Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Reynolds, Sheila M.; Käll, Lukas; Riffle, Michael; Bilmes, Jeff; Noble, William Stafford

doi:10.1371/journal.pcbi.1000213

Cited by 244 publications

(198 citation statements)

References 40 publications

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“…The DBN that we use in this work is similar to a previous DBN-based method we developed to predict transmembrane protein topology from sequence [23], and is implemented using the Graphical Models Toolkit (GMTK) [24]. The task addressed by Philius, the topology prediction DBN, is the segmentation of an input protein into a series of non-overlapping regions belonging to one of three classes: membrane, inside, or outside.…”

Section: A Dynamic Bayesian Network For Nucleosome Predictionmentioning

confidence: 99%

“…For simplicity, this graphical model omits the portion of the graph which takes care of the counting for the fixed-duration states. This counting mechanism is implemented exactly as in Philius [23]. To fully define the nucleosome positioning DBN, in addition to the graphical model shown in Figure 5a, the precise form of the relationship between each node and its parent(s) must be defined.…”

Section: A Dynamic Bayesian Network For Nucleosome Predictionmentioning

confidence: 99%

“…The result is that the probability of a particular sequence of state assignments is weighted according to the information available at each position. This direct influence on the local probability over the possible assignments to the state variable is accomplished using the concept of "virtual evidence" [23,25,26], as will be described in more detail below. We add the subscript i to this conditional relationship to indicate that it depends on the current position, i, unlike the relationship between the state and the label, and unlike the observation distribution in a typical time-homogeneous HMM.…”

Section: A Dynamic Bayesian Network For Nucleosome Predictionmentioning

confidence: 99%

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Predicting Nucleosome Positioning Using Multiple Evidence Tracks

Reynolds

Weng

Bilmes

et al. 2010

Lecture Notes in Computer Science

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Abstract.We describe a probabilistic model, implemented as a dynamic Bayesian network, that can be used to predict nucleosome positioning along a chromosome based on one or more genomic input tracks containing position-specific information (evidence). Previous models have either made predictions based on primary DNA sequence alone, or have been used to infer nucleosome positions from experimental data. Our framework permits the combination of these two distinct types of information. We show how this flexible framework can be used to make predictions based on either sequence-model scores or experimental data alone, or by using the two in combination to interpret the experimental data and fill in gaps. The model output represents the posterior probability, at each position along the chromosome, that a nucleosome core overlaps that position, given the evidence. This posterior probability is computed by integrating the information contained in the input evidence tracks along the entire input sequence, and fitting the evidence to a simple grammar of alternating nucleosome cores and linkers. In addition to providing a novel mechanism for the prediction of nucleosome positioning from arbitrary heterogeneous data sources, this framework is also applicable to other genomic segmentation tasks in which local scores are available from models or from data that can be interpreted as defining a probability assignment over labels at that position. The ability to combine sequence-based predictions and data from experimental assays is a significant and novel contribution to the ongoing research regarding the primary structure of chromatin and its effects upon gene regulation.

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Section: A Dynamic Bayesian Network For Nucleosome Predictionmentioning

confidence: 99%

Section: A Dynamic Bayesian Network For Nucleosome Predictionmentioning

confidence: 99%

Section: A Dynamic Bayesian Network For Nucleosome Predictionmentioning

confidence: 99%

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Predicting Nucleosome Positioning Using Multiple Evidence Tracks

Reynolds

Weng

Bilmes

et al. 2010

Lecture Notes in Computer Science

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“…The beta-barrel prediction problem is generally divided into three unique domains (Reynolds and Kall, Riffle, Bilmes, 2008):…”

Section: Introductionmentioning

confidence: 99%

NN approach and its comparison with NN-SVM to beta-barrel prediction

Kazemian

Yusuf²,

White

et al. 2016

Expert Systems with Applications

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Abstract:This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel transmembrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences.To address the problem, this research embarks upon a NN technique and its comparison with hybrid-two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmetric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transition trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN 2 with and without redundant proteins for prediction of transmembrane beta barrel spanning regions.

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“…Plewczynski et al [22] used neural networks to detect signal peptides from the extracted Swiss-PROT protein database and obtained a combined accuracy of 73% for eukaryotes and prokaryotes. Reynolds [23] utilised dynamic Bayesian networks which resulted in achieving a relative accuracy of 13% over Phobius methodology with sensitivity and specificity of 0.96 in signal peptide detection. Sun and Wang [24] used SVM with a K-nearest classifier.…”

Section: Introductionmentioning

confidence: 99%

Signal peptide discrimination and cleavage site identification using SVM and NN

Kazemian

Yusuf

White

2014

Computers in Biology and Medicine

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tAbout 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short aminoacid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification.The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model.

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Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Cited by 244 publications

References 40 publications

Predicting Nucleosome Positioning Using Multiple Evidence Tracks

Predicting Nucleosome Positioning Using Multiple Evidence Tracks

NN approach and its comparison with NN-SVM to beta-barrel prediction

Signal peptide discrimination and cleavage site identification using SVM and NN

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