Biological Sequence Prediction using General Fuzzy Automata

BackgroundProfile Hidden Markov Model (HMM) is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available.ResultsWe develop a visual editor for profile Hidden Markov Models (HMMEditor). HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path.ConclusionHMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

BackgroundIntrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.MethodsIn this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).ResultsTo evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.ConclusionsUsing HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM. Due to the fact that with a MSA, the sequences of amino acids are biologically related, the one-by-one dependency between two amino acids can be considered. In other words, based on the MSA, the dependency between an amino acid and its corresponding amino acid located above can be combined with the PHMM. For this purpose, the new emission probability matrix which considers the one-by-one dependencies between amino acids is constructed. The parameters of a PHMM are of two types; transition and emission probabilities which are usually estimated using an EM algorithm called the Baum-Welch algorithm. We have generalized the Baum-Welch algorithm using similarity emission matrix constructed by integrating the new emission probability matrix with the common emission probability matrix. Then, the performance of similarity emission is discussed by applying it to the top twenty protein families in the Pfam database. We show that using the similarity emission in the Baum-Welch algorithm significantly outperforms the common Baum-Welch algorithm in the task of assigning protein sequences to protein families.

In this work we created a sequence model that goes beyond simple linear patterns to model a specific type of higher-order relationship possible in biological sequences. Particularly, we seek models that can account for partially overlaid and interleaved patterns in biological sequences. Our proposed context-switching model (cswHMM) is designed as a variable-order hidden Markov model (HMM) with a specific structure that allows switching control between two or more sub-models.Tests of this approach suggest that a combination of HMMs for protein sequence analysis, such as pattern mining based HMMs or profile HMMs, with the context-switching approach can improve the descriptive ability and performance of the models.

Automata theory establishes a formal structure for modeling computation, yet bridging this theory with practical, ever-changing systems requires enhanced flexibility. This paper presents a dynamic state general fuzzy automata (GFA[Formula: see text]) that enables applications to dynamically adjust their states in response to changing input data. This approach enhances the capabilities of existing general fuzzy automata (GFA) models to handle uncertainty in applications where the state set is not predefined. Instead of defining a new GFA, similar states are integrated into the existing automata. Algorithms are proposed to calculate the similarity between states and accommodate these similar states into the current model. Thus, this work bridges the gap between theoretical and practical applications of fuzzy automata in real-world applications such as resource allocation and air quality prediction. The substantial volume of data presents a significant challenge in these fields. The proposed model addresses this by effectively incorporating similar historical data to enhance air pollution forecasting. A machine learning linear regression model is used for air quality index (AQI) prediction to validate this approach. A comparative analysis between the proposed GFA[Formula: see text] model and the linear regression model highlights the performance differences and demonstrates the effectiveness of each model in accurately predicting AQI values.

Inherently, the operational processes of a large number of digital communication systems are based on input events. Therefore, discrete event-based system (DEVS) model is an appropriate tool to model such systems. On the other hand, as communication networks become more popular, congestion control becomes more vital. This necessitates the design and development of a suitable model that is able to represent the complete behavior of the network traffic as much as possible. Many researchers have attempted to develop a model first, and then at the next step, develop a good controller for the communication system. In other words, modeling and control are usually treated as two separate issues. However, it seems that treating the modeling and control issues simultaneously may improve the performance and efficiency of communication systems significantly. In other words, the idea is to develop a model which is naturally similar to the system and at the same time is intelligent enough for self-adaptation so that real-time updates of the system's states are possible. General Fuzzy Automata (GFA) is a discrete-event-based formalism and is thus naturally similar to communication systems. Due to the fuzzy nature of GFA, it is possible to incorporate the advantages of fuzzy control. Therefore, in this paper, GFA is proposed as a tool for the modeling and control of communication systems. Simulation studies show that the GFA-based approach is superior to the classical ATM switch in the usage of a system's resources and system performance.

Hidden Markov Models are a widely used generative model for analysing sequence data. A variant, Profile Hidden Markov Models are a special case used in Bioinformatics to represent, for example, protein families. In this paper we introduce a simple propositionalisation method for Profile Hidden Markov Models. The method allows the use of PHMMs discriminatively in a classification task. Previously, kernel approaches have been proposed to generate a discriminative description for an HMM, but require the explicit definition of a similarity measure for HMMs. Propositionalisation does not need such a measure and allows the use of any propositional learner including kernel-based approaches. We show empirically that using propositionalisation leads to higher accuracies in comparison with PHMMs on benchmark datasets.

Molecular biologists use Hidden Markov Models (HMMs) as a popular tool to statistically describe protein families. This statistical description can then be used for sensitive and selective database scanning, e.g. new protein sequences are compared with a set of HMMs to detect functional similarities. Even though efficient dynamic programming algorithms exist for the problem, the required scanning time is still very high, and because of the rapid database growth finding fast solutions is of high importance to research in this area. In this paper we present how reconfigurable architectures can be used to derive an efficient fine-grained parallelization of the dynamic programming calculation. It is described how this technique leads to significant runtime savings for HMM database scanning on a standard off-the-shelf FPGA.KeywordsHide Markov ModelSystolic ArrayViterbi AlgorithmProcessor ArrayQuery ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Chapter 6 - Biological sequence analysis on GPU

In this paper, we first define the notion of a complete general fuzzy automaton with threshold c and construct an H - group, as well as commuta- tive hypergroups, on the set of states of a complete general fuzzy automaton with threshold c. We then define invertible general fuzzy automata, discuss the notions of homogeneity, separation, thresholdness connected, thresh- oldness inner irreducible and principal and strongly connected, as applied to them and use these concepts to construct a quasi-order hypergroup on an invertible general fuzzy automaton. Finally, we derive relationships between the properties of an invertible general fuzzy automaton and the induced hy- pergroup.

In this paper we propose a new framework for modeling 2D shapes. A shape is first described by a sequence of local features (e.g., curvature) of the shape boundary. The resulting description is then used to build a profile hidden Markov model (PHMM) representation of the shape. PHMMs are a particular type of hidden Markov models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions and missing contour parts. Different from traditional HMM-based shape models, the sparseness of the PHMM structure allows efficient inference and learning algorithms for shape modeling and analysis. The new framework can be applied to a wide range of problems, from shape matching and classification to shape segmentation. Our experimental results show the effectiveness and robustness of this new approach in the three application domains.

BackgroundLong short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Amino acid sequence profiles are widely used for bioinformatics studies, such as sequence similarity searches, multiple alignments, and evolutionary analyses. Currently, many biological sequences are becoming available, and the rapidly increasing amount of sequence data emphasizes the importance of scalable generators of amino acid sequence profiles.ResultsWe employed the LSTM network and developed a novel profile generator to construct profiles without any assumptions, except for input sequence context. Our method could generate better profiles than existing de novo profile generators, including CSBuild and RPS-BLAST, on the basis of profile-sequence similarity search performance with linear calculation costs against input sequence size. In addition, we analyzed the effects of the memory power of LSTM and found that LSTM had high potential power to detect long-range interactions between amino acids, as in the case of beta-strand formation, which has been a difficult problem in protein bioinformatics using sequence information.ConclusionWe demonstrated the importance of sequence context and the feasibility of LSTM on biological sequence analyses. Our results demonstrated the effectiveness of memories in LSTM and showed that our de novo profile generator, SPBuild, achieved higher performance than that of existing methods for profile prediction of beta-strands, where long-range interactions of amino acids are important and are known to be difficult for the existing window-based prediction methods. Our findings will be useful for the development of other prediction methods related to biological sequences by machine learning methods.

Regulation of gene expression influences almost all biological processes in an organism; sequence-specific DNA-binding transcription factors are critical to this control. For most genomes, the repertoire of transcription factors is only partially known. Hitherto transcription factor identification has been largely based on genome annotation pipelines that use pairwise sequence comparisons, which detect only those factors similar to known genes, or on functional classification schemes that amalgamate many types of proteins into the category of ‘transcription factor’. Using a novel transcription factor identification method, the DBD transcription factor database fills this void, providing genome-wide transcription factor predictions for organisms from across the tree of life. The prediction method behind DBD identifies sequence-specific DNA-binding transcription factors through homology using profile hidden Markov models (HMMs) of domains. Thus, it is limited to factors that are homologus to those HMMs. The collection of HMMs is taken from two existing databases (Pfam and SUPERFAMILY), and is limited to models that exclusively detect transcription factors that specifically recognize DNA sequences. It does not include basal transcription factors or chromatin-associated proteins, for instance. Based on comparison with experimentally verified annotation, the prediction procedure is between 95% and 99% accurate. Between one quarter and one-half of our genome-wide predicted transcription factors represent previously uncharacterized proteins. The DBD () consists of predicted transcription factor repertoires for 150 completely sequenced genomes, their domain assignments and the hand curated list of DNA-binding domain HMMs. Users can browse, search or download the predictions by genome, domain family or sequence identifier, view families of transcription factors based on domain architecture and receive predictions for a protein sequence.

One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models. We anticipate that the review will be useful both to researchers professionally working in bioinformatics as well as to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.

Increasing volumes and variety of biological data makes analysis more challenging. Storing those volumes of heterogeneous data is a bigger challenge. Biological Sequence analysis is more important in bioinformatics which affects the healthcare system. Genomic sequence analysis can predict the risk of a disease before it becomes a problem. These analytics can improve the overall patient’s health, improve patient’s experience and reduce the cost of healthcare system. Towards the end of the paper, a framework is proposed which can effectively model and analyse biological sequences in parallel distributed computing environment using NoSQL databases.