Evaluation and Estimation of Various Markov Models with Applications to Membrane Channel Kinetics

Hidden Markov modelling is a powerful and efficient digital signal processing strategy for extracting the maximum likelihood model from a finite length sample of noisy data. Assuming the number of states in the model is known, then the state levels, transition probabilities, initial state distribution and the noise variance can be estimated. We investigate the applicability of this technique in membrane channel kinetics not only as a parameter estimator, but also as an aid to discriminating between various model types according to their statistical likelihood. We survey three representative classes of channel dynamics, namely: aggregated Markov models, semi-Markov models (with asymptotically convergent transition probabilities), and coupled Markov models; reformulating each within a discrete-time hidden Markov model framework. We then provide numerical evidence of the effectiveness of the procedure using simulated channel data and hence show that the correct model, as well as the model parameters, can be discerned. We also demonstrate that the model likelihood can be used to indicate the approximate number of states in the model.     

Statistical modelling of insect behavioural responses in relation to the chemical composition of test extracts

Abstract. The use of generalized linear models (GLM) for relating changes in insect behaviour to changes in the chemical composition of a plant extract is presented and applied to data from an experimental study of the olfactory response of Cydia pomonella L. (Lepidoptera: Tortricidae) to apple volatiles. The volatiles were collected from healthy apples, artificially damaged apples or apples infested with C. pomonella larvae (either instar I, IV or V). These treatments produced a blend of 23 major components and the chemical composition of the blends differed substantially amongst the treatments.
A statistically significant relationship was found between the concentration of hexyl hexanoate and 2-methylbutyl acetate in each extract and the number of moths moving upwind. Statistically significant models were developed which suggested that a relationship exists between the concentration of Z , E -α-farnesene, hexyl hexanoate and 2-methylbutyl acetate and the number and duration of movements made by the moths.
Subsequently Y-tube assays were carried out to validate the predictions made with respect to the orientation of mated female C. pomonella . The results of these assays confirm hexyl hexanoate as an attractant. There were indications that 2-methylbutyl acetate acted as a repellent although differences were not statistically significant. Previous bioassays have shown that C. pomonella displays a statistically significant negative linear dose–response to α-farnesene ( Hern & Dorn, 1999 ).
The statistical methods employed are very flexible and fairly easy to implement, offering the potential to screen plant extracts for bioactive compounds with a minimum of biological constraints. Their general applicability has yet to be demonstrated and as such these analyses only offer evidence of statistical relationships; the results must be validated by additional bioassays before conclusions can be drawn.     

Classifying movement behaviour in relation to environmental conditions using hidden Markov models

1.  Linking the movement and behaviour of animals to their environment is a central problem in ecology. Through the use of electronic tagging and tracking (ETT), collection of in situ data from free-roaming animals is now commonplace, yet statistical approaches enabling direct relation of movement observations to environmental conditions are still in development.
2.  In this study, we examine the hidden Markov model (HMM) for behavioural analysis of tracking data. HMMs allow for prediction of latent behavioural states while directly accounting for the serial dependence prevalent in ETT data. Updating the probability of behavioural switches with tag or remote-sensing data provides a statistical method that links environmental data to behaviour in a direct and integrated manner.
3.  It is important to assess the reliability of state categorization over the range of time-series lengths typically collected from field instruments and when movement behaviours are similar between movement states. Simulation with varying lengths of times series data and contrast between average movements within each state was used to test the HMMs ability to estimate movement parameters.
4.  To demonstrate the methods in a realistic setting, the HMMs were used to categorize resident and migratory phases and the relationship between movement behaviour and ocean temperature using electronic tagging data from southern bluefin tuna ( Thunnus maccoyii ). Diagnostic tools to evaluate the suitability of different models and inferential methods for investigating differences in behaviour between individuals are also demonstrated.     

A Bayesian network model for protein fold and remote homologue recognition

MOTIVATION: The Bayesian network approach is a framework which combines graphical representation and probability theory, which includes, as a special case, hidden Markov models. Hidden Markov models trained on amino acid sequence or secondary structure data alone have been shown to have potential for addressing the problem of protein fold and superfamily classification. RESULTS: This paper describes a novel implementation of a Bayesian network which simultaneously learns amino acid sequence, secondary structure and residue accessibility for proteins of known three-dimensional structure. An awareness of the errors inherent in predicted secondary structure may be incorporated into the model by means of a confusion matrix. Training and validation data have been derived for a number of protein superfamilies from the Structural Classification of Proteins (SCOP) database. Cross validation results using posterior probability classification demonstrate that the Bayesian network performs better in classifying proteins of known structural superfamily than a hidden Markov model trained on amino acid sequences alone.     

A markovian approach for the prediction of mouse isochores

Hidden Markov models (HMMs) are effective tools to detect series of statistically homogeneous structures, but they are not well suited to analyse complex structures. For example, the duration of stay in a state of a HMM must follow a geometric law. Numerous other methodological difficulties are encountered when using HMMs to segregate genes from transposons or retroviruses, or to determine the isochore classes of genes. The aim of this paper is to analyse these methodological difficulties, and to suggest new tools for the exploration of genome data. We show that HMMs can be used to analyse complex gene structures with bell-shaped length distribution by using convolution of geometric distributions. Thus, we have introduced macros-states to model the distributions of the lengths of the regions. Our study shows that simple HMM could be used to model the isochore organisation of the mouse genome. This potential use of markovian models to help in data exploration has been underestimated until now.     

Hidden Markov Models and Animal Behaviour

This paper proposes the use of hidden Markov time series models for the analysis of the behaviour sequences of one or more animals under observation. These models have advantages over the Markov chain models commonly used for behaviour sequences, as they can allow for time-trend or expansion to several subjects without sacrificing parsimony. Furthermore, they provide an alternative to higher-order Markov chain models if a first-order Markov chain is unsatisfactory as a model. To illustrate the use of such models, we fit multivariate and univariate hidden Markov models allowing for time-trend to data from an experiment investigating the effects of feeding on the locomotory behaviour of locusts (Locusta migratoria).     

Bayesian restoration of a hidden Markov chain with applications to DNA sequencing.

Hidden Markov models (HMMs) are a class of stochastic models that have proven to be powerful tools for the analysis of molecular sequence data. A hidden Markov model can be viewed as a black box that generates sequences of observations. The unobservable internal state of the box is stochastic and is determined by a finite state Markov chain. The observable output is stochastic with distribution determined by the state of the hidden Markov chain. We present a Bayesian solution to the problem of restoring the sequence of states visited by the hidden Markov chain from a given sequence of observed outputs. Our approach is based on a Monte Carlo Markov chain algorithm that allows us to draw samples from the full posterior distribution of the hidden Markov chain paths. The problem of estimating the probability of individual paths and the associated Monte Carlo error of these estimates is addressed. The method is illustrated by considering a problem of DNA sequence multiple alignment. The special structure for the hidden Markov model used in the sequence alignment problem is considered in detail. In conclusion, we discuss certain interesting aspects of biological sequence alignments that become accessible through the Bayesian approach to HMM restoration.     

HIPPI: highly accurate protein family classification with ensembles of HMMs

Background

Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics.

Results

We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy.

Conclusion

HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp.

     

Applying hidden Markov models to the analysis of single ion channel activity

Hidden Markov models have recently been used to model single ion channel currents as recorded with the patch clamp technique from cell membranes. The estimation of hidden Markov models parameters using the forward-backward and Baum-Welch algorithms can be performed at signal to noise ratios that are too low for conventional single channel kinetic analysis; however, the application of these algorithms relies on the assumptions that the background noise be white and that the underlying state transitions occur at discrete times. To address these issues, we present an "H-noise" algorithm that accounts for correlated background noise and the randomness of sampling relative to transitions. We also discuss three issues that arise in the practical application of the algorithm in analyzing single channel data. First, we describe a digital inverse filter that removes the effects of the analog antialiasing filter and yields a sharp frequency roll-off. This enhances the performance while reducing the computational intensity of the algorithm. Second, the data may be contaminated with baseline drifts or deterministic interferences such as 60-Hz pickup. We propose an extension of previous results to consider baseline drift. Finally, we describe the extension of the algorithm to multiple data sets.     

Activation of the ambusher tick Rhipicephalus microplus (Acari: Ixodidae) exposed to different stimuli

The present study aimed to evaluate the behaviour of larvae of Rhipicephalus microplus exposed to different stimuli. A Y-olfactometer was positioned vertically and R. microplus larvae were exposed to environmental air, CO₂ alone, N,N-diethyl-3-methylbenzamide (DEET) alone, and CO₂ combined with the repellents DEET and (E)-2-octenal. Tests were also conducted with the olfactometer positioned horizontally; in this case, however, only CO₂ was tested. In all tests conducted with the Y-olfactometer positioned vertically, CO₂ activated R. microplus larvae even in the presence of DEET and (E)-2-octenal, although activation was lower when these repellents were used. In the absence of CO₂, larval behaviour against DEET was similar to that of the larvae in the control group. In the tests performed with the olfactometer positioned horizontally, the larvae had no significant response to the presence of CO₂. The larvae were not attracted to or repelled by any compound tested in either the vertical or horizontal position of the olfactometer. The lack of horizontal displacement, attraction or repellence may have been a result of the ambush behaviour of this tick species. However, when larvae were exposed to stimuli and the olfactometer was positioned vertically, the interference of attractant and repellent stimuli in larval behaviour was assessed.     

Movement patterns of large juvenile loggerhead turtles in the Mediterranean Sea: Ontogenetic space use in a small ocean basin

Mechanisms that determine how, where, and when ontogenetic habitat shifts occur are mostly unknown in wild populations. Differences in size and environmental characteristics of ontogenetic habitats can lead to differences in movement patterns, behavior, habitat use, and spatial distributions across individuals of the same species. Knowledge of juvenile loggerhead turtles' dispersal, movements, and habitat use is largely unknown, especially in the Mediterranean Sea. Satellite relay data loggers were used to monitor movements, diving behavior, and water temperature of eleven large juvenile loggerhead turtles (Caretta caretta) deliberately caught in an oceanic habitat in the Mediterranean Sea. Hidden Markov models were used over 4,430 spatial locations to quantify the different activities performed by each individual: transit, low‐, and high‐intensity diving. Model results were then analyzed in relation to water temperature, bathymetry, and distance to the coast. The hidden Markov model differentiated between bouts of area‐restricted search as low‐ and high‐intensity diving, and transit movements. The turtles foraged in deep oceanic waters within 60 km from the coast as well as above 140 km from the coast. They used an average area of 194,802 km², where most individuals used the deepest part of the Southern Tyrrhenian Sea with the highest seamounts, while only two switched to neritic foraging showing plasticity in foraging strategies among turtles of similar age classes. The foraging distribution of large juvenile loggerhead turtles, including some which were of the minimum size of adults, in the Tyrrhenian Sea is mainly concentrated in a relatively small oceanic area with predictable mesoscale oceanographic features, despite the proximity of suitable neritic foraging habitats. Our study highlights the importance of collecting high‐resolution data about species distribution and behavior across different spatio‐temporal scales and life stages for implementing conservation and dynamic ocean management actions.     

Assessment of the repellent effect of Lippia alba essential oil and major monoterpenes on the cattle tick Rhipicephalus microplus

The control of Rhipicephalus microplus (Ixodida: Ixodidae) is achieved using synthetic acaricides. However, resistant tick populations are widespread around the world. Plant essential oils can act as repellents, keeping ticks away from hosts and decreasing the selection pressure on synthetic acaricides. The aim of this study was to evaluate the in vitro repellent effect of Lippia alba essential oil on R. microplus larvae. Leaves from two L. alba genotypes maintained under the same agronomic and environmental conditions were collected. Essential oil was extracted by hydrodistillation and analysed by gas chromatography–mass spectrometry (GC‐MS). The major monoterpenes detected in the chemical analysis were commercially acquired and tested. For the repellency test, a glass rod was vertically fixed to measure active climbing of approximately 30 R. microplus larvae aged 14–21 days in response to essential oils and monoterpenes. Repellency was evaluated at 1 h, 3 h and 5 h after treatment. Variation in repellent action was detected between the genotypes. The major monoterpenes identified in the essential oils (limonene and carvone) showed low repellent effects in comparison with intact essential oils. Thus, the present results showed that L. alba essential oil contains bioactive compounds with great repellent activity against ticks that varies according to the plant genotype.     

Bayesian restoration of ion channel records using hidden Markov models

Hidden Markov models have been used to restore recorded signals of single ion channels buried in background noise. Parameter estimation and signal restoration are usually carried out through likelihood maximization by using variants of the Baum-Welch forward-backward procedures. This paper presents an alternative approach for dealing with this inferential task. The inferences are made by using a combination of the framework provided by Bayesian statistics and numerical methods based on Markov chain Monte Carlo stochastic simulation. The reliability of this approach is tested by using synthetic signals of known characteristics. The expectations of the model parameters estimated here are close to those calculated using the Baum-Welch algorithm, but the present methods also yield estimates of their errors. Comparisons of the results of the Bayesian Markov Chain Monte Carlo approach with those obtained by filtering and thresholding demonstrate clearly the superiority of the new methods.     

Hap-seqX: Expedite algorithm for haplotype phasing with imputation using sequence data

Haplotype phasing is one of the most important problems in population genetics as haplotypes can be used to estimate the relatedness of individuals and to impute genotype information which is a commonly performed analysis when searching for variants involved in disease. The problem of haplotype phasing has been well studied. Methodologies for haplotype inference from sequencing data either combine a set of reference haplotypes and collected genotypes using a Hidden Markov Model or assemble haplotypes by overlapping sequencing reads. A recent algorithm Hap-seq considers using both sequencing data and reference haplotypes and it is a hybrid of a dynamic programming algorithm and a Hidden Markov Model (HMM), which is shown to be optimal. However, the algorithm requires extremely large amount of memory which is not practical for whole genome datasets. The current algorithm requires saving intermediate results to disk and reads these results back when needed, which significantly affects the practicality of the algorithm. In this work, we proposed the expedited version of the algorithm Hap-seqX, which addressed the memory issue by using a posterior probability to select the records that should be saved in memory. We show that Hap-seqX can save all the intermediate results in memory and improves the execution time of the algorithm dramatically. Utilizing the strategy, Hap-seqX is able to predict haplotypes from whole genome sequencing data.     

链霉菌基因组岛和次生代谢物合成相关的生物信息学工具及数据库

随着DNA测序技术的进步, 迄今为止已有12个链霉菌基因组被测序。面对海量组学的数据, 急需采用生物信息学方法来大规模深度挖掘这些重要微生物资源, 进而实现链霉菌资源挖掘和代谢潜力释放的深度互动。围绕链霉菌基因组比较分析中菌株特有的基因组岛和次生代谢物生物合成基因簇的识别及功能解析等两个常见问题, 本文收集了近期开发的一些常用生物信息学工具和二级数据库。以链霉菌染色体核心区和两臂的划分、天蓝色链霉菌和变铅青链霉菌基因组岛的识别、卡特利链霉菌巨型质粒的鉴别为例, 简介了这些生物信息学资源的使用方法。此外, 还简述了我们课题组进行放线菌型整合性接合元件识别和开发硫肽生物合成基因簇预测新工具的一些尝试。生物信息学工具和二级数据库在链霉菌基因组比较分析中有重要作用, 可将研究重点迅速地聚焦在某株菌的可移动遗传元件和次生代谢物生成基因簇上, 确定其对应的菌株特有表型, 及解析新型化合物生物合成和调控机理。     

Learning generative models for protein fold families

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.     

Repellent action and contact toxicity mechanisms of the essential oil extracted from Chinese chive against Plutella xylostella larvae

Botanical pesticides play increasingly important roles in the control of agricultural pests. In this study, the insecticidal effects, specifically the repellent action and contact toxicity, of the essential oil extracted from Chinese chive (EOC) against Plutella xylostella larvae were confirmed. The mechanisms of repellent’s action were studied using electroantennograms (EAGs), and the effects on glutathione S‐transferase (GST), carboxylesterase (CarE), and acetyl cholinesterase were investigated after EOC treatments. The EOC affected the EAG results and inhibited the activities of GST and CarE in treated P. xylostella larvae, which could explain its insecticidal effects. And, four pyrazines showed greater repellent activities than that of the EOC, which was confirmed as the main active compounds of EOC.     

Effects of fenvalerate on the net-spinning behaviour of Hydropsych siltalai (Döhler) (Trichoptera: Hydropsychidae)

The effects of fenvalerate exposure on the net-spinning behaviour of Hydropsyche siltalai were examined in a laboratory study. The larvae were exposed to nominal pulse-doses of 0.25 and 0.5 μg fenvalerate l-1. Nets were collected and examined for anomalies after four days of exposure to fenvalerate. Additional nets were collected after another four day of exposure. The fenvalerate dissipated rapidly from the water column, and since only two doses of fenvalerate were given, the larvae were exposed to two pulse-doses of fenvalerate rather than to a constant concentration. In the 0.5 μg l-1-treatment the net-spinning behaviour was significantly affected, expressed as an increased mesh-opening and a decreased symmetry of the nets. No significant effects of fenvalerate exposure on the net-spinning behaviour were detected in the 0.25 μg l-1-treatment. Thus, with the conditions given in this experiment, exposure to fenvalerate starts to affect the net-spinning behaviour of Hydropsyche siltalai at a concentration between 0.25 and 0.5 μg l-1. The use of net-anomalies and Hydropsyche as bioindicators for monitoring pollutants in stream ecosystems are discussed. This revised version was published online in July 2006 with corrections to the Cover Date.