Nucleic Acids Research Advance Access originally published online on December 14, 2006
Nucleic Acids Research 2007 35(2):e12; doi:10.1093/nar/gkl1024
Nucleic Acids Research, 2007, Vol. 35, No. 2 e12
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts
Scott Mann1,
Jinyan Li3 and
Yi-Ping Phoebe Chen1,2,*
1 School of Engineering and Information Technology, Deakin University Victoria, Australia
2 Australian Research Council Centre in Bioinformatics Melbourne, Australia
3 Institute for Infocomm Research Singapore 119613
*To whom correspondence should be addressed. Tel: +61 3 92517684; Fax: + 61 3 92517604; Email: phoebe{at}deakin.edu.au
Received September 13, 2006. Revised October 25, 2006. Accepted November 14, 2006.
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ABSTRACT
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The computational approach for identifying promoters on increasingly
large genomic sequences has led to many false positives. The
biological significance of promoter identification lies in the
ability to locate true promoters with and without prior sequence
contextual knowledge. Prior approaches to promoter modelling
have involved artificial neural networks (ANNs) or hidden Markov
models (HMMs), each producing adequate results on small scale
identification tasks, i.e. narrow upstream regions. In this
work, we present an architecture to support prokaryote promoter
identification on large scale genomic sequences, i.e. not limited
to narrow upstream regions. The significant contribution involved
the hybrid formed via aggregation of the profile HMM with the
ANN, via Viterbi scoring optimizations. The benefit obtained
using this architecture includes the modelling ability of the
profile HMM with the ability of the ANN to associate elements
composing the promoter. We present the high effectiveness of
the hybrid approach in comparison to profile HMMs and ANNs when
used separately. The contribution of Viterbi optimizations is
also highlighted for supporting the hybrid architecture in which
gains in sensitivity (+0.3), specificity (+0.65) and precision
(+0.54) are achieved over existing approaches.
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INTRODUCTION
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Motif identification in biological sequences is a common and
growing task in bioinformatics, arising from ever growing numbers
of genomic sequences. Specific motif identification tasks such
as in this case promoter element recognition, enable context
to be placed on raw sequences and aid knowledge discovery through
autonomous sequence annotation. The problem specific to promoter
identification centres on the ability to recognize consensus
sequences often of short length, in large context unaware background
sequences. In this manner a two-tiered issue arises, namely,
sequence deviation from consensus and the shortness of signal
relative to the background. The biological reality of promoter
structure imposes these challenges, to which many computational
techniques have attempted to answer.
The promoter is the key regulatory region which enables gene transcription. Its composition is dependent upon the taxonomic classification of the organism and the category of the gene under its control. Whilst conservation does exist broadly in promoters i.e. prokaryotic –35 and –10 boxes and the TATA box in eukaryotes, significant variability exists, including the absence of these sequences. Such sequences and associated upstream elements are very short and are easily lost when analysing large regions of nucleotides without prior context knowledge. In a study conducted by (1), a conclusion was drawn which stated that only 20% of the known promoters had scoring above the false positives for a 0.1 Mb genomic sequence. The question of search domain arises whereby 90.31% of sigma 70 promoters fall within 250 bp of the transcription start site (TSS) (2). These authors using a weight matrix approach and 250 bp window upstream from the TSS concluded that >50% of promoter sequences had false signals (promoter like) that score better than the true promoter. The –35 and –10 signal detection yielded 38 signals per 250 bp using 3 SDs. The discovery of so many potential RNA polymerase (RNAP) binding signals would suggest promoter degeneracy over evolutionary time due to mutations which lead to up and down effects on the promoter strength. Alternatively, another hypothesis for the prevalence of false signals was proposed by (3) in which the multitude of candidate promoters serve in negative competition or positively towards the channelling of the RNAP to the promoter.
In our study, such an accessory ‘up’ element was used. Other such elements exist, including the process whereby a false promoter element attracts the RNAP to the true promoter for the RNAP to then be relocated by an activator as in the CRP–MalT system (4).
Techniques to achieve promoter identification typically rely on the detection of consensus motifs. This scheme works well when identifying highly conserved motifs in short biological background sequences, however, motifs quickly get lost and produce many false positives in larger background sequences. The most effective use of consensus identification is toward seeking relatively large motifs, e.g. CpG islands, as by the classical hidden Markov models (HMMs) (5).
HMMs are statistical models based on a double stochastic process with hidden underlying states and observable emissions. The purpose of this model is to represent a motif in a stochastic framework which is then used for the classification of candidate sequences. Many applications in bioinformatics have benefited from HMMs, including binding site prediction (6), protein families and homology modelling (7), conserved sequence recognition (8), gene finding (9) and alignment (10). Another approach to motif identification involves artificial neural networks (ANNs). An ANN is a graph which is composed of computational elements that are loosely designed to model the human nervous system. ANNs have generally been applied to the identification of regulatory sequences (11–15). The purpose of this technique is to form associations in the data under analysis, which can be used to model motifs in situ.
To address the situation of incorporating additional neighbouring biological information into computational models, an effective motif extraction technique, such as the HMM, needs its results placed in a biological context, as can be achieved by the ANN. By filtering HMM results via an ANN, the false positives obtained by an HMM-only scheme can be reduced. This approach effectively builds a profile for the regulatory region using two of the most powerful pattern searching techniques available. The stimuli for this hybrid approach is founded in the properties of each model. Profile HMMs (5) model biological sequences and account for variation, whilst ANNs learn association between entities, in this case, promoter elements. Using these properties, it is logical to model and associate promoter elements to form a prediction algorithm. The first such use of HMM/ANN hybrids has been towards speech processing (16). In this case, the ANN applied contextual knowledge to the raw HMM scores. Hybrid architectures that are composed of HMMs and ANNs have been used in bioinformatics recently. Such applications involve (17), whereby an ANN is used primarily for eukaryote secondary structure prediction with a HMM then applied to filter the results. This is inverse to what our process involves. When compared with other techniques in the field of secondary structure prediction, such an approach meets current performance levels. Using an architecture similar to the model developed by the authors of this study, prediction of G-protein coupled with receptor specificity has been achieved (18). The reported finding indicates a 94% classification rate. Another application of the hybrid approach included (19), whereby an NN-HMM-ENSEMBLE is combined to predict all-alpha proteins with prediction accuracy better by 7–9% than other techniques.
The unified hybrid model developed by the authors for the purpose of promoter element recognition has proved a concept thematic in n-motif functional region identification. The ability to model composition variability and motif relative location was enabled by utilizing the attributed of pHMMs (motif composition) and ANNs (locality validation). The resultant property was a model that could identify motifs and further justify their predicted score by incorporating their positional arrangement. Performance outcomes involve improvements of sensitivity (+0.3), specificity (+0.65) and precision (+0.54) over existing techniques (20,21).
In this study, both pHMMs and ANNs are shown to be ineffective toward genomic promoter identification, therefore highlighting the need for an improved model. False positives and narrow signal detection in short backgrounds are the key motivations taken by the authors. The result is the development of an architecture for the integration of these two approaches, whereby the performances of either approach is extended through novel integration strategies. By combining the modelling and scoring optimizations of the pHMM with the associative power of ANN's, this architecture will more accurately represent the motifs under consideration in genomic contexts. The architecture of our model is comprised of a three-layer feed-forward neural network with a pHMM acting as the input neurons via a Viterbi scoring determination which is shown to provide more discriminative pHMM scoring optimization. The outcome is a hybrid model capable of locating promoters in large sequence backgrounds a goal which neither pHMM nor ANN can achieve when used in isolation.
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MATERIALS AND METHODS
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Combining the pHMM and ANN architectures to accomplish genomic
scale promoter identification is the function of the hybrid
architecture. The first stage in modelling specific regulatory
regions involves pHMM construction and training. The promoter
regions of prokaryote ‘housekeeping’ genes were
used as an example system. The composition of such regions includes
the –35 and –10 promoter elements. To incorporate
such elements into the hybrid architecture, each conserved sequence
requires modelling via a pHMM. Each pHMM is trained using 100
promoter element annotated
Escherichia coli –10 and –35
signals, respectively, obtained from NCBI GenBank (
22) which
are aligned using ClustalW (
23). These sequences were obtained
using the query ‘–10_SIGNAL AND
E.coli [ORGANISM]
NOT PLASMID’ and then filtered to remove insertion sequences,
therefore focusing on native chromosomal promoters (Supplementary
Data ‘Core Promoter Data’). The outcome of the data
mining task results in two profiles representing the –35
and –10 core signals respectively via pHMMs. Scoring follows
a sliding window of size 6 bp for each position in the sequence.
Numeric scores from the pHMMs are sent to the input layer of
a three-layer feed-forward neural network which uses a sigmoidal
transfer function as described below:
 | (1) |
where
v represents the Viterbi score for the
final state
k in the optimal path
though the pHMM for the candidate
sequence
x = (1...
N). The requirement of integrating two disparate
models falls upon the prerequisite modelling foundations of
the pHMM. In order to achieve the best predictive outcomes,
the pHMM scoring scheme has to be optimized for the problem
under analysis. In this study, a pHMM is used to model each
promoter element. The short motif e.g. 6 bp is represented by
the pHMM by consensus, insert and delete states. The problem
under analysis regards the determination of the candidate sequence
being of a promoter element modelled by the pHMM. Two scoring
schemes are classically applied to HMM analysis, the forward
algorithm and the Viterbi algorithm. The forward algorithm generates
a score indicating the likelihood of the observed sequence given
in the model, an obvious choice. The Viterbi algorithm determines
the optimal path through the model, given the observed sequence
and effectively aligns the candidate sequence to the model.
Upon analysis, the forward algorithm encompasses all paths through
the model to generate the scoring outcome, however, the Viterbi
algorithm only uses the maximal path. The desired property of
the pHMM lies in its ability to model, as closely as possible,
to the promoter element and discriminately score candidate sequences.
The forward algorithm, when compared with the Viterbi algorithm
as shown below, has lower discrimination ability, whereby forward
values produce many false predictions, however the Viterbi scoring
produces only 1 significant prediction, see
Figure 1.
Traditionally, the forward algorithm is used to answer the question
surrounding candidate outcome for a given model, however, our
investigation has found the Viterbi algorithm to be more discriminative.
The underlying algorithm of taking the best path through the
model is a more desirable property which is produced by the
Viterbi algorithm. This property serves the pHMM portion of
the hybrid better than traditional scoring via the forward algorithm.
Capitalizing on this outcome, the Viterbi output serves as the
bridge linking pHMM to ANN.
The HMM-ANN hybrid transfer function for the hidden layer that receives the pHMM values is:
 | (2) |
The ANN training set was composed of 75 pHMM score distributions
for positive genomic sequences which contain a promoter and
75 distributions for genomic sequences which do not contain
a promoter sequence (Supplementary Data—‘ANN Training
Data’). The test data, both positive and negative sequences,
were obtained from the NCBI GenBank (
22), which consisted of
E.coli promoter (positive set) and
E.coli coding sequence (negative
dataset). The database queries for the positive dataset remained
as before however the query for the negative dataset included
a ‘NOT—xx_signal’ clause to reflect the absence
of a promoter. Error backpropagation was used in these training
epochs. The ANN output represents a score which indicates the
likelihood of the candidate sequence score being a promoter.
In the example of the architecture Figure 2, there are three pHMMs, each modelling separate prokaryote promoter elements. The pHMMs scores serve as input to the three-layer feed-forward ANN whose purpose is to classify the sequences based on its trained network parameters. The numeric classification of putative promoter sequence is achieved via initial ANN parameters whose input node values are initialized to the mean pHMM score for their respective promoter motif, with zeroed hidden and output neuron initial values. Error backpropagation values involve a momentum term of 0.001 and a threshold of 0.1. The determination regarding the number of hidden neurons was achieved via plotting the effect neuron count had on positive-negative dataset discrimination. The discrimination measure was calculated via computing the mean hybrid score for 25 promoter and 25 non-promoter motifs. The desirable trait of distinguishing promoter from non-promoter versus the required number of hidden neurons to achieve the maximal discrimination is shown in Figure 3.
The decision to implement a five-neuron hidden layer was based
on the experimental evidence as shown in
Figure 3, whereby additional
neurons provided limited discrimination advantage whilst five
neurons maintained computational efficiency and discrimination
ability. The plateau trends in discrimination via adding more
hidden neurons, suggest five neurons represent a balance between
discrimination ability and computational efficiency.
Hybrid model testing data was obtained from the NCBI GenBank database (22) and represented prokaryote rRNA other coding genomic sequences. Incrementally obtained results from core promoter identification discussed later in this paper led to the requirement of modelling an additional promoter component motif. The ‘UP’ element was chosen for its role in transcription and spatial proximity to the core promoter. Due to the limited annotation of such UP elements containing genomic sequences, all available sequences (in total 10) at the time of writing were chosen for the positive test dataset (Supplementary Data—‘UP element data’). The UP element motifs from these same sequences were used in generating the UP element pHMM. This lack of annotation regarding UP elements is a task upon which our model has the ability to contribute through sequence discovery. The retrieval of E.coli genomic sequences for the test dataset followed the previous signal scheme however now utilizing the specific feature ‘UP Element’, e.g. UP element AND–35_signal AND–10_signal
To contrast the known UP element promoter-containing genomic sequences, the NCBI GenBank database (22) was again utilized to retrieve 10 non-promoter containing sequences (Supplementary Data—Test Set).
The integration of pHMM with ANN is achieved via an aggregation scheme in the JAVA programming language.
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RESULTS
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Motif identification classically suffers from a high degree
of false positives. In terms of promoter elements such as the
–10 or –35 elements, such a small motif is easily
hidden in larger sequence backgrounds. Signal conservation is
a major factor in locating signals. Prokaryote promoter signals
are relatively conserved, however and on closer analysis the
–35 box is significantly less conserved than its paired
–10 box (refer scoring trends
Figures 4 and
5). The problem
lies in the fact that a 6 bp consensus –10 box does not
possess sufficient information content to accurately distinguish
it from the sequence background. ‘Boosting’ is a
term used to aid in scoring but finding signals in the same
locality is often exploited to great effect. On the promoter
level, such surrounding signals are constrained to a very specific
region and the conservation level introduces further complications.
Using the classic prokaryote
E.coli promoter as an example,
a relatively weak –35 signal is followed by a stronger
–10 signal. These signals have a 6 bp conserved length
and therefore pose a significant challenge when locating a genomic
sequence, contrast 250 bp considered as the default window (
2)
especially when coupled with lower conservation. To reiterate,
there are three factors under consideration, (i) signal length,
(ii) signal conservation, (iii) spacer length between signals.
Signal length and composition have typically been modelled by
position weight matrices (
24) and HMMs (
25,
26). ANNs, whose
major feature is association, have been used to model spacer
length between the promoter signals (
13). Acting alone, these
techniques produce acceptable results on small background sequences—98%
positive and 90.2% negative identification of the ANN approach
(
13) whilst the HMM approach of (
25) yielded 74.1% accuracy.
Our approach firstly investigated the algorithms in isolation
and then as a hybrid. When applying such singular techniques
on large sequences, false positives severely limit the usefulness
of the model, as indicated by the images shown in
Figures 4 and
5.
Neither scoring outcome is useful in promoter recognition and thus concur with the prior findings of (1,2) regarding the number of false signals in the promoter region. Positive bars indicate a closer match to consensus whilst negative bars indicate poor matching. Figure 4 would additionally suggest much lower conservation of the –35 signal, a fact determined by others (11).
For the GenBank (22) sequence ‘AB102735
[GenBank]
’, Figures 4 and 5 show a high level of false positives. When the HMM results are placed in spatial context via the ANN, the number of false positives are reduced significantly, as seen in Figure 6 when compared with Figures 4 and 5. Whilst the hybrid model showed an improvement in lowering false predictions, it was not deemed a useful model in its current form, certainly not for large sequences as shown in Figure 6. The key concern centred on what it means to be a promoter and why there were so many false signals (scoring better than the real promoter elements). RNAP is a biological complex with specific binding ability, i.e. transcription initiation is not a random process. The biological neighbourhood therefore needed to be examined to place the true promoter in context of the genomic region. Biological reference was sought in the form of a conserved sequence that could aid in correct RNAP targeting to the true promoter. Our focus centred on the rRNA coding genes due the realization that these genes contained regulatory elements termed ‘UP elements’ located up to 82 bp upstream of the TSS. This element is recognized by the 
 | (3) |
 | (4) |
 | (5) |
subunit
of the RNAP (
27). By training a pHMM and incorporating it into
the ANN, the results as shown in
Figure 7, were accurate and
displayed excellent discrimination ability. As seen in
Figure 7,
there is only one prediction in the >6500 bp sequence, the
prediction being the true promoter. This is a highly significant
achievement when compared with HMMs used in isolation (
Figures 4 and
5) and in part the ANN result shown
Figure 6 (Supplementary
Data—Results).
To place this result in context for future research in this
area of genomic context promoter identification, the following
is a summary of the results achieved in this study as compared
with existing promoter identification tools: NNPP version 2.2
(
20) based on ANN and SAK (
21) based on support vector machines.
The test data comprised of 20 sequences: 10 containing prokaryote
promoters and 10 containing no prokaryote promoter. All the
sequences were sourced from the NCBI GenBank (
22) using query
sequences inline with prior examples as stated previously (Supplementary
Data—Test Set).
The 20 sequence test set represented an average sequence length of 3667 bp including genomic sequences of Sinorhizobium meliloti, Agrobacterium tumefaciens, Azospirillum brasilense, Shewanella violacea and E.coli. Performance measures as per Tables 1 and 2.
The results highlight the discrimination ability of the hybrid
approach, hence justify the decision to combine pHMM and ANNs
via a Viterbi integration which produces a clear improvement
over the current tools. Performance measures followed the commonly
accepted approaches as defined below.
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DISCUSSION
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False positives are a significant disadvantage to many algorithms
in motif extraction. By combining the modelling ability of pHMMs
with the associative learning capability of the ANN, this architecture
serves to address this important issue. When placed in the context
of previous research, our approach extends beyond current techniques
via the implemented hybrid architecture and the application
target, genomic context promoter identification. We therefore
do not require explicit search windows to carry out analysis.
Alone, each technique of HMM or ANN has long been used in promoter
identification, the majority of which centre around identification
of the conserved –35 and –10 hexamers. Our approach
differs from other studies on molecule type and topology. At
the time of writing, this hybrid approach to genomic promoter
identification is considered novel to the best of our research.
The trend observed in our research indicates that scoring such
small motifs in isolation results in a negative effect on prediction.
Even when combined into an ANN, the –35 and –10
signals were not significant enough to provide a useful model.
The key focus in overcoming this problem was to concentrate
on the biological purpose of the promoter and understand why
so many false signals exist. By studying the neighbourhood and
the properties of the RNAP, we were able to achieve a result
that sets our scoring model apart from previous works in terms
of discrimination ability at such large genomic contexts. Our
model has the power to predict the correct promoter element
in a >6500 bp segment of genomic data with virtually no competing
false signals, an achievement that other approaches cited in
this paper have failed to accomplish. For the GenBank (
22) sequence
‘AB102735
[GenBank]
’, the NNPP (
20) algorithm produced 21
predictions and the SAK (
21) method 24 predictions above default
(0.8-NNPP) and reasonable thresholds (0.5-SAK). By contrast,
the hybrid model produced one prediction which located the promoter
correctly.
This achievement is facilitated by the discovery whereby the Viterbi algorithm provides beneficial discrimination ability to the pHMM component of the hybrid. We are therefore confident that our model extends the state-of-the-art in model design for promoter recognition and delivers greater discrimination for promoter prediction in genomic contexts.
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SUPPLEMENTARY DATA
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Supplementary Data are available at NAR online.
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ACKNOWLEDGEMENTS
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The work in this paper was partially supported by Australian
Research Council Grants LP0349235 and LX0560616. Funding to
pay the Open Access publication charges for this article was
provided by Australian Research Council Grants and Faculty of
Science and Technology Deakin University.
Conflict of interest statement. None declared.
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ABSTRACT
INTRODUCTION
