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Background: Many metagenomic studies have linked the imbalance in microbial abundance profiles to a wide range of diseases. These studies suggest utilizing the microbial abundance profiles as potential markers for metagenomic-associated conditions. Due to the inevitable importance of biomarkers in understanding the disease progression and the development of possible therapies, various computational tools have been proposed for metagenomic biomarker detection. However, most existing tools require prior scripting knowledge and lack user friendly interfaces, causing considerable time and effort to install, configure, and run these tools. Besides, there is no available all-in-one solution for running and comparing various metagenomic biomarker detection simultaneously. In addition, most of these tools just present the suggested biomarkers without any statistical evaluation for their quality., Results: To overcome these limitations, this work presents MetaAnalyst, a software package with a simple graphical user interface (GUI) that (i) automates the installation and configuration of 28 state-of-the-art tools, (ii) supports flexible study design to enable studying the dataset under different scenarios smoothly, iii) runs and evaluates several algorithms simultaneously iv) supports different input formats and provides the user with several preprocessing capabilities, v) provides a variety of metrics to evaluate the quality of the suggested markers, and vi) presents the outcomes in the form of publication quality plots with various formatting capabilities as well as Excel sheets., Conclusions: The utility of this tool has been verified through studying a metagenomic dataset under four scenarios. The executable file for MetaAnalyst along with its user manual are made available at https://github.com/mshawaqfeh/MetaAnalyst ., (© 2022. The Author(s).)

Background: Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed., Results: Toward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy., Conclusions: The proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers.

Background: Inferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were proposed to infer MINs using the generalized Lotka-Volterra (gLV) model. Main drawbacks of these models include the fact that these models only consider the measurement noise without taking into consideration the uncertainties in the underlying dynamics. Furthermore, inferring the MIN is characterized by the limited number of observations and nonlinearity in the regulatory mechanisms. Therefore, novel estimation techniques are needed to address these challenges., Results: This work proposes SgLV-EKF: a stochastic gLV model that adopts the extended Kalman filter (EKF) algorithm to model the MIN dynamics. In particular, SgLV-EKF employs a stochastic modeling of the MIN by adding a noise term to the dynamical model to compensate for modeling uncertainties. This stochastic modeling is more realistic than the conventional gLV model which assumes that the MIN dynamics are perfectly governed by the gLV equations. After specifying the stochastic model structure, we propose the EKF to estimate the MIN. SgLV-EKF was compared with two similarity-based algorithms, one algorithm from the integral-based family and two regression-based algorithms, in terms of the achieved performance on two synthetic data-sets and two real data-sets. The first data-set models the randomness in measurement data, whereas, the second data-set incorporates uncertainties in the underlying dynamics. The real data-sets are provided by a recent study pertaining to an antibiotic-mediated Clostridium difficile infection. The experimental results demonstrate that SgLV-EKF outperforms the alternative methods in terms of robustness to measurement noise, modeling errors, and tracking the dynamics of the MIN., Conclusions: Performance analysis demonstrates that the proposed SgLV-EKF algorithm represents a powerful and reliable tool to infer MINs and track their dynamics.

Bioinformatics skills required for genome sequencing often represent a significant hurdle for many researchers working in computational biology. This humble effort highlights the significance of genome assembly as a research area, focuses on its need to remain accurate, provides details about the characteristics of the raw data, examines some key metrics, emphasizes some tools and draws attention to a generic tutorial with example data that outlines the whole pipeline for next-generation sequencing. The article concludes by pointing out some major future research problems., (© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Motivation: Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications., Results: We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA., Availability: The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA

This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.

In the realm of bioinformatics and computational biology, the most rudimentary data upon which all the analysis is built is the sequence data of genes, proteins and RNA. The sequence data of the entire genome is the solution to the genome assembly problem. The scope of this contribution is to provide an overview on the art of problem-solving applied within the domain of genome assembly in the next-generation sequencing (NGS) platforms. This article discusses the major genome assemblers that were proposed in the literature during the past decade by outlining their basic working principles. It is intended to act as a qualitative, not a quantitative, tutorial to all working on genome assemblers pertaining to the next generation of sequencers. We discuss the theoretical aspects of various genome assemblers, identifying their working schemes. We also discuss briefly the direction in which the area is headed towards along with discussing core issues on software simplicity., (Copyright © 2012 Beijing Institute of Genomics, Chinese Academy of Sciences. Published by Elsevier Ltd. All rights reserved.)

Recently, the concept of mutual information has been proposed for inferring the structure of genetic regulatory networks from gene expression profiling. After analyzing the limitations of mutual information in inferring the gene-to-gene interactions, this paper introduces the concept of conditional mutual information and based on it proposes two novel algorithms to infer the connectivity structure of genetic regulatory networks. One of the proposed algorithms exhibits a better accuracy while the other algorithm excels in simplicity and flexibility. By exploiting the mutual information and conditional mutual information, a practical metric is also proposed to assess the likeliness of direct connectivity between genes. This novel metric resolves a common limitation associated with the current inference algorithms, namely the situations where the gene connectivity is established in terms of the dichotomy of being either connected or disconnected. Based on the data sets generated by synthetic networks, the performance of the proposed algorithms is compared favorably relative to existing state-of-the-art schemes. The proposed algorithms are also applied on realistic biological measurements, such as the cutaneous melanoma data set, and biological meaningful results are inferred.

Motivation: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations., Results: Based on the minimum description length principle, a novel network inference algorithm is proposed that greatly shrinks the search space for graphical solutions and achieves a good trade-off between modeling complexity and data fitting. Simulation results show that the algorithm achieves good performance in the case of synthetic networks. Compared with existing state-of-the-art results in the literature, the proposed algorithm exceptionally excels in efficiency, accuracy, robustness and scalability. Given a time-series dataset for Drosophila melanogaster, the paper proposes a genetic regulatory network involved in Drosophila's muscle development., Availability: Available from the authors upon request.

In this paper, the joint spectrum sensing and resource allocation problem is investigated in a multi-band-multi-user cognitive radio (CR) network. Assuming imperfect spectrum sensing information, our goal is to jointly optimize the sensing threshold and power allocation strategy such that the average total throughput of secondary users (SUs) is maximized. In Addition, the power of SUs is constrained to keep the interference introduced to primary users under certain limit, which gives rise to a nonconvex mixed integer non-linear programming (MINLP) optimization problem. Our contribution in this paper is threefold. First, it is illustrated that the dimension of the nonconvex MINLP problem can be significantly reduced, which helps to re-formulate the optimization problem without resorting to integer variables. Second, it is demonstrated that the simplified formulation admits the canonical form of a monotonic optimization, and an $\epsilon $ -optimal solution can be achieved using the polyblock outer approximation algorithm. Third, a practical low-complexity spectrum sensing and resource allocation algorithm is developed to reduce the computational cost. Finally, the effectiveness of proposed algorithms is verified by simulations. [ABSTRACT FROM AUTHOR]

Background: Recent developments of high throughput sequencing technologies allow the characterization of the microbial communities inhabiting our world. Various metagenomic studies have suggested using microbial taxa as potential biomarkers for certain diseases. In practice, the number of available samples varies from experiment to experiment. Therefore, a robust biomarker detection algorithm is needed to provide a set of potential markers irrespective of the number of available samples. Consistent performance is essential to derive solid biological conclusions and to transfer these findings into clinical applications. Surprisingly, the consistency of a metagenomic biomarker detection algorithm with respect to the variation in the experiment size has not been addressed by the current state-of-art algorithms. Results: We propose a consistency-classification framework that enables the assessment of consistency and classification performance of a biomarker discovery algorithm. This evaluation protocol is based on random resampling to mimic the variation in the experiment size. Moreover, we model the metagenomic data matrix as a superposition of two matrices. The first matrix is a low-rank matrix that models the abundance levels of the irrelevant bacteria. The second matrix is a sparse matrix that captures the abundance levels of the bacteria that are differentially abundant between different phenotypes. Then, we propose a novel Robust Principal Component Analysis (RPCA) based biomarker discovery algorithm to recover the sparse matrix. RPCA belongs to the class of multivariate feature selection methods which treat the features collectively rather than individually. This provides the proposed algorithm with an inherent ability to handle the complex microbial interactions. Comprehensive comparisons of RPCA with the state-of-the-art algorithms on two realistic datasets are conducted. Results show that RPCA consistently outperforms the other algorithms in terms of classification accuracy and reproducibility performance. Conclusions: The RPCA-based biomarker detection algorithm provides a high reproducibility performance irrespective of the complexity of the dataset or the number of selected biomarkers. Also, RPCA selects biomarkers with quite high discriminative accuracy. Thus, RPCA is a consistent and accurate tool for selecting taxanomical biomarkers for different microbial populations. [ABSTRACT FROM AUTHOR]

Dynamic planning has been used by network operators for energy saving by switching ON–OFF base stations (BSs) according to the traffic load condition while providing quality-of-service (QoS) guarantee for mobile users. In the literature, research efforts focus mainly on satisfying the QoS of downlink mobile users. However, the impact of dynamic planning on the QoS of uplink mobile users is overlooked. A dynamic planning approach that relies only on the downlink performance of mobile users to determine the BS switching decision can result in deactivating nearby small cells and associating uplink mobile users to a faraway macro BS. Consequently, uplink mobile users may suffer from QoS degradation due to the longer transmission distance. Hence, while saving energy for the network operators and satisfying the downlink QoS, the uplink QoS could be violated. In this paper, we propose a balanced dynamic planning approach that accounts for QoS requirements both in the uplink and downlink to specify the BS switching decision. The switching decision is formulated as a two-timescale average reward Markov decision process with finite horizon. Due to computational complexity, sub-optimal algorithms are proposed. Simulation results demonstrate the trade-off between energy saving and QoS guarantee. [ABSTRACT FROM PUBLISHER]

Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal) exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs. [ABSTRACT FROM AUTHOR]

Clock synchronization has become an indispensable requirement in wireless sensor networks due to its central importance in vital network operations such as data fusion and duty cycling, and has attracted considerable research interest recently. Assuming exponentially distributed random delays in a two-way message exchange mechanism, this work proposes a network-wide clock synchronization algorithm using a factor graph representation of the network. Message passing using the max-product algorithm is adopted to derive the update rules for the proposed iterative procedure. A closed form solution is obtained for each node's belief about its clock offset at each iteration. Simulation results show that the application of the proposed message passing-based network-wide clock synchronization algorithm provides convergent estimates for both regular cycle-free and random topologies. Moreover, the mean square error (MSE) performance of the proposed algorithm is also compared with the Cramer-Rao bound (CRB) for small example networks, which further highlights the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities, which are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the proposed methods suffer from restrictions and large false positives to a certain extent. Additionally, in some experiments, arbitrarily irregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task. A novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive approach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then analyzed using Fisher's hypothesis test. With a proper P-value threshold, periodic genes can be detected. A periodic signal, two nonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition, two yeast real datasets were applied for validation. The simulations and real data analysis reveal that RIAA can perform competitively with the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and when the number of cycles covered by the sampling time points is very reduced. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability. [ABSTRACT FROM AUTHOR]

DNA copy number alterations (CNAs) can cause genetic diseases, and studies have revealed a relationship between CNAs and gene expression; however, the manner in which CNAs relate to gene expression and what regulatory mechanisms underlying the relationship remain unclear. In many instances, real data have revealed a nonlinear relationship between copy number and gene expression. In this paper, queueing theory is used to model this relationship, with the basic structural parameters involving transcription factor (TF) arrival and departure rates. A key finding is that the ratio of TF arrival rate to TF departure rate is critical: small and large ratios corresponding to nonlinear and linear relationships, respectively. Indeed, copy number amplifications do not necessarily lead to expression increases: when one of the regulatory TFs exists in a small amount, copy number gains can cause a down regulation. Using the concept of mutual information, we show that the TF with minimum activation probability can have maximum dependence in regulation: a TF in small amount could result in a nonlinear copy-number-gene-expression relationship and play a major role in regulation. The expectation-maximization algorithm is used to estimate the ratio of TF arrival rate to TF departure rate. The theoretical results are illustrated via simulations. [ABSTRACT FROM PUBLISHER]

Motivated by the necessity of having a good clock synchronization amongst the nodes of wireless ad-hoc sensor networks, the joint maximum likelihood (JML) estimator for clock phase offset and skew under exponential noise model for reference broadcast synchronization (RBS) protocol is formulated and found via a direct algorithm. The Gibbs Sampler is also proposed for joint clock phase offset and skew estimation and shown to provide superior performance relative to JML-estimator. Lower and upper bounds for the mean-square errors (MSE) of JML-estimator and Gibbs Sampler are introduced in terms of the MSE of the Uniform Minimum Variance Unbiased (UMVU) estimator and the conventional Best Linear Unbiased Estimator (BLUE), respectively. [ABSTRACT FROM AUTHOR]

This letter proposes a new method to estimate the number of competing stations in IEEE 802.11 networks. Due to the nonlinear/non-Gaussian nature of measurement model, a nonlinear filtering algorithm, called the Gaussian mixture sigma point particle filter (GMSPPF), is proposed herein to estimate the number of competing stations. Since GMSPPF represents a better alternative to the conventional extended Kalman filter (EKF), unscented Kalman filter/UKF), particle filter (PF), and unscented particle filter (UPF) for nonlinear/non-Gaussian (or Gaussian) tracking problems, we apply this filter for IEEE 802.11 WLANs. GMSPPF provides a more viable means for tracking in any conditions the number of competing stations in IEEE 802.11 WLANs relative to EKF, UKF, PF, and UPF. Further, GMSPPF presents both high accuracy as well as prompt reactivity to changes in the network occupancy status. For the more accurate method (GMSPPF), the combined access mode is shown to maximize the system throughput by switching between the basic access mode and the RTS/CTS access mode. [ABSTRACT FROM AUTHOR]

Recently, a few efficient timing synchronization protocols for wireless sensor networks (WSNs) have been proposed with the goal of maximizing the accuracy and minimizing the power utilization. This paper proposes novel clock skew estimators assuming different delay environments to achieve energy-efficient network-wide synchronization for WSNs. The proposed clock skew correction mechanism significantly increases the re-synchronization period, which is a critical factor in reducing the overall power consumption. The proposed synchronization scheme can be applied to the conventional protocols without additional overheads. More-over, this paper derives the Cramer-Rao lower bounds and the maximum likelihood estimators under different delay models and assumptions. These analytical metrics serves as good benchmarks for the thus far reported experimental results. [ABSTRACT FROM AUTHOR]

This paper presents a general feedforward symbol-timing estimation framework based on the conditional maximum likelihood principle. The proposed timing estimator presents reduced implementation complexity and is obtained by performing an approximation on the Fourier series expansion of the conditional maximum likelihood function. The proposed algorithm is applied to linear modulations and two commonly used continuous phase modulations: minimum shift keying (MSK) and Gaussian MSK (GMSK). For the linear modulations, it is shown both analytically and via simulations that the performance of the proposed estimator is very close to the conditional CRB and modified CRB for signal-to-noise ratios (SNRs) in the range SNR ≤ 30 dB. Furthermore, the proposed estimator is shown to be asymptotically equivalent to the classic square-law nonlinearity estimator under certain conditions. In the case of MSK and GMSK modulations, although the proposed algorithm reaches the conditional CRB at certain SNRs, however, the conditional CRB is quite far away from the modified CRB, and there exists an alternative algorithm whose performance comes closer to the modified CRB. Therefore, the proposed estimator is more suitable for linear modulations than for MSK and GMSK modulations. [ABSTRACT FROM AUTHOR]

Although it has been studied extensively for additive white Gaussian noise and flat fading channels, the problem of continuous-mode frame synchronization over frequency-selective channels has received much less attention. In this paper, based on the maximum likelihood criterion, we derive a computationally efficient continuous-mode algorithm for joint frame synchronization, channel and frequency offset estimation for linear modulations transmitted through unknown frequency-selective channels affected by carrier-frequency offset. Computer simulations show that the proposed algorithms exhibit low implementation complexity and good performance. [ABSTRACT FROM AUTHOR]

Describes another approach for the reconstruction of a finite set of samples for an oversampled bandlimited signal. Computation of the value of the signal's spectrum; Advantages of the proposed noniterative algorithm; Reconstruction of missing samples.