implement classification. 10-fold cross-validated
results showed that the accuracies for H. sapiens, D. melanogaster, C.
elegans, B. subtilis, and E. coli were respectively 93.3%, 93.9%, 95.7%,
95.2%, and 93.1%
(Reference: Lai H-Y et al. (2019) Mol Ther Nucleic
Acids 17: 337–346)
Transcriptional terminators
these only apply to rho-independent terminators; for rho-dependent termiantor sites see
Transcription Terminator Prediction
Transcription Terminator Prediction (Anne de Jong, University of Groningem, The Netherlands) - is part of the excellent Genome2D webserver for Analysis and Visualization of Bacterial Genomes and Transcriptomes
ARNold
ARNold
- finds rho-independent terminators in nucleic acid sequences using two
complementary programs, Erpin and RNAmotif. The program colors the
terminator stem and loop
(Reference: Gautheret D, Lambert A. 2001. J Mol
Biol. 313:1003–11 & Macke T. et al. 2001. Nucleic Acids Res. 29:4724–4735).
iTerm-PseKNC
iTerm-PseKNC - is a webserver for the identification of
bacterial transcriptional terminators based on machine learning method.
In the predictor, 5-tuple nucletide frequency and physicochemical
property were extracted to formulate samples. The binomial distribution
technique was proposed to rank 1024 5-tuple nucleotides. Then the
incremental feature selection (IFS) was used to determine the optimal
features which could produce the maximum accuracy. The support vector
machine (SVM) was utilized to perform prediction. Five-fold
cross-validated results showed that 86.07% terminators and 99.46%
non-terminators can be correctly recognized, respectively.
(Reference: Lai H-Y et al. (2019) Mol Ther Nucleic
Acids. 17: 337-346).
Updated: November, 2025