. 2022 Jan 11;23(2):bbab551. doi: 10.1093/bib/bbab551

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction

Meng Zhang ^1,^#, Cangzhi Jia ^2,^#,^✉, Fuyi Li ^3,^4,^#, Chen Li ^5,^#, Yan Zhu ^6,^#, Tatsuya Akutsu ^7,^#, Geoffrey I Webb ^8,⁹, Quan Zou ^10,^✉, Lachlan J M Coin ^11,^✉, Jiangning Song ^12,^13,^✉

¹ School of Science, Dalian Maritime University, Dalian 116026, China

² School of Science, Dalian Maritime University, Dalian 116026, China

³ Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia

⁴ The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC, Australia

⁵ Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia

⁶ School of Science, Dalian Maritime University, Dalian 116026, China

⁷ Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan

⁸ Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC 3800, Australia

⁹ Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia

¹⁰ Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China

¹¹ The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC, Australia

¹² Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia

¹³ Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia

^✉

Corresponding authors: Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. E-mail: Jiangning.Song@monash.edu; Lachlan J.M. Coin, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia. E-mail: lachlan.coin@unimelb.edu.au; Quan Zou, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China. E-mail: zouquan@nclab.net; Cangzhi Jia, School of Science, Dalian Maritime University, Dalian 116026, China. E-mail: cangzhijia@dlmu.edu.cn

Meng Zhang, Cangzhi Jia, Fuyi Li and Chen Li authors contributed equally to this work.

PMCID: PMC8921625 PMID: 35021193

Abstract

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning–based approaches generally outperformed scoring function–based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

Keywords: machine learning, deep learning, promoter identification, performance evaluation

Introduction

Promoters are noncoding regions located close to the transcription start sites (TSSs) in the genomic DNA (i.e. gDNA) sequences, serving as a ‘switch’ to facilitate the transcription and to determine the activities of particular genes [1, 2]. Promoter regions contain short conserved DNA sequences, namely core promoter elements, as binding sites for RNA polymerase and transcription factors [3, 4]. It is well known that the promoter structure and binding complexity of regulating gene expression vary between prokaryotic and eukaryotic genomes. In prokaryotic cells, a collection of different Inline graphic subunit factors of RNA holoenzyme are responsible for binding to the specific promoter regions during gene transcription [5]. Accordingly, the types of prokaryotic promoters are determined by a variety of factor types labeled based on their molecular weights, for example , , , , and Inline graphic -promoter in E. coli and (equivalently, -promoter in E. coli), , , , , , , , , , , , and -promoter in B. subtilis [6]. Promoters in bacteria commonly contain two short DNA sequences: Pribnow box (i.e. −10 box) with consensus nucleic trait ‘TATAAT’ around −10 bp upstream of TSS, and −35 box with consensus ‘TTGACA’ around −35 bp upstream of TSS [7].

Transcription in eukaryotic cells, on the other hand, commences only when the competent pre-initiation complex (PIC) composed of RNA polymerase (RNA pol) and several general transcription factors are recruited to the promoters [8, 9]. Three types of RNA polymerase promoters have been reported responsible for transcribing different subsets of genes due to different locations of RNA pol in the nucleus, including (i) RNA pol I promoters that transcribe genes encoding rRNAs; (ii) RNA pol II promoters that transcribe genes encoding mRNAs, long noncoding RNAs (lncRNAs) and small nuclear RNAs (snRNAs); and (iii) RNA pol III promoters that transcribe genes encoding tRNAs and other small RNAs [10]. As only RNA pol II can transcribe mRNA precursors and translate them into proteins after processing and maturation, the RNA pol II promoter is our main focus in this study. Eukaryotic cells require a minimum of seven transcription factors (i.e. TATA box-binding protein (TBP), pol II-associated TF (TF II D, TF II A, TF II B, TF II F, TF II E and TF II H)) to be involved in transcription initiation [11]. Eukaryotic promoters are significantly more complex and diverse than prokaryotic promoters, spanning a wide range of DNA sequences, including the TATA box located about 25–35 base pairs upstream of the TSS with consensus sequence of ‘TATAAA’, and the upstream activating sequence located at −40 to −110 bp containing the CAAT box and GC box, which control the rate of transcription initiation [12, 13].

A variety of sequencing techniques have been developed for the identification and characterization of promoter sequences [14], including genomic sequencing coupled with full-length cDNA capture and ascertainments [15, 16], such as CAGE [17], 3PEAT [18] and RAMPAGE [19]. Such advanced techniques have led to rapid proliferation of experimental evidence of both prokaryotic and eukaryotic promoter sequences in the post-genomic era, which underpins the design and implementation of computational approaches for promoter prediction. During the past two decades, a number of computational approaches have been developed for predicting prokaryotic and eukaryotic promoters and can be generally categorized into three groups according to their computational methodologies (Figure 1A) including (i) sequence-scoring function–based methods; (ii) traditional machine learning–based methods, and (iii) deep learning–based methods. Several surveys of promoter prediction have been published [2, 13, 20–28] (Figure 1B); however, all focus on tools published prior to 2010 (excluding more recent tools such as TSSPlant [29], CNNProm [30], iProEP [31] and DeePromoter [32]) and moreover do not systematically cover prokaryotic promoter identification tools (40 such approaches). More importantly, most published surveys did not systematically assess the prediction performance of compared approaches by conducting extensive and independent benchmarking tests.

A timeline of (A) the details of computational approaches for predicting prokaryotic and eukaryotic promoters. Font colors are used to distinguish algorithm types and stick colors indicate the prediction targets; and (B) historical reviews and assessments of these methods.

With the goal to address the above issues, in this study, we provide a systematic and comparative analysis by surveying the most up-to-date research progress for predicting both eukaryotic and prokaryotic promoters. To this end, we have manually curated 58 large-scale, reliable, and up-to-date benchmark datasets accompanying this comprehensive survey analysis, which are created to assist the community to assess the relative functionality of alternative approaches and support future research on promoters in both prokaryotes and eukaryotes. A total number of 106 computational approaches based on our literature mining using the papers collected from PubMed and Web of Science with the key words ‘promoter identification’ or ‘promoter prediction’, including 61 for eukaryotic promoter, 40 for prokaryotic promoter and 5 for both, are carefully assessed, benchmarked and extensively discussed in terms of the model construction, performance evaluation strategy, webserver and software usability. More importantly, using the 58 independent test datasets that cover diverse species from a variety of databases, we have systemically assessed the prediction performance of investigated approaches with available webservers or locally executable tools. We anticipate that the comparative analysis in this study serves as a critical analysis of state-of-the-art approaches and represents a stepping-stone toward future development for accurate identification of both prokaryotic and eukaryotic promoters.

Systematic comparison of computational approaches for prokaryotic and eukaryotic promoter prediction

Existing approaches for prokaryotic and eukaryotic promoter prediction

Among the 106 computational approaches for promoter prediction analyzed in this study, 40 were designed and implemented for predicting prokaryotic promoters only, including E. coli, B. subtilis, Cyanobacteria and Chlamydia [33–72]. These methods are systematically described and summarized in Table 1, in terms of algorithm, selected features, evaluation strategy, webserver availability and targeted species. The majority of predictors for prokaryotic promoters can be categorized into three types, including (i) specifically for E. coli Inline graphic -promoter; (ii) generally for E. coli promoters (such as , , , , and ) and (iii) generally for B. subtilis promoters. For eukaryotic promoter prediction, we collected 61 computational prediction tools for H. sapiens, M. musculus, A. thaliana and D. melanogaster [29, 32, 73–131] etc., and further summarized these methods in Table 2. Another five frameworks, including Rani et al.-I [132], Rani et al.-II [133], IPMD [134], CNNProm [30] and iProEP [31], are capable of accurately identifying both prokaryotic and eukaryotic promoters and have been summarized in Table 3. Figure 2 illustrates a flowchart describing four generic steps used by these computational approaches for identifying prokaryotic and eukaryotic promoters, which are discussed in detail in the following sections. An annual breakdown (from 2000 to 2020) of the numbers of publications of predicting prokaryotic and eukaryotic promoters have been provided in Figure 3A.

Table 1.

A comprehensive list of the reviewed methods/tools for the prediction of prokaryotic promoters^a

Framework	Tool^b	Year	Webserver/tool^c	Features/Motifs	Scoring function /Algorithm	Evaluation strategy	Promoter type^d	Species^e	Sequence length (bp)^f
Deep learning–based	Le et al. [67]	2019	Yes^*	FastText n-grams	CNN	5-fold CV	Strong and weak; and unknow	E. coli	81
	iPromoter-BnCNN [70]	2020	Decommissioned	Monomer, trimer and DSP	CNN	5-fold CV and independent test	, , , , and	E. coli	81
Traditional machine learning–based	Leo Gordon et al. [33]	2003	Decommissioned	SAK	SVM	50% train, 50% test	σ ⁷⁰	E. coli	80
	Monteiro et al. [36]	2005	No	–	Comparative study of NBC, DT, SVM and ANN	Leave-one-out	–	B. subtilis and E. coli	117, 57
	da Silva et al. [38]	2006	No	–	Comparative study of KNN, NBC, DT, SVM and ANN	10-fold CV	–	B. subtilis, B. licheniformis, B. cereus, B. megaterium, B. thuringiensis, and B. firmus	111
	Wang et al. [40]	2006	No	DSP₁, −10 motif scores	Fisher LDA	Independent test	–	E. coli and B. subtilis	100
	J. J. Gordon et al. [41]	2006	Decommissioned	5-mer tagged with its location, −10 and −35 hexamers	committee-SVM	10-fold CV	σ ⁷⁰	E. coli	200
	Towsey et al.-I [42]	2006	No	5-mer tagged with its location, −10 and −35 hexamers	SVM	10-fold CV	σ ⁷⁰	E. coli	200
	pHMM-ANN [39]	2007	No	UP element, −10, −35 elements pHMMs	ANN	Independent test	–	E. coli	–
	Towsey et al.-II [44]	2007	No	Similarity score of candidate TSS, −10, −35 scores, TSS-GSS distance, DSP₂	C4.5	10-fold CV	σ ⁷⁰	E. coli	250
	TSS-PREDICT [47]	2008	No	−10 and −35 hexamers, 5-mer tagged with its location, TSS- GSS distribution	Ensemble-SVM	Independent test	σ ⁷⁰; σ⁴³; σ⁶⁶	E. coli, B. subtilis and C. trachomatis	200
	N4 [48]	2009	No	DDS	ANN	Leave-one-out	–	E. coli	414
	Polat et al. [49]	2009	No	57 sequential DNA nucleotide attributes	Fuzzy-AIRS	10-fold CV	–	E. coli	57
	Song et al. [53]	2012	Yes^*	vw Z-curve	PLS	10-fold CV	, , , , and ; , , etc.	E. coli and B. subtilis	80
	iPro54-PseKNC [55]	2014	Yes^*	PseKNC	SVM	10-fold CV and leave-one-out	σ ⁵⁴	E. coli	81
	de Avila e Silva et al. [56]	2014	No	DDS	ANN	2,3,10-fold CV	, , , , and	E. coli	80
	bTSSfinder [57]	2017	Decommissioned	PE, DPE, k-mer, TFBSD, PCP	ANN	Independent test	, , , , , and	E. coli, S. elongatus, Nostoc, and Synechocystis	251, 1101
	iPromoter-2L [58]	2018	Yes	PseKNC	RF	5-fold CV	, , , , and	E. coli	81
	70ProPred [59]	2018	Yes	PSTNP_SS/PSTNP_DS, PseEIIP	SVM	5-fold CV and leave-one-out	σ ⁷⁰	E. coli	81
	IBPP-SVM [60]	2018	Yes^*	‘image’	SVM	Independent test	σ ⁷⁰	E. coli	81
	BacSVM+ [61]	2018	Decommissioned	–	SVM	–	, , , , and	B. subtilis	80
	iPro70-PseZNC [62]	2019	Yes	PseZNC	SVM	5-fold CV	σ ⁷⁰	E. coli	81
	iPromoter-FSEn [63]	2019	Yes	k-mer, g-gapped k-mer, NSM, ASPC, PSO, DN	SVM, LDA, LR	10-fold CV and leave-one-out	σ ⁷⁰	E. coli	81
	iPro70-FMWin [64]	2019	Yes	k-mer, g-gapped k-mer, NSM, ASPC, PSO	LR	10-fold CV	σ ⁷⁰	E. coli	81
	iPSW(2L)-PseKNC [65]	2019	Yes	General PseKNC	SVM	5-fold CV	Strong and weak; and unknow	E. coli	81
	MULTiPly [66]	2019	Yes	BPB, KNN, KNC, DAC	SVM	5-fold CV, leave-one-out and independent test	, , , , and	E. coli	81
	iPromoter-2L2.0 [68]	2019	Yes	k-mer, PseKNC	SVM, EL	5-fold CV	, , , , and	E. coli	81
	SELECTOR [69]	2020	Yes	CKSNAP, PCPseDNC, PSTNPss and DNA strand	RF, AdaBoost, GBDT, LightGBM, XGBoost	5-fold CV and independent test	, , , , and	E. coli	81
Scoring function–based	Huerta et al. [34]	2003	No	−10 and −35 box, spacer between −10 and −35 box	PWM	Independent test	σ ⁷⁰	E. coli	250
	TLS-NNPP [35]	2005	No	TSS-TLS distance, the results from NNPP2.2	Probability	Independent test	–	E. coli	500
	Kanhere et al. [37]	2005	No	DDS	DE	Independent test	–	E. coli, B. subtilis and C. glutamicum	1000
	Li et al. [43]	2006	No	Hexamer sequence conservation	PCSF	10-fold CV	σ ⁷⁰	E. coli	81
	Beagle [72]	2006	Decommissioned	UP element, −10, −35 and extended −10 elements, and TSS-GSS gap	PWM	10-fold CV	σ ⁷⁰	E. coli and B. subtilis	250
	Footy [45]	2007	Decommissioned	−10 and −35 hexamers	PWM	Independent test	σ ⁶⁶	C. trachomatis, C. pneumoniae, C. caviae and C. muridarum	–
	Rangannan et al. [46]	2007	No	DDS	DE	Independent test	–	E. coli and B. subtilis	101, 1001
	PromPredict [50]	2009	Yes^*	DDS	DE	Independent test	–	E. coli, B. subtilis and M. tuberculosis	1001
	PromPredict [51]	2010	Yes^*	DDS, GC content	DE	Independent test	–	913 bacteria in PromBase [183]	1001
	BacPP [52]	2011	Yes	Rules extracted from neural networks	Weighting promoter prototypes	2, 3, 10-fold CV	, , , , and	E. coli	80
	Todt et al. [54]	2012	No	−10, −35 and extended −10 elements	PWM	Independent test	σ ⁷⁰	L. plantarum	100
	G4PromFinder [71]	2018	Yes*	AT-rich element and G-quadruplex motif	–	Independent test	–	S. coelicolor and P. aeruginosa	251

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^aAbbreviations: CNN—convolutional neural network; CV—cross-validation; DSP—DNA structural property; SAK—sequence alignment kernel; SVM—support vector machine; TSS-TLS distance—the distance between the transcription start site (TSS) and the translation start site (TLS); TDNN—time-delay neural network; NBC—naïve Bayes classifier; DT—decision tree; ANN—artificial neural network; KNN—k-nearest neighbor; DSP₁—SIDD, curvature, deformability, thermodynamic stability; SIDD—stress-induced DNA duplex destabilization; LDA—linear discriminant analysis; committee-SVM—DGS, PWM and ensemble SVM; DGS—the distribution of TSS distance to gene start; PWM—position weight matrix; pHMMs—profile hidden Markov models; DSP₂—DNA curvature, SIDD, stacking energy; DDS—DNA duplex stability; Fuzzy-AIRS—Artificial Immune Recognition System with Fuzzy resource allocation mechanism; vw Z-curve—variable-window Z-curve; PLS—partial least squares; PseKNC—pseudo–K-tuple nucleotide composition; PE—promoter elements including −10, −35, −15 and AT-rich UP elements, together with the new TSS motifs by the authors; DPE—distances (d) between promoter elements (contains d(−10/−35), d(−10/TSS) and d(−15/−10)); TFBSD—TFBSs density; PCP—physico-chemical properties (i.e. free energy, base stacking, entropy and melting temperature); RF—random forest; PSTNP_SS/PSTNP_DS—position-specific trinucleotide propensity based on single-stranded or double-stranded characteristic, PseEIIP—electron–ion interaction pseudo-potentials of trinucleotide; PseZNC—pseudo–multi-window Z-curve nucleotide composition; NSM—nucleotide statistical measure; ASPC—approximate signal pattern count; PSO—position specific occurrences; DN—distribution of nucleotides; LR—logistic regression; BPB—bi-profile Bayesian signatures; KNC—k-tuple nucleotide composition; DAC—dinucleotide-based auto-covariance; EL—ensemble learning; CKSNAP—composition of k-spaced nucleic acid pairs; PCPseDNC—parallel correlation pseudo-dinucleotide composition; GBDT—gradient boosting decision tree; DE—relative stability (the difference in free energy); PCSF—position-correlation scoring function.

^bThe URL addresses for the listed tools: iPro54-PseKNC—http://lin-group.cn/server/iPro54-PseKNC; iPromoter-2L—http://bioinformatics.hitsz.edu.cn/iPromoter-2L/; 70ProPred—http://server.malab.cn/70ProPred/; iPro70-PseZNC—http://lin-group.cn/server/iPro70-PseZNC; iPro70-FMWin—http://ipro70.pythonanywhere.com/server; iPromoter-FSEn—http://ipromoterfsen.pythonanywhere.com/server; iPSW(2L)-PseKNC—http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC; MULTiPly—http://flagshipnt.erc.monash.edu/MULTiPly/; iPromoter-2L2.0—http://bliulab.net/iPromoter-2L2.0/; SELECTOR—http://SELECTOR.erc.monash.edu/; PromPredict— http://nucleix.mbu.iisc.ernet.in/prompredict/prompredict.html; BacPP—http://bacpp.bioinfoucs.com/home.

^cYes—The approach is accompanied with a webserver/tool and it is still working; Decommissioned—The webserver/tool is no longer available; No—The approach has no webserver or tool; Yes^*—The server/tool was not involved in our performance comparison due to the unavailable pretrained model, unavailable latest test data or the unmatched sequence length.

^dWe listed the detailed prokaryotic promoter types based on the description in the papers. ‘–’ demonstrates such information is not present in the paper.

^eThe species information of the sequences used in corresponding studies was directly extracted from the studies. For some species, the Latin names have been provided according to the predictors; for other species, based on the information provided in the papers, we just provided the general names of the species when their Latin names are not available.

^f‘–’ demonstrates that no clearly length information is provided in the paper.

Table 2.

A comprehensive list of the reviewed methods/tools for the prediction of eukaryotic promoters^a.

Method type	Tool^b	Year	Webserver/tool^c	Features/Motifs	Scoring function /Algorithm	Evaluation strategy	Promoter type^d	Species^e	Sequence length (bp)^f
Deep learning–based	Qian et al. [127]	2018	No	–	CNN	10-fold CV	–	H. sapiens	300
	DeePromoter [32]	2019	Yes		CNN, BiLSTM	5-fold CV	TATA-containing, TATA-less	H. sapiens and M. musculus	300
	DeeReCT-PromID [129]	2019	Decommissioned		CNN	90% train; 10% test	TATA-containing, TATA-less	H. sapiens	10000
	Depicter [130]	2020	Yes		CNN and capsule network	5-fold CV and independent test	TATA-containing, TATA-less	H. sapiens, M. musculus, D. melanogaster and A. thaliana	300
Traditional machine learning–based	CpG_promoter [74]	2000	Decommissioned	CpG island parameters	QDA	70% train, 30% test	CpG-related, non-CpG-related	H. sapiens	–
	Ohler et al.-I [75]	2000	Decommissioned	Upstream, TATA box and Inr/downstream regions	SSM	5-fold CV	–	H. sapiens and D. melanogaster	300
	McPromoter [76]	2000	Decommissioned	Segmental and structural profile features	ANN	Independent test	–	D. melanogaster	300
	FirstEF [77]	2001	Decommissioned	Hexamer and pentamer frequencies, the CpG and G+C percentage	QDA	Independent test	CpG-related, non-CpG-related	H. sapiens	570
	Hannenhalli et al. [78]	2001	No	5-mer and TFBSD	LDA	Independent test	–	H. sapiens	1200
	NNPP2.2 [79]	2001	Yes	TATA box and Inr	TDNN	4-fold CV and independent test	–	H. sapiens and D. melanogaster	–
	Eponine [81]	2002	Decommissioned	–	RVM	Independent test	–	H. sapiens and M. musculus	–
	CpGProD [82]	2002	Yes^*	CpG island	GLM	–	CpG-related	H. sapiens and M. musculus	–
	Ohler et al.-II [83]	2002	No	–	ANN	5-fold CV and independent test	–	D. melanogaster	300
	DPF [84]	2002	Decommissioned	PWMs of pentamers	ANN	Independent test	–	Vertebrates in EPD [184]	–
	DRAGON [87]	2003	Decommissioned	PWMs of pentamers	ANN	Independent test	–	Vertebrates in EPD [184]	250
	PromH [88]	2003	Decommissioned	PEs	LDA	Independent test	TATA-containing, TATA-less	H. sapiens and M. musculus	–
	DragonGSF [89]	2003	Decommissioned	CpG island, TSS location, DPE	ANN	Independent test	–	H. sapiens	10000
	ProGA [90]	2003	Decommissioned	DNC	GA	80% train; 20% test	TATA-containing, DPE-containing	D. melanogaster	400
	Kasabov et al. [91]	2004	No	Similarity reflexed on the promoter vocabulary	TSVM	3-fold CV	–	Vertebrates in EPD [184]	250
	Prometheus [93]	2005	No	k-mer, GC%, Lyapunov component and Tsallis entropy	SVM	10-fold CV and independent test	–	H. sapiens	300
	TSSP-TCM [94]	2005	Decommissioned	Content and signal features	SVM	Independent test	TATA-containing, TATA-less	Plant in PlantProm [185]	351
	BayesProm [95]	2005	Decommissioned	Oligonucleotide positional density	NBC	Independent test	–	H. sapiens	600
	PromoterExplorer [96]	2006	No	Local distribution of pentamers, positional CpG island features and digitized DNA sequence	AdaBoost	Independent test	–	Vertebrates in EPD [184]	250
	ARTS [99]	2006	Decommissioned	WDs, spectrum kernel, twisting angles and stacking energies	SVM	Independent test	–	H. sapiens	2000
	FProm [100]	2006	Yes	Motif density, triplets, hexaplets, position triplet matrix, CpG content, TATA box, similarity, Protein-DNA-twist, Protein-induced deformability	LDA	Independent test	TATA-containing, TATA-less	H. sapiens	250
	Ohler et al.-III [102]	2006	No	TATA box, Inr, DPE, MTE, M1/6	ANN	5-fold CV and independent test	–	D. melanogaster	300
	CoreBoost [103]	2007	Decommissioned	PEs, TFBSs, flexibility scores, Markovian scores, and k-mer	LogitBoost	5-fold CV and leave-one-out	CpG-related, non-CpG-related	H. sapiens	300
	TSS-AMOSA [104]	2007	No	PEs, TFBSs, flexibility scores, Markovian scores and k-mer	LDA	5-fold CV	CpG-related, non-CpG-related	H. sapiens	300
	MetaProm [107]	2007	No	Predictions from PSPA, FirstEF, McPromoter, DragonGSF, DragonPF, and FProm along with their features	ANN	10-fold CV	CpG-related, non-CpG-related	H. sapiens	–
	PromMachine [108]	2008	No	4-mer	SVM	7-fold CV	–	D. melanogaster, plant in PlantProm [185], H. sapiens, M. musculus and R. norvegicus	251
	IDQD [109]	2008	No	4, 5, 6-mer, G+C content	IDQD	Independent test	–	H. sapiens	2000
	Yang et al. [113]	2008	No	Z curve analysis, CTD, EDP	LDA	50% train, 50% test	–	H. sapiens	600
	CoreBoost_HM [115]	2009	Decommissioned	PEs, TFBSs, flexibility scores, Markovian scores and k-mer	LogitBoost	10-fold CV	CpG-related, non-CpG-related	H. sapiens	300
	RBF-TSS [116]	2009	Yes^*	k-mer	RBFNN	Independent test	–	H. sapiens	2400
	SCS [117]	2010	No	Signal, context and structure features	C4.5	3-fold CV	–	H. sapiens	251
	DDM [118]	2010	Decommissioned	k-mer	Multi-staged daisy-chain filtering	4-fold CV	–	H. sapiens and M. musculus	200, 1600
	PromoBot [119]	2011	No	Hexamer frequency, RTP	SVM	5-fold CV	–	Plant in PlantProm [185]	251
	Zuo et al. [120]	2011	No	KPCS, KIOCD, GCSS, DNA geometric flexibility	SVM	10-fold CV, independent test	TATA-containing, TATA-less	Plant in PlantProm [185]	251
	GPMiner [123]	2012	Decommissioned	CpG islands, nucleotide composition and DNA stability	SVM	5-fold CV	–	Human, mouse, rat, chimpanzee and dog in DBTSS	6000
	ProMT [125]	2014	No	DSP	MCM	Independent test	–	H. sapiens	1000
	TSSPlant [29]	2017	Yes	PE, d(PE, TSS), k-mer, TFBSD, CG skew, AC skew	ANN	Independent test	TATA-containing, TATA-less	A. thaliana, O. sativa	251, 1101
	DCDE-MSVMs [128]	2019	Decommissioned	k-mer, DCDE	SVM, BD	k-fold CV	–	H. sapiens	251
Scoring function–based	PromoterInspector [73]	2000	Decommissioned	IUPAC groups with wildcards	–	3-fold CV	–	Vertebrates in EPD [184]	100
	Levitsky et al. [80]	2001	Decommissioned	DNC	TATA box weight matrix	Leave-one-out	TATA-containing, TATA-less	D. melanogaster	400
	CONPRO [85]	2002	Decommissioned	–	Combining the results of TSSG, TSSW, NNPP, PROSCAN and PromFD methods	Independent test	–	H. sapiens and M. musculus	–
	PromoSer [86]	2003	Decommissioned	–	Alignments of partial and full-length mRNA sequences to genomic DNA	–	–	H. sapiens, M. musculus and R. norvegicus	–
	Ma et al. [92]	2004	No	TFBSs	PSSD	Independent test	–	Vertebrates in EPD [184]	300
	PSPA [97]	2006	Decommissioned	Position specific k-mers	PSPA	10-fold CV	CpG-related, non-CpG-related	H. sapiens	200
	PromAn [98]	2006	Decommissioned	GC distribution, TSS location and TFBS predictions	Phylogenetic footprinting	–	–	Species in EPD [184] and DBTSS	–
	Pandey et al. [101]	2006	No	A+T content, relative entropy, periodicity, DNA curvature	PWM	Independent test	TATA-containing, TATA-less	Plant in PlantProm [185], yeast and E. coli	100
	Wu et al. [105]	2007	No	k-mer	PWM	Independent test	–	H. sapiens	250
	ProStar [106]	2007	Decommissioned	Dinucleotide flexibility parameters	MD	Independent test	–	H. sapiens	500
	EP3 [110]	2008	Yes	GC content and DSP₁ profiles	–	Independent test	–	H. sapiens, M. musculus, P. falciparum, O. pacifica, O. tauri, A. thaliana, O. sativa, P. trichocarpa, S. cerevisiae, S. pombe, C. elegans, D. melanogaster, T. nigroviridis	400
	EnsemPro [111]	2008	No	–	Combining the results of TSSG, TSSW, NNPP, Proscan, FirstEF, Dragon, Eponine, Promoter2.0 methods	fold CV, independent test	–	H. sapiens	–
	Akan et al. [112]	2008	No	DSP₂, CpG counts and ATG codon count	PWM	–	CpG-related, non-CpG-related	H. sapiens and M. musculus	1201
	TSSer [114]	2009	No	Positional frequency of 5’ EST matches on genomic DNA	–	Independent test	–	A. thaliana and M. musculus	400
	PromPredict [121]	2011	Yes	DDS	DE	Independent test	–	A. thaliana, O. sativa	1001
	Fang et al. [122]	2011	No	TFBSs-SV, TFBSs-PV	IWM	Independent test	–	H. sapiens, vertebrate	300
	NPEST [124]	2013	Decommissioned	DEST	NPML	–	–	A. thaliana	3000
	Datta et al [131]	2013	No	CpG islands and CpG counts, PEs, DSP₃	CFG rules	Independent test	–	H. sapiens	1201
	PromPredict [126]	2018	Yes	DDS, GC content	DE	Independent test	–	48 eukaryotic species	1001

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^aAbbreviations: CNN—convolutional neural network; CV—cross-validation; BiLSTM—bidirectional long short-term memory; CpG island parameters—length, C+G mononucleotide content and ratio of observed to expected CpG content; QDA—quadratic discriminant analysis; Inr—initiator; SSM—stochastic segment models; ANN—artificial neural network; TFBSD—TFBS density; TDNN—time-delay neural network; RVM—relevance vector machine; PWMs—positional weight matrices; PEs—promoter elements; LDA—linear discriminant analysis; DPE—downstream promoter element; DNC—dinucleotide composition; GA—genetic algorithm; TSVM—transductive support vector machines; NBC—naïve Bayes classifier; WDs—weighted degree kernel with shifts; MTE—motif 10 element; TFBSs—transcription factor binding sites; IDQD—increment diversity with quadratic discriminant analysis; CTD—composition–transition–distribution; EDP—entropy density profile; RBFNN—radial basis function neural network; RTP—random triplet-pair; KPCS—k-mer position correlation score; KIOCD—k-mer increment of overlap content diversity; GCSS—GC-Skew score; DSP—DNA structural properties; MCM—Markov chain model; d(PE, TSS)—distance between promoter element and TSS; TFBSD—TFBS density; CG skew and AC skew—variations in base frequencies along sequence; DCDE—deep convolutional divergence encoding; BD—bilayer decision model; PSSD—TFBS pair scoring system with distance; PSPA—position-specific propensity analysis; MD—Mahalanobis distance; DSP₁—base-stacking property, bendability and duplex stability-free energy; DSP₂—propeller twist angle, bendability and nucleosome positioning preference; DE—relative stability (the difference in free energy); TFBSs-SV—transcription factor binding sites structural variability; TFBSs-PV—transcription factor binding sites positional variability; IWM—interval weight matrix; DEST—the distribution of expressed sequence tags; NPML—nonparametric maximum likelihood; DSP₃—propeller twist, bendability, nucleosome position, DNA denaturation, zDNA, base staking energy, bDNAtwist and Aphilicity; CFG—context-free grammar.

^bThe URL addresses for the listed tools are DeePromoter—https://home.jbnu.ac.kr/NSCL/deepromoter.htm; Depicter—https://depicter.erc.monash.edu/; TSSPlant—http://www.softberry.com/berry.phtml?topic=tssplant&group=programs&subgroup=promoter; PromPredictvhttp://nucleix.mbu.iisc.ac.in/prompredict/prompredict.html; CpGProD—http://pbil.univ-lyon1.fr/software/cpgprod.html; FProm—http://www.softberry.com/berry.phtml?topic=fprom&group=programs&subgroup=promoter; NNPP2.2—https://www.fruitfly.org/seq_tools/promoter.html.

^cYes—The approach is accompanied with a webserver/tool and it is still working; Decommissioned—The webserver/tool is no longer available; No—The approach has no webserver or tool; Yes^*—The server/tool was not involved in our performance comparison due to the fact that the outputs of the predictor need extra steps to interpret.

^dWe listed the detailed eukaryotic promoter types based on the description in the papers. ‘–’ demonstrates such information is not present in the paper.

^eThe species information of the sequences used in corresponding studies was directly extracted from the papers. For some species, the Latin names have been provided according the predictors; for other species, based on the information provided in the papers, we just provided the general names of the species when their Latin names are not available.

^f‘–’ demonstrates that no clearly length information is provided in the paper.

Table 3.

A comprehensive list of the reviewed methods/tools for prediction of prokaryotic and eukaryotic promoters^a

Method type	Tool^b	Year	Webserver^c	Features/Motifs	Scoring function /Algorithm	Evaluation strategy	Species and promoter type	Sequence length (bp)
Deep learning–based	CNNProm [30]	2017	Yes	–	CNN	70% train, 20% test, 10% validation	H. sapiens (TATA-containing and TATA-less), M. musculus (TATA-containing and TATA-less), A. thaliana (TATA-containing and TATA-less), E. coli () and B. subtilis	81, 251
Traditional machine learning–based	Rani et al.-I [132]	2007	No	DNC	ANN	5-fold CV and independent test	E. coli (), and D. melanogaster	80, 241
	Rani et al.-II [133]	2009	No	n-gram (n=2,3,4,5)	ANN	5-fold CV and independent test	E. coli () and D. melanogaster	80, 300
	iProEP [31]	2019	Yes	PseKNC and PCSF	SVM	5, 10-fold CV	D. melanogaster, H. sapiens, C. elegans, E. coli () and B. subtilis ()	81, 300
Scoring function–based	IPMD [134]	2010	No	PCSF and ID	Modified MD	10-fold CV and independent test	D. melanogaster, H. sapiens, C. elegans, E. coli () and B. subtilis ()	81, 300

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^aAbbreviations: CNN—convolutional neural network; DNC—dinucleotide composition; ANN—artificial neural network; CV—cross-validation; PseKNC—pseudo–K-tuple nucleotide composition; PCSF—position-correlation scoring function; SVM—support vector machine; ID—increment of diversity; MD—Mahalanobis Discriminant.

^bThe URL addresses for the predictors with functioning webserver: CNNProm—http://www.softberry.com/berry.phtml?topic=cnnprom&group=programs&subgroup=deeplearn; iProEP—http://lin-group.cn/server/iProEP/pages/download.php.

^cYes—The approach is accompanied with a webserver/tool and it is still working; Decommissioned—The webserver/tool is no longer available; No—The approach has no webserver or tool.

A graphical illustration of four common steps for the construction and evaluation of computational approaches for predicting prokaryotic and eukaryotic promoters, including data collection, feature engineering, classification algorithm selection and performance evolution and webserver development.

A statistical analysis of current computational approaches for promoter prediction. (A) The numbers of annual publications for prokaryotic and eukaryotic promoter prediction since 2000. Different colors represent different types of predictors, including promoter prediction tools for prokaryote, eukaryote and both. (B) Length distribution of prokaryotic and eukaryotic promoter sequences from a variety of training datasets. (C) Comparison of frequency of computational approaches applied promoter prediction for prokaryote and eukaryote. (D) Pie charts demonstrating the types and availability of prokaryotic and eukaryotic promoter prediction tools.

Construction of training datasets

Four public databases, including the Eukaryotic Promoter Database (EPDnew) [135], DBTSS [136], RegulonDB [137] and DBTBS [138], have been used as mainstream data resources for constructing the training datasets for prokaryotic and eukaryotic promoter predictors. In addition, Ensembl [139], BioMart [140] and UCSC Genome Browser [141] have been used for retrieving and extracting the promoter datasets as well. A brief summary of these databases is provided in Table 4. EPDnew is the major database that provides a nonredundant collection of eukaryotic pol II promoters from a variety of species, including animals, plants, fungi and invertebrates, while DBTSS is a database documenting the biological information of transcription start sites. For prokaryotic promoters, RegulonDB and DBTBS are considered as the major hubs containing Inline graphic , , , , and -promoter data from E. coli and , , , , , , , , , , , , and -promoter data from B. subtilis, respectively. We collected the training sequences from all available predictors for analyzing the length distribution of prokaryotic and eukaryotic promoters, respectively. The sequence length in the prokaryotic promoter training data ranges from 57 to 1101 bp, while the sequence length ranges from 100 bp to 10000 bp in the eukaryotic promoter training data. As shown in Figure 3B, most of the prokaryotic promoters are 81 bp long and less than 251 bp, while the vast majority of the eukaryotic promoters are within 251–300 bp long. The maximum length for the eukaryotic promoter sequences in Figure 3B has been restricted to 3000 bp for a better representation due to the highly skewed data.

Table 4.

The databases extensively applied to collect multifarious promoter sequences

Type	Database	Latest version	Feature	Website (URL)
Prokaryotic	RegulonDB [137]	10.8	RegulonDB is the primary database on transcriptional regulation in Escherichia coli K-12 containing knowledge manually curated from original scientific publications, complemented with high-throughput datasets and comprehensive computational predictions.	http://regulondb.ccg.unam.mx/
	DBTBS [138]	5.0	DBTBS is a database of transcriptional regulation in Bacillus subtilis containing upstream intergenic conservation information.	http://dbtbs.hgc.jp/
Eukaryotic	EPDnew [135]	latest	EPDnew is an annotated nonredundant collection of eukaryotic POL II promoters, for which the transcription start site has been determined experimentally.	https://epd.epfl.ch//EPD_database.php
	DBTSS [136]	10.1	DBTSS is a database of transcription start sites.	https://dbtss.hgc.jp/

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Construction of independent test datasets

In order to objectively compare the prediction performance of current existing approaches, we constructed in total 58 balanced and unbalanced independent test datasets by cross-referencing public databases and removing the overlapping sequences with the training datasets of compared approaches. The resulting independent test datasets contain sequences from a variety of species, including E. coli, B. subtilis, H. sapiens, R. norvegicus, A. thaliana, D. melanogaster and Z. mays. Herein we describe the detailed procedures for curating the independent test datasets (Table 5).

Table 5.

The balanced independent test datasets for seven species

Type	Species	Dataset name	TATA	Number of promoters	Number of non-promoters	Location	Length
Prokaryotic	E. coli	E. coli-I	–	1089	1089	[−60, +20]	81
	E. coli	E. coli-II		48	48	[−60, +20]	81
	B. subtilis	B. subtilis		283	283	[−60, +20]	81
Eukaryotic	H. sapiens	H. sapiens-I	TATA-containing	229	229	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001
	H. sapiens	H. sapiens-II	TATA-less	1328	1328	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001
	R. norvegicus	R. norvegicus-I	TATA-containing	456	456	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001
	R. norvegicus	R. norvegicus-II	TATA-less	3834	3834	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001
	D. melanogaster	D. melanogaster-I	TATA-containing	207	207	[−249, +50], [−500, +500]	300, 1001
	D. melanogaster	D. melanogaster-II	TATA-less	1153	1153	[−249, +50], [−500, +500]	300, 1001
	A. thaliana	A. thaliana-I	TATA-containing	474	474	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001
	A. thaliana	A. thaliana-II	TATA-less	1073	1073	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001
	Z. mays	Z. mays-I	TATA-containing	3051	3051	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001
	Z. mays	Z. mays-II	TATA-less	13813	13813	[−200, +50], [−249, +50], [−500, +500]	251, 300, 1001

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Based on the three types of predictors for prokaryotic promoters (refer to ‘Existing approaches for prokaryotic and eukaryotic promoter prediction’), three balanced independent test datasets were constructed for evaluating the prediction performance of these predictors (Table 5). We collected all types of promoter sequences from E. coli with 81 bp length from RegulonDB [137] (version 10.8, last update on 10 December 2020). After removing those sequences with pairwise sequence identities greater than 80% using CD-HIT-EST [142, 143], 3028 sequences were retained (including 488 Inline graphic , 137 , 294 , 232 , 99 and 1778 -promoter sequences). As DNA sequences only contain four letters, it is possible to achieve high similarity between unrelated sequences. In general, comparing DNAs at low identity <80% may not be very effective. Such threshold for sequence redundancy removal is a stringent cutoff, which is consistent with the threshold used in the previous studies [31, 58, 66, 69]. Non-promoter sequences (i.e. negative samples) refer to the DNA sequences/regions that do not contain promoter regions (i.e. the 0-th positions of sequences that are confirmed not to be TSSs). According to the methods for selecting negative samples in the majority of the literature works, non-promoter sequences for B. subtilis and E. coli include the coding sequences and convergent intergenic sequences. Thus, we extracted 4695 E. coli K12 code-region sequences from RegulonDB. All possible subsequences with 81 bp length were then generated, and 20 000 sequences were randomly selected. Then the similar sequences were removed using similarity of 80% by CD-HIT-EST, leaving 18244 non-promoter sequences retained. For the available predictors designed specifically for Inline graphic -promoter (iPro70-PseZNC, 70ProPred, iPromoter-FSEn, iPro70-FMWin), we removed those sequences greater than 80% similarity by comparing the 741 -promoter with our newly collected 1778 -promoter sequences. For negative samples (i.e. non-promoter sequences), the similar procedure was applied to the 18 244 non-promoter sequences, and the equal number of non-promoter sequences was randomly selected. The resulting independent test dataset E. coli-I contains 1089 Inline graphic -promoter and 1089 non-promoter sequences. Similarly, the independent test dataset E. coli-II was generated by removing the similar sequences using CD-HIT-EST compared to the training datasets of available predictors for generic E. coli promoters (including BacPP, iPSW(2L)-PseKNC, iPromoter-2L, MULTiPly, iPromoter-2L 2.0, SELECTOR, iProEP and CNNProm). The resulting E. coli-II dataset contained 48 promoter and 48 non-promoter sequences after removing the similar sequences using CD-HIT-EST. For B. subtilis, the promoter sequences were downloaded from DBTBS [138] Release 5. After removing redundant sequences and aligning with the combined B. subtilis training datasets from CNNProm and iProEP, 283 promoter sequences remained after similarity removal by CD-HIT-EST, constituting the positive samples of the independent test dataset B. subtilis. Over 100 coding-region sequences of B. subtilis were manually extracted from NCBI Nucleotide database (accession number NC_000964.3). Similarly, all possible subsequences with 81 bp length were extracted. After applying CD-HIT-EST, 7314 non-promoter sequences remained, 283 out of which were randomly selected to constitute the negative samples of the B. subtilis dataset. As iProEP can predict general promoters of both E. coli and B. subtilis, it was compared and assessed using all the three independent test datasets.

For eukaryotic promoter prediction, given that some models divided the eukaryotic promoters into TATA-containing promoters (i.e. promoter sequences with the TATA box element) and TATA-less promoters (i.e. promoter sequences without the TATA box element) [32, 88, 129, 130, 144], we downloaded the eukaryotic promoter sequences of four species (i.e. H. sapiens, R. norvegicus, A. thaliana and D. melanogaster), based on the presence of core promoter elements, i.e. TATA box, from the EPDnew [135] database (last update October 2019). These sequences are 251 bp long from 200 bp upstream to 50 bp downstream regions of TSS (i.e. [−200, +50]), with the TSS referred to as the 0-th site. Subsequently, for each species, the CD-HIT-EST program was employed to remove sequence redundancy with the sequence identity threshold of 80%. A similar sequence removal procedure was applied to each species and promoter type, resulting in eight balanced independent test datasets (Table 5). Note that DeePromoter provides both human and mouse models trained by the M. musculus and H. sapiens promoter sequences, respectively. However, there is no update of M. musculus or H. sapiens promoter sequences in EPDnew. We therefore used sequences of R. norvegicus to perform a cross-species evaluation for DeePromoter, considering that M. musculus and R. norvegicus are murids and the sequences among these two species share high sequence identity. We also did not compare the similarity between the training human promoter sequences of DeePromoter with our dataset. There are many unidentified nucleotides in the R. norvegicus promoter sequences that cannot be processed by some tools, and these sequences with unidentified nucleotides were removed. Note that different predictors require different lengths of input sequences. In general, three different sequence lengths are needed, including 251, 300 and 1001 bp. The input for the corresponding predictors was then adjusted to the requested sequence length (251: [−200, +50]; 300: [−249, +50] and 1001: [−500, +500]). The non-promoter sequences of four species, containing introns and exons, were extracted from Exon-Intron Database (EID) (http://bpg.utoledo.edu/~afedorov/lab/eid.html). The initially collected non-promoter sequences were processed as above and then we randomly selected the same number as the promoter sequences. The details on the numbers and location ranges of promoter and non-promoter sequences for each species in the independent test datasets are given in Table 5.

For each of the six species, in addition to the balanced test datasets, we have constructed unbalanced independent test datasets, which include a small number of positive samples (i.e. promoter regions/sequences) and a large number of negative samples (i.e. non-promoter regions/sequences). Specifically, the ratios of promoters to non-promoters (i.e. positive: negative) are 1:2, 1:3, 1:4 and 1:5 for E. coli, B. subtilis, H. sapiens, R. norvegicus, A. thaliana and D. melanogaster, respectively. The numbers of non-promoter sequences in the unbalanced independent test datasets for six species are shown in Table 6. Apart from the commonly used dicot plant (i.e. A. thaliana) promoter dataset, we further extracted the monocot plant (i.e. Z. mays) promoter dataset from the EPDnew [135] database, which contained 3051 TATA-containing promoters and 13813 TATA-less promoters after removing the redundant sequence and those sequences with ‘N’. As sufficient samples are available, for each promoter type, we constructed the balanced dataset (positive: negative = 1:1) for performance evaluation. We also have added the 5′ and 3′ UTR sequences as the non-promoter sequences into the unbalanced eukaryotic independent test datasets.

Table 6.

The unbalanced independent test datasets of six species

Type	Species	Dataset name	TATA	1:1^a	1:2	1:3	1:4	1:5
Prokaryotic	E. coli	E. coli-I	–	1089	2178	3267	4356	5445
	E. coli	E. coli-II		48	96	144	192	240
	B. subtilis	B. subtilis		283	566	849	1132	1415
Eukaryotic	H. sapiens	H. sapiens-I	TATA-containing	229	458	687	916	1145
	H. sapiens	H. sapiens-II	TATA-less	1328	2656	3984	5312	6640
	R. norvegicus	R. norvegicus-I	TATA-containing	456	912	1368	1824	2280
	R. norvegicus	R. norvegicus-II	TATA-less	3834	7668	11502	15336	19170
	D. melanogaster	D. melanogaster-I	TATA-containing	207	414	621	828	1035
	D. melanogaster	D. melanogaster-II	TATA-less	1153	2306	3459	4612	5765
	A. thaliana	A. thaliana-I	TATA-containing	474	948	1422	1896	2370
	A. thaliana	A. thaliana-II	TATA-less	1073	2146	3219	4292	5365

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^aPositive:negative ratio.

In addition, we downloaded the human chromosome 22 DNA sequence that is short and manageable, from the UCSC Genome Browser [141] (http://genome.ucsc.edu/) as an independent test dataset to evaluate the performance of EP3 [110] and PromPredict [126], which are two human promoter prediction methods and can predict promoters on the genome level. To avoid a biased evaluation of currently available promoter predictors, performance comparison was conducted on the CAGE dataset, which is based on the cap analysis gene expression (CAGE) technique [17]. It covers the whole human genome more widely and is retrieved from the FANTOM3 project (http://fantom.gsc.riken.go.jp/). As described in EP3 [110], only tag clusters with at least two mapped tags on the same genomic location were considered to be real TSSs. After mapping these tags to the human genome sequence, we obtained 181,047 unique human TSSs. The whole human genome (hg17) was retrieved using the UCSC Genome Browser [141].

Feature engineering and representation

To build a reliable and robust computational model for promoter prediction, features representing the DNA sequences should be carefully designed and extracted. Four major classes of features have been widely applied in the 106 approaches we revisited in this study, including signal-, context-, structure-based and integrated features.

Signal-based features

Signal-based features mainly contain the information of core promoter elements, specifically TATA box and mammalian CpG islands, which reflect the salient biological signals [145, 146]. The prokaryotic promoter regions may contain a Pribnow box (i.e. −10 box), which serves as a homolog compared to the eukaryotic TATA box with consensus sequence TATAAT, and −35 box [7]. Another important element is the AT-rich UP element, which serves as binding site for the alpha subunit of RNA polymerase [147, 148]. Given that the exact number and the location of individual promoter elements vary, it is expedient to define a promoter region by calculating the distance between the element and the TSS [35, 44, 47]. Eukaryotic promoters, on the other hand, typically contain seven core promoter elements, including TATA box, BRE^u and BRE^d (TF II B recognition element), Inr (initiator element), TCT (polypyrimidine), MTE (motif ten element) and DPE (downstream promoter element). Located within 25–30 bp upstream of the TSS with consensus sequence of ‘TATAAA’ [149], the TATA box appears in approximately 30–50% of all known promoters, except for its absence from the promoters of some particular genes, such as housekeeping and photosynthesis genes [150]. These promoters are therefore referred to as TATA-less promoters. Two BRE motifs are located in either upstream (BRE^u) or downstream (BRE^d) of a subset of TATA box elements [151]. The TCT motif, which serves as a key component of an RNA polymerase II system, spans from −2 to +6 relative to the TSS [152], while the MTE and DPE motifs are adjacent and both appear to be in the proximity to the TF II D subunits TAF6 and TAF9 [153]. In addition, CpG islands are unmethylated DNA segments with length of at least 200 bp [154]. The CpG island is featured by its GC pairs that account for more than 50% of the content, and its observed-to-expected CpG ratio (>60%). Approximately, half of the promoters in mammalian genomes have the CpG islands close to the starts of genes [146]. Therefore, the presence of a CpG island may represent a useful global signal for locating the promoters across genomes [74, 82, 89, 96].

Context-based features

Context-based features are extracted to describe the genomic context of the promoter sequences. We classify these context-based features into three groups: (i) DNA primary sequence-derived features, such as k-mer composition [68, 118], g-gapped k-mer composition [64], nucleotide statistical measure (NSM) [63] and variable-window Z-curve [53]; (ii) nucleotide physicochemical properties including pseudo–k-tuple nucleotide composition (PseKNC) [58], general PseKNC [65], pseudo–multi-window Z-curve nucleotide composition (PseZNC) [62], electron–ion interaction pseudo-potentials of trinucleotide (PseEIIP) [59] and dinucleotide-based auto-covariance (DAC) [66]; and (iii) position-specific scoring matrices, such as bi-profile Bayes (BPB) [66], position-correlation scoring function (PCSF) [31] and position-specific trinucleotide propensity based on single-stranded or double-stranded characteristic of DNA (PSTNP_SS/PSTNP_DS) [69]. Among these features, k-mer composition score, which describes the k-length substring of all possible combinations of A, C, G and T, has been extensively used in computational biology [155–157]. Most of the features above can be easily calculated by state-of-the-art bioinformatics pipelines, such as Pse-in-One [158], iFeature [159], iLearn [160] and iLearnPlus [161]. Compared to earlier published models for prokaryotic promoters (i.e. before 2010), which tended to use signal- or structure-based feature encoding schemes rather than context-based features, a majority of machine learning–based methods for prokaryotic promoter after 2010 favored context-based features for identifying promoter sequences. On the other hand, context-based features have been constantly the widely applied feature type for eukaryotic promoter prediction.

Structure-based features

Structure-based features are calculated based on the DNA 3D structures that characterize promoters [162]. It has been reported that the local changes of structural features around the classical TATA box and the TSS are contributive to differentiating the promoters from non-promoter sequences [112]. In light of this, several structure-based features have been introduced and demonstrated to be effective in boosting the prediction performance for both scoring function–based and traditional machine learning–based prokaryotic and eukaryotic promoter predictors. There are four major types of structure-based features, including (i) DNA curvature, which describes the extent of the DNA deviation due to the interaction of adjacent base pairs [163]; (ii) DNA bendability that describes the anisotropic bending of duplex DNA, which is closely related to DNase I cutting [164]; (iii) DNA duplex stability (DDS), which is used to describe the ability of DNA to open up or melt, depending on its hydrogen bonding and base pair stacking [56, 126]; (iv) stress-induced DNA duplex destabilization (SIDD), which is the required incremental free energy (kcal/mole) to keep the base pair open under the assumed superhelicity [40, 44]; and (v) G-quadruplex, which is a DNA secondary structure motif consisting of multiple vertically stacked guanine tetrads [71, 164].

Integrated features

Rather than using the single type of features described above, many promoter predictors combined different types of features [165], seeking to further improve the prediction performance. For example, by combining signal-, context- and structure-based features using decision trees, SCS [117] reached the highest sensitivity and specificity, while bTSSfinder [57] used oligomer frequencies, promoter elements and physicochemical properties to describe bacterial promoter regions.

Predictive algorithms employed

There are three types of major prediction algorithms applied in prokaryotic and eukaryotic promoter prediction (Figure 1 and Tables 1–3), including (i) scoring function–based algorithms, such as Position-Correlation Scoring Function (PCSF) [43], Modified Mahalanobis Discriminant (Modified MD) [134], Positional Weight Matrix (PWM) [34, 45, 54, 105] and Relative Stability (DE) [50, 126]; (ii) traditional machine learning–based algorithms, such as Support Vector Machine (SVM) [33, 55, 60, 65, 66, 123], Fisher’s Linear Discriminant Analysis (LDA) [40, 78, 88, 104], Logistic Regression (LR) [64], Random Forest (RF) [58], Naive Bayes Classifier (NBC) [95], Decision Tree (DT) [44] and Artificial Neural Network (ANN) [39, 48, 57, 76, 133]; and (iii) deep learning–based frameworks such as Convolutional Neural Network (CNN) [30, 32, 67, 127, 129].

Scoring function–based approaches

The statistical methods applied in the scoring function–based approaches [34, 43, 50, 134] are straightforward and simplified, such as sequence similarity and consensus patterns derived from the training data (i.e. the validated promoter sequences). Consequently, scoring function–based methods are particularly efficient for the identification of promoter sequences in a high-throughput manner. The mathematical descriptions of the commonly used scoring function–based algorithms are provided in the Supplementary Methods, available online at http://bib.oxfordjournals.org/.

Traditional machine learning–based algorithms

Among the surveyed predictors in this study, SVM, LDA and ANN are the most extensively used machine learning algorithms (Figure 3C). Previously, Monteiro and da Silva et al. [36, 38] only reported an empirical comparison of machine learning techniques for prokaryotic promoter prediction, including NBC, DT, SVM and ANN. According to the performance comparison, SVM outperformed other compared algorithms on an independent test dataset from B. subtilis. In addition to the individual machine learning techniques, ensemble strategies have also been successfully applied in both DNA (prokaryotic and eukaryotic) and noncoding RNA promoter prediction [63, 69, 166], as well as mRNA subcellular localization prediction [167]. For example, a feature subspace ensemble involving three different classifiers (i.e. SVM, LDA and LR) was applied to develop iPromoter-FSEn [63] for the identification of E. coli σ⁷⁰-promoter sequences. Li et al. [69] constructed the stacking ensemble-learning model, namely SELECTOR, for the identification of promoters in E. coli by employing five popular individual and ensemble-learning algorithms, including RF, AdaBoost [168], GBDT [169], XGBoost [170] and LightGBM [171].

Deep learning–based approaches

Evolving from traditional ANN frameworks, deep learning techniques have been reported to achieve significantly boosted prediction performance across a variety of research fields, including natural language processing [172], drug design [173] and medical image analysis [174]. Among the different deep learning techniques, convolutional neural network (CNN) has drawn significant attention and has been widely applied to both prokaryotic and eukaryotic promoter prediction. A number of predictors have been developed using CNN and CNN-based deep learning techniques [30, 32, 67, 70, 127, 129] in combination with other strategies, such as bidirectional long short-term memory (BiLSTM) [175].

Strategies and measures for performance assessment

K-fold cross-validation (CV) test, jackknife validation test (i.e. leave-one-out CV) and independent test have been used to evaluate the prediction performance for the predictors surveyed in this study (Tables 1–3). Among these performance evaluation strategies, k-fold CV (k = 5 or 10) is the most frequently used strategy to construct performance matrices. In the k-fold CV test, the dataset is randomly split into k equally sized subsets. Each of the subsets is used as a test dataset once to evaluate the performance of the predictor trained by the remaining k − 1 subsets. The training and testing procedures are then conducted k times, and the average performance of the k combinations is usually reported. The jackknife test, on the other hand, is considered as a special case of k-fold CV, where k is set to the number of samples in the dataset. To make an objective and effective assessment and comparison with previous existing methods, the independent test is usually implemented using a newly assembled dataset with no overlap or low similarity with the sequences in the training datasets of compared predictors. A variety of widely applied performance measures in the field of bioinformatics [176–178], including area under the curve (AUC), sensitivity, specificity, accuracy (Acc), Matthew’s correlation coefficient [179] (MCC), precision and F1 score, have been used to quantitatively estimate the prediction performance. These measures are defined as follows:

where TP, TN, FP and FN denote the numbers of true positives, true negatives, false positives and false negatives, respectively.

Webserver/software availability and usability

A user-friendly webserver and/or a locally executable tool can significantly facilitate the use of the proposed prediction tool for both prokaryotic and eukaryotic promoters. In total, 59.4% (63 out of 106) of the surveyed predictors have available websites/tools and 46.0% (29 out of 63) of these are still active (Figure 3D). Based on our investigation of the predictors for high-throughput prokaryotic promoter prediction in this study, 24 predictors out of 40 (60.0%) were implemented with webservers and/or stand-alone software (Table 1). For eukaryotic promoter prediction (Table 2), 37 out of 61 (60.7%) predictors provide webservers and/or stand-alone program; however, more than half unfortunately are offline. Among the predictors that can predict both prokaryotic and eukaryotic promoters, iProEP and CNNProm provide executable webservers.

All the webservers mentioned above allow users to submit their sequences of interest in the FASTA format. However, certain servers have some special requirements in terms of number and length of the submitted sequences. For example, BacPP only allows up to 2000 characters of input sequences; the minimum length of submitted sequences is 1001 bp for PromPredict; iPSW(2L)-PseKNC only allows no more than 100 sequences submitted each time; the allowed length for each sequence is 251–100 000 bps for TSSPlant; DeePromoter and iProEP allow fixed 300 bp of each sequence, while CNNProm allows for submitted sequences with 251 bp. Most prokaryotic promoter predictors require fixed 81 bp long sequences. In particular, BacPP was designed to analyze sequences with 80 nucleotides. Longer sequences will be sliced by a sliding window for further processing.

A well-designed output format is critical for users to understand and interpret the prediction results. Most servers display the instantaneous prediction results on their webpages. MULTiPly, SELECTOR and Depicter also allow users to retrieve previous prediction results using the assigned job IDs. The output generally contains predicted labels of query sequences (e.g. promoter or non-promoter). Some methods such as iPro70-PseZNC, iPro70-FMWin, iPromoter-FSEn, iProEP, CNNProm, NNPP2.2 and SELECTOR offer the probability scores of promoters or non-promoters as well. TSSPlant provides predicted positions and scores of TSS and TATA box, while NNPP2.2 and PromPredict can identify more than one promoter sequence with the start and end positions.

Experimental results

Performance comparison of prokaryotic promoter prediction tools

Three balanced independent test datasets were used to assess the prediction performance of the predictors for prokaryotic promoter, including E. coli-I (i.e. for Inline graphic promoters), E. coli-II (i.e. for general E. coli promoters) and B. subtilis (i.e. for general B. subtilis promoters). See ‘Construction of independent test datasets’ for the detailed dataset construction procedures. Note that only predictors with available webservers/tools were assessed (bolded in Tables 1 and 3). The prediction performance is shown in Figure 4 and Supplementary Table 1A available online at http://bib.oxfordjournals.org/. Among the available predictors, iPro70-PseZNC, 70ProPred, iPromoter-FSEn and iPro70-FMWin were designed for σ⁷⁰ promoters only while iProEP was implemented for generic prokaryotic promoters. The Acc, AUC and MCC values for σ⁷⁰ promoters are shown in Figure 4A. iPro70-FMWin outperformed all the predictors by achieving Acc of 86.46%, AUC of 0.936 and MCC of 0.733. For prediction of general E. coli promoters, eight available predictors were employed, including BacPP, iPSW(2L)-PseKNC, iPromoter-2L, MULTiPly, iPromoter-2L2.0, SELECTOR, iProEP and CNNProm. Among these methods, iPSW(2L)-PseKNC achieved the best prediction performance in terms of all the measures except specificity and precision (Figure 4B and Supplementary Table 1A available online at http://bib.oxfordjournals.org/). In addition, Le et al. and iPSW(2L)-PseKNC further categorized the predicted promoters into two types, namely ‘strong’ and ‘weak’, which is a unique feature of the two methods compared to others. For B. subtilis, there is no available specific computational pipeline, and we therefore assessed the prediction performance of two general predictors, including iProEP and CNNProm (Figure 4C and Supplementary Table 1A available online at http://bib.oxfordjournals.org/). Despite that iProEP achieved higher AUC (0.828), CNNProm demonstrated its prediction ability by higher Acc (77.03%) and MCC (0.557). In addition, we plotted two heatmaps to illustrate the precision and F1 score of all the compared predictors using the three datasets (Figure 4E and F). iProEP is the only predictor that is applicable to all the three datasets. While iProEP has not been ranked as the top predictor, it has achieved satisfactory prediction performance on all the three datasets and therefore may be a useful tool to classify promoter sequences with unknown species or promoter type. When evaluated on the unbalanced independent test datasets for E. coli-I (i.e. for Inline graphic promoters), iPro70-FMWin constantly outperformed all other predictors. The prediction performance is shown in Supplementary Table 1B–E available online at http://bib.oxfordjournals.org/. For general E. coli promoter prediction, iProEP and CNNProm performed exceptionally well in terms of specificity and precision. While for B. subtilis, iProEP achieved higher specificity and precision while CNNProm obtained higher Acc and MCC. As shown in Figure 4 and Supplementary Table 1 available online at http://bib.oxfordjournals.org/, deep learning–based methods have not achieved satisfactory prediction performance for prokaryotic promoters. One possible reason is that the datasets used for training and testing the prokaryotic promoter predictors are very limited, while deep learning models usually require a larger volume of training samples to enable the construction of reliable and accurate models. In contrast, traditional machine learning–based methods developed based on handcrafted features can competently deal with small datasets and can achieve more robust performance.

Prediction performance of prokaryotic promoter predictors on three independent test datasets, including *E. coli*-I, *E. coli-*II and *B. subtilis* in terms of Acc, AUC and MCC for (A) *E. coli* promoters (*E. coli*-I dataset), (B) *E. coli* promoters (*E. coli*-II dataset) and (C) *B. subtilis* promoters (*B. subtilis* dataset). Two heatmaps were plotted to illustrate (D) the Precision and (E) the F1 scores of assessed predictors using all the three datasets. The gray grids in the heatmaps (D) and (E) mean the current method is non-applicable to this dataset.

Inline graphic — Prediction performance of prokaryotic promoter predictors on three independent test datasets, including *E. coli*-I, *E. coli-*II and *B. subtilis* in terms of Acc, AUC and MCC for (A) *E. coli* promoters (*E. coli*-I dataset), (B) *E. coli* promoters (*E. coli*-II dataset) and (C) *B. subtilis* promoters (*B. subtilis* dataset). Two heatmaps were plotted to illustrate (D) the Precision and (E) the F1 scores of assessed predictors using all the three datasets. The gray grids in the heatmaps (D) and (E) mean the current method is non-applicable to this dataset.

Performance comparison of eukaryotic promoter prediction tools

Ten balanced independent test datasets were constructed for two major types of eukaryotic promoters (i.e. TATA-containing and TATA-less) for five species including A. thaliana, Z. mays, D. melanogaster, R. norvegicus and H. sapiens. In total, eight available predictors (bolded in Tables 2 and 3) were benchmarked using these datasets. The prediction results are illustrated in Figure 5 and Supplementary Tables 2A, 3, 4A, 5A and 6A available online at http://bib.oxfordjournals.org/. Note that different methods request different lengths of submitted sequences. There are three major lengths of submitted sequences, including 251 bp ([−200, +50]), 300 bp ([−249, +50]) and 1001 bp ([−500, +500]). For these methods, we submitted the promoter sequences with different lengths of extended upstream and downstream of TSS.

Prediction performance of different predictors on eight eukaryotic promoter independent test datasets in terms of AUC, Acc and MCC on the (A) *A. thaliana*, (B) *D. melanogaster*, (C) *R. norvegicus* and (D) *H. sapiens* datasets. In addition, two heatmaps demonstrate the (E) F1 and (F) Precision scores of all the compared predictors on these datasets. The gray grids in the heatmaps (E) and (F) mean the current method is non-applicable to this dataset.

Five available tools, including CNNProm, TSSPlant, NNPP2.2, Depicter and PromPredict (2018 version), were designed for recognition of A. thaliana promoters. These predictors allow submissions with 251, 300 and 1001 bp sequence extension flanking the TSS. Due to the unexpected unavailability of the CNNProm server, we were unable to include it in the performance comparison on the unbalanced datasets. As shown in Figure 5A, E, F and Supplementary Table 2A available online at http://bib.oxfordjournals.org/, Depicter achieved the best prediction performance for predicting both TATA-containing (MCC of 0.977; F1 of 0.989) and TATA-less promoters (MCC of 0.876; F1 of 0.937). CNNProm was ranked the second-best predictor based on the performance measures. Note that both Depicter and CNNProm are deep learning–based approaches and the prediction results have clearly demonstrated the improved performance powered by deep learning techniques, compared to scoring function–based and traditional machine learning–based approaches. Tested on the unbalanced independent test datasets of A. thaliana, similar performance results described above can be observed in the Supplementary Table 2B–E available online at http://bib.oxfordjournals.org/. We further examined the methods designed for recognition of A. thaliana promoters using the additional test dataset of monocot plant (i.e. Z. mays), including CNNProm, TSSPlant, NNPP2.2, Depicter and PromPredict. The predictive performance is provided in Supplementary Table 3 available online at http://bib.oxfordjournals.org/. It can be seen that for the TATA-containing promoters, Depicter achieved optimal performance metrics except sensitivity, while for the TATA-less promoters, TSSPlant performed overall best with MCC (0.442) and F1 (0.730).

NNPP2.2, iProEP, Depicter and PromPredict were benchmarked for performance comparison on D. melanogaster promoters (Figure 5B, E, F and Supplementary Table 4A available online at http://bib.oxfordjournals.org/). Depicter again outperformed other baselines by achieving AUC of 0.993, MCC of 0.962 and Acc of 98.07% for TATA-containing promoter and AUC of 0.963, MCC of 0.909 and Acc of 95.40% for TATA-less promoter, respectively. It is also noticeable that iProEP was ranked as the second-best predictor, outperforming both NNPP2.2 and PromPredict. The prediction results suggest that using only DNA structural properties or signal-based features is not sufficient to accurately predict promoters. Rather, the addition of context-based features such as k-mer and PseKNC can assist the predictor to obtain more accurate and reliable prediction results. The difference in the context between promoters and non-promoters (from coding regions) may be the main reason for this result. Tested on the unbalanced independent test datasets of D. melanogaster, iProEP achieved higher specificity and precision for TATA-containing promoter and outperformed the other tools in terms of all the measures except sensitivity with the increase of negative samples (Supplementary Table 4B–E available online at http://bib.oxfordjournals.org/).

CNNProm, NNPP2.2, DeePromoter, Depicter and PromPredict can be used to predict M. musculus promoters. However, there has not been a major update for M. musculus promoters in EPDnew and we therefore could not assemble an independent test dataset from M. musculus. Instead, we generated two promoter datasets (i.e. TATA-containing and TATA-less) from R. norvegicus sequences documented in EPDnew (see ‘Construction of independent test datasets’). In general, CNNProm outperformed other predictors for R. norvegicus TATA-containing promoters more accurately with MCC of 0.895 and F1 of 0.946 (Figure 5C, E, F and Supplementary Table 5A available online at http://bib.oxfordjournals.org/). While for R. norvegicus TATA-less promoters, Depicter achieved the highest MCC of 0.738 and F1 of 0.874, and CNNProm achieved highest specificity (0.891) and precision (0.883). CNNProm and Depicter still performed well, separately for TATA-containing and TATA-less promoters on the unbalanced independent test datasets of R. norvegicus (Supplementary Table 5B–E available online at http://bib.oxfordjournals.org/). The prediction results suggest that these predictors can be reliably applied to R. norvegicus sequences, despite that they were trained using the sequences from M. musculus.

Seven predictors were used to assess the performance on H. sapiens promoters. As shown in Figure 5D, E, F and Supplementary Table 6A available online at http://bib. oxfordjournals.org/, Depicter again achieved the highest AUC (0.972), Acc (93.01%) and MCC (0.862) for TATA-containing promoters. For TATA-less promoters, Depicter achieved highest Acc and MCC values, while iProEP attained the highest AUC (0.859). CNNProm achieved satisfactory prediction performance for TATA-containing promoters, but unfortunately it cannot process TATA-less promoter sequences. Interestingly, NNPP2.2, DeePromoter and PromPredict achieved low specificity values, suggesting that they tend to misclassify the non-promoters as promoters, thereby leading to high false positive rate. iProEP performed best in terms of all measures except for sensitivity for TATA-less promoters (Supplementary Table 6B–E available online at http://bib.oxfordjournals.org/) on the unbalanced independent test datasets of H. sapiens.

Among the available eukaryotic promoter prediction tools, EP3 and PromPredict are able to recognize promoters on the whole-genome scale with rapid computing speed. Thus, we compared these two methods on the task of human chromosome 22 genome promoter prediction. The prediction performance was evaluated with the reference to the annotated TSS set of the CAGE dataset. Specifically, we set the maximum allowed mismatch values as 2000, 1000 and 500 bp used in EP3, between the prediction and the true TSS. If a predicted promoter region is within the maximum allowed mismatch from a true TSS, it is then regarded as a true TSS hit (i.e. true positive, TP). If a region contains a true TSS but is not predicted as a promoter region, it is then regarded as a false negative (FN). If a predicted promoter region is further than the maximum allowable distance from a true TSS, it is then considered as a false positive (FP). The performance of EP3 and PromPredict in terms of sensitivity, precision and F1 score based on the three maximum allowed mismatch thresholds is shown in Supplementary Figure 1 available online at http://bib.oxfordjournals.org/. For each maximum allowed mismatch threshold, PromPredict achieved a considerably high sensitivity but low precision, thereby leading to a low F1 score. On the contrary, EP3 showed a high precision and low sensitivity, but its balance of precision and sensitivity appeared to be better than PromPredict.

Discussion

Promoters play critical roles in regulating DNA transcription, and precise recognition of promoter regions can therefore assist biologists to further improve genome annotation and guide experimental design for elucidation of gene transcriptional regulation mechanisms. Benefiting from the advances of sequencing techniques, more experimentally validated species-specific promoter sequences have become publicly available, thereby providing reliable training data to construct computational tools for predicting prokaryotic and eukaryotic promoters. Before 2012, most of the computational studies for promoter prediction focused on eukaryotic cells, while more tools for prokaryotic promoter prediction were published after 2012, due to the rapid accumulation of prokaryotic sequencing data (Figure 3A). Earlier predictive models for promoter prediction are usually based on scoring functions, which are trained using the underlying sequence patterns of known promoters. Compared to traditional machine learning and deep learning techniques, scoring function–based approaches usually predict rapidly with lower prediction performance. Despite the recent efforts, there is large room for in silico methods to further improve their prediction performance. Almost all current available methods are species-specific. As such, they are trained only on one species and can predict promoters for that particular species. In addition, many state-of-the-art tools limit the length of the test sequence. We also note that the webservers of a number of promoter predictors developed in the past two decades are not well maintained, as shown in Figure 3D. This has significantly impacted their usability and applicability. As user friendly and publicly accessible webservers represent the future direction for developing practically useful models, it is highly encouraged that source codes and webservers can be publicly accessible to ensure the best practice of data reproducibility. For eukaryotic promoter prediction, some models [32, 88, 100, 129] divided the eukaryotic promoters into TATA-containing promoters and TATA-less promoters based on the presence of core promoter elements, i.e. TATA box. Other classes of promoter elements such as Inr [9], TCT [152] and MTE [153] are also significantly conserved in eukaryotes; however, there is a lack of computational tools for such classes of eukaryotic promoters.

There are three major prediction strategies applied to the promoter identification, namely, scoring function, traditional machine learning–based techniques and deep learning–based techniques. Based on the independent tests using 58 datasets, deep learning–based models, which usually require more time and computational resource to train, have achieved outstanding prediction performance, while scoring function–based methods, which are straightforward to implement, usually demonstrate less desirable performance. Traditional machine learning–based methods, on the other hand, balance the computational burden and algorithm complexity. It is therefore at developers’ discretion to determine the most cost-effective prediction strategy, based on the training data. Scoring function–based tools tend to predict a number of false positives or have poor sensitivity when being applied to predefined promoter sequences. Nevertheless, the prediction outputs by these methods can still be helpful for drawing some meaningful biological conclusions. For example, PromPredict can identify predicted regions that have lower stability, higher curvature and less bendability, which favors transcription initiation (which may act as promoters). DDM finds that on average more than 40% of the human genome can be highly unlikely to initiate transcription based on the analysis of human chromosomes 4, 21 and 22. While Wang et al.’s work shows that the propensity for SIDD is closely associated with specific promoter regions. The features used to represent the promoter sequences are another important performance-determining factor for both traditional machine learning– and deep learning–based approaches. We summarized the major feature types of the surveyed predictors, namely signal-, context-, structure-based and integrated features. Comparing to individual feature type, combining all the three types of features (i.e. integrated features) can significantly improve the prediction performance. For example, SCS [117] combining signal, context and structure features by the decision trees reached the highest sensitivity and specificity. On the other hand, it is possible that the dimensionality of calculated feature vectors is high and feature selection methods should be therefore widely applied to reduce the dimensionality by filtering out the misleading features, thereby further improving the prediction performance [62, 64, 67]. Another problem that needs to be considered is the unbalanced amount of positive and negative sequences, as the non-promoter sequences significantly outnumber the promoter sequences. To build a balanced classifier, it is practical to randomly select a pool of negative sequences with the same numbers of positive sequences. Ideally, such random selection should be repeated multiple times, and the average results should be reported. In addition, various effective resampling algorithms such as Safe-Level-SMOTE [180] oversampling, and most distant undersampling techniques, have been developed to address the data imbalanced problem [181]. Therefore, such methods can be considered in the model construction when the datasets are significantly unbalanced.

Conclusion

In this study, we have surveyed 106 state-of-the-art computational approaches for predicting prokaryotic and eukaryotic promoter sequences and benchmarked 19 predictors with available and functioning webservers/local tools. To our best knowledge, our study represents the most comprehensive and large-scale benchmarking test of promoter predictors. A wide range of aspects in detail were summarized, including employed algorithms, calculated features, performance evaluation strategies and software usability. By curating 58 independent test datasets for various species and promoter types, we extensively benchmarked 19 available predictors and demonstrated their prediction performance. The prediction results show that traditional machine learning–based and deep learning–based methods generally outperform scoring function–based methods for predicting species-specific promoters. One should bear in mind that the performance of the surveyed predictors is subject to change in cases where different test datasets and new data are used to train the models [182]. In addition, we have discussed several issues and provided some insights to potentially useful strategies that may be used to further enhance the performance of promoter prediction. We expect this analysis to be a steppingstone toward design and implementation of more accurate predictors for both prokaryotic and eukaryotic promoters in the future.

Data Availability

All the 58 independent test datasets used for benchmarking various predictors in this study are available at https://github.com/chenli-bioinfo/promoter/.

Author Contributions

J.S., L.J.M.C. and Q.Z. conceived and designed the project. M.Z., C.J., F.L. and C.L. conducted the data analysis and independent tests and drafted the manuscript. T.A. and G.I.W. provided critical comments and useful insight to improve the scientific quality of the work. Q.Z., L.J.M.C. and J.S. revised the manuscript, which has been approved by all the other authors.

Key Points

We curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters.
We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both).
We found that deep learning and traditional machine learning–based approaches generally outperformed scoring function–based approaches.
We have assessed the prediction performance of 19 available prokaryotic and eukaryotic promoter predictors using our newly curated 58 benchmark datasets.

Supplementary Material

Supplementary_bbab551

Click here for additional data file.^{(169.8KB, docx)}

Meng Zhang received her MS degree from Dalian Maritime University, China. She is currently a PhD student in Nanjing University of Aeronautics and Astronautics. Her research interests are bioinformatics, computational biology and machine learning.

Cangzhi Jia is an associate professor in the College of Science, Dalian Maritime University. She obtained her PhD degree in the School of Mathematical Sciences from Dalian University of Technology in 2007. Her major research interests include mathematical modeling in bioinformatics and machine learning.

Fuyi Li is currently a Bioinformatics Research Officer in the Department of Microbiology and Immunology, the Peter Doherty Institute for Infection and Immunity, the University of Melbourne, Australia. His research interests are bioinformatics, computational biology, machine learning and data mining.

Chen Li is a Research Fellow in the Biomedicine Discovery Institute and Department of Biochemistry of Molecular Biology, Monash University. He is currently a CJ Martin Early Career Research Fellow, supported by Australian National Health and Medicine Research Council (NHMRC). His research interests include systems proteomics, immunopeptidomics, personalized medicine and experimental bioinformatics.

Yan Zhu is currently pursuing her MS degree in the School of Science, Dalian Maritime University, China. Her research interests are bioinformatics, deep learning and machine learning.

Tatsuya Akutsu has been a professor in Bioinformatics Center, Institute for Chemical Research, Kyoto University since 2001. He obtained Dr Eng. degree from University of Tokyo in 1989. His research interests include bioinformatics, complex networks and discrete algorithms.

Geoffrey I. Webb received his PhD degree in Computer Science in 1987 from La Trobe University. He is the research director of the Monash Data Futures Institute and a professor in the Faculty of Information Technology at Monash University. His research interests include machine learning, data mining, computational biology and user modeling.

Quan Zou is a professor at University of Electronic Science and Technology of China. He is a senior member of IEEE and ACM. His research interests include bioinformatics, machine learning and algorithms.

Lachlan J.M. Coin is a professor and group leader in the Department of Microbiology and Immunology at the University of Melbourne. He is also a member of the Department of Clinical Pathology, University of Melbourne. His research interests are bioinformatics, machine learning, transcriptomics and genomics.

Jiangning Song is an associate professor and group leader in the Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia. He is also affiliated with the Monash Centre of Data Science, Faculty of Information Technology, Monash University. His research interests include bioinformatics, computational biology, machine learning, data mining and pattern recognition.

Contributor Information

Meng Zhang, School of Science, Dalian Maritime University, Dalian 116026, China.

Cangzhi Jia, School of Science, Dalian Maritime University, Dalian 116026, China.

Fuyi Li, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia; The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC, Australia.

Chen Li, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.

Yan Zhu, School of Science, Dalian Maritime University, Dalian 116026, China.

Tatsuya Akutsu, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan.

Geoffrey I Webb, Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC 3800, Australia; Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia.

Quan Zou, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.

Lachlan J M Coin, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC, Australia.

Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia; Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia.

Funding

National Natural Science Foundation of China (62071079), the National Health and Medical Research Council of Australia (NHMRC) (APP1127948 and APP1144652), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965), a Major Inter-Disciplinary Research project awarded by Monash University, and the collaborative research program of the Institute for Chemical Research, Kyoto University (#2021-28). J.S. is supported by the JSPS Invitational Fellowship (L21503). L.J.M.C. is supported by an NHMRC career development fellowship (1103384), as well as an NHMRC-EU project grant GNT1195743. C.L. is currently supported by an NHMRC CJ Martin Early Career Research Fellowship (1143366).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary_bbab551

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Data Availability Statement

All the 58 independent test datasets used for benchmarking various predictors in this study are available at https://github.com/chenli-bioinfo/promoter/.

PERMALINK

Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction

Meng Zhang

Cangzhi Jia

Fuyi Li

Chen Li

Yan Zhu

Tatsuya Akutsu

Geoffrey I Webb

Quan Zou

Lachlan J M Coin

Jiangning Song

Abstract

Introduction

Figure 1.

Systematic comparison of computational approaches for prokaryotic and eukaryotic promoter prediction

Existing approaches for prokaryotic and eukaryotic promoter prediction

Table 1.

Table 2.

Table 3.

Figure 2.

Figure 3.

Construction of training datasets

Table 4.

Construction of independent test datasets

Table 5.

Table 6.

Feature engineering and representation

Signal-based features

Context-based features

Structure-based features

Integrated features

Predictive algorithms employed

Scoring function–based approaches

Traditional machine learning–based algorithms

Deep learning–based approaches

Strategies and measures for performance assessment

Webserver/software availability and usability

Experimental results

Performance comparison of prokaryotic promoter prediction tools

Figure 4.

Performance comparison of eukaryotic promoter prediction tools

Figure 5.

Discussion

Conclusion

Data Availability

Author Contributions

Key Points

Supplementary Material

Contributor Information

Funding

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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