PROMOTERS & TERMINATORS

A. Bacterial

 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).  

 70ProPred - is a predictor for discovering sigma70 promoters based on combining multiple features including position-specific trinucletide propensity (PSTNP) feature extraction in combination with the electron-ion interaction pseudopotentials (EIIPs) of nucleotides.  (Reference: He W et al. (2018) BMC Syst Biol 12(Suppl 4): 44). 

 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 - 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: 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).

 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 - 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).

 BPROM (Softberry) - (Reference: V. Solovyev & A Salamov (2011) Automatic Annotation of Microbial Genomes and Metagenomic Sequences. In Metagenomics and its Applications in Agriculture, Biomedicine and Environmental Studies (Ed. R.W. Li), Nova Science Publishers, p. 61-78)

 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 (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 - 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 (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). 

 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).

 SeqTU (RNA-seq Based Transcription Unit Finder for Prokaryotes) - is a recently developed machine-learning method to accurately identify TUs from RNA-seq data, based on two features of the assembled RNA reads: the continuity and stability of RNA-seq coverage across a genomic region. While good performance was achieved by the method on Escherichia coli and Clostridium thermocellum, substantial work is needed to make the program generally applicable to all bacteria, knowing that the program requires organism specific information.(Reference: Chen X et al. (2017) Sci Rep. 7: 43925).

B. Eukaryotic

Not being a eukaryotic molecular biologist I cannot comment on utility and accuracy of the following promoter- prediction programs.

 FindM (Find Motifs around Functional Sites)  - choose Promoter Motifs from Motif Library

Promoter 2.0 Prediction Server (S. Knudsen,Center for Biological Sequence Analysis, Technical University of Denmark) - predicts transcription start sites of vertebrate Pol II promoters in DNA sequences
Promoter and gene expression regulatory motifs search (Softberry, U.S.A.) - offers a variety of promoter-scanning programs  

 CNNPromoter_e - Prediction of Eukaryotic Promoters by CNN models in genomic sequences. (Reference: Umarov RK, & Solovyev VV (2017) PLoS One. 12(2): e0171410). 

C. Transcriptional terminators - these only apply to rho-independent terminators;  for rho-dependent termiantor sites see

 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

 WebGeSTer - Genome Scanner for Terminators - my favourite terminator search program is finally web enabled.  Please note that if you want to analyze data from a *.gbk file you need to use  their conversion program "GenBank2GeSTer" first. A complete description of each terminator including a diagram is produced by this program.  This site linked to an extensive database of transcriptional terminators in bacterial genome (WebGeSTer DB) (Reference: Mitra A. et al. 2011.  Nucl. Acids Res. 39(Database issue):D129-35).

 ARNold -  finds rho-independent terminators in nucleic acid sequences using two complementary programs, Erpin and RNAmotif. The program colors the terminator stem and loop (References: Gautheret D, Lambert A. 2001.  J Mol Biol. 313:1003–11 & Macke T. et al. 2001. Nucleic Acids Res. 29:4724–4735 ).

FindTerm (Softberry Inc.) - can also be used for mapping rho-independent terminators. You might consider using the advanced feature options and minimally increase the default energy threshold to -12.0. Please note that the online version of this program will only find one terminator at a time.  If you are dealing with a long sequence, once you have located a terminator, delete it from the DNA sequence and resubmit.

 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).