Alternative splicing (AS) produces multiple messenger RNAs by combining different regions of the precursor transcript to produce diversity in gene products. Under stress conditions, many genes produce transcripts that are not otherwise produced during normal conditions. Plant growth and development are extensively affected by environmental stresses. In this study, we combine Differentially Alternatively Spliced Genes (DASGs) with Differentially Expressed Genes (DEGs) to discover important metabolic networks in the presence of environmental stress. Using publicly available RNA-Seq datasets from Arabidopsis thaliana (Col-0) subjected to heat stress conditions, we extracted several molecular pathways associated with temperature stress-response using genes that are either differentially alternatively spliced or differentially expressed. Most DASGs are linked with biological processes such as splicing, circadian rhythm, and metabolic processes. In contrast, most DEGs are linked with cell cycle and division, and transport. These differences in the biological processes highlight the importance of integrating differential splicing information along with differential expression to extract important metabolic pathways. Our analysis suggests that the exon/intron usage of the transcripts involved in key metabolic pathways significantly changes during heat stress conditions.

We propose a novel method and software tool, Strawberry, for transcript reconstruction and quantification from RNA-Seq data under the guidance of genome alignment and independent of gene annotation. Strawberry consists of two modules: assembly and quantification. The novelty of Strawberry is that the two modules use different optimization frameworks but utilize the same data graph structure, which allows a highly efficient, expandable and accurate algorithm for dealing large data. The assembly module parses aligned reads into splicing graphs, and uses network flow algorithms to select the most likely transcripts. The quantification module uses a latent class model to assign read counts from the nodes of splicing graphs to transcripts. Strawberry simultaneously estimates the transcript abundances and corrects for sequencing bias through an EM algorithm. Based on simulations, Strawberry outperforms Cufflinks and StringTie in terms of both assembly and quantification accuracies. Under the evaluation of a real data set, the estimated transcript expression by Strawberry has the highest correlation with Nanostring probe counts, an independent experiment measure for transcript expression. AVAILABILITY: Strawberry is written in C++14, and is available as open source software at https://github.com/ruolin/strawberry under the MIT license.

BACKGROUND: Alternative Splicing (AS) as a post-transcription regulation mechanism is an important application of RNA-seq studies in eukaryotes. A number of software and computational methods have been developed for detecting AS. Most of the methods, however, are designed and tested on animal data, such as human and mouse. Plants genes differ from those of animals in many ways, e.g., the average intron size and preferred AS types. These differences may require different computational approaches and raise questions about their effectiveness on plant data. The goal of this paper is to benchmark existing computational differential splicing (or transcription) detection methods so that biologists can choose the most suitable tools to accomplish their goals. RESULTS: This study compares the eight popular public available software packages for differential splicing analysis using both simulated and real Arabidopsis thaliana RNA-seq data. All software are freely available. The study examines the effect of varying AS ratio, read depth, dispersion pattern, AS types, sample sizes and the influence of annotation. Using a real data, the study looks at the consistences between the packages and verifies a subset of the detected AS events using PCR studies. CONCLUSIONS: No single method performs the best in all situations. The accuracy of annotation has a major impact on which method should be chosen for AS analysis. DEXSeq performs well in the simulated data when the AS signal is relative strong and annotation is accurate. Cufflinks achieve a better tradeoff between precision and recall and turns out to be the best one when incomplete annotation is provided. Some methods perform inconsistently for different AS types. Complex AS events that combine several simple AS events impose problems for most methods, especially for MATS. MATS stands out in the analysis of real RNA-seq data when all the AS events being evaluated are simple AS events.

MOTIVATION: Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. RESULTS: The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. AVAILABILITY: We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: CytoModeler is an open-source Java application based on the Cytoscape platform. It integrates large-scale network analysis and quantitative modeling by combining omics analysis on the Cytoscape platform, access to deterministic and stochastic simulators, and static and dynamic network context visualizations of simulation results. Availability: Implemented in Java, CytoModeler runs with Cytoscape 2.6 and 2.7. Binaries, documentation and video walkthroughs are freely available at http://vrac.iastate.edu/~jlv/cytomodeler/. Contact: [email protected]; [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Understanding the connections and the activity levels of regulators is important for the research of gene regulatory networks. While relevance score based algorithms that reconstruct gene regulatory networks from transcriptome data can infer genome-wide gene regulatory networks, they are unfortunately prone to false positive results. Transcription factor activities (TFAs) quantitatively reflect the ability of the transcription factor to regulate target genes. However, classic relevance score based gene regulatory network reconstruction algorithms use models do not include the TFA layer, thus missing a key regulatory element. RESULTS: This work integrates TFA prediction algorithms with relevance score based network reconstruction algorithms to reconstruct gene regulatory networks with improved accuracy over classic relevance score based algorithms. This method is called Gene expression and Transcription factor activity based Relevance Network (GTRNetwork). Different combinations of TFA prediction algorithms and relevance score functions have been applied to find the most efficient combination. When the integrated GTRNetwork method was applied to E. coli data, the reconstructed genome-wide gene regulatory network predicted 381 new regulatory links. This reconstructed gene regulatory network including the predicted new regulatory links show promising biological significances. Many of the new links are verified by known TF binding site information, and many other links can be verified from the literature and databases such as EcoCyc. The reconstructed gene regulatory network is applied to a recent transcriptome analysis of E. coli during isobutanol stress. In addition to the 16 significantly changed TFAs detected in the original paper, another 7 significantly changed TFAs have been detected by using our reconstructed network. CONCLUSIONS: The GTRNetwork algorithm introduces the hidden layer TFA into classic relevance score-based gene regulatory network reconstruction processes. Integrating the TFA biological information with regulatory network reconstruction algorithms significantly improves both detection of new links and reduces that rate of false positives. The application of GTRNetwork on E. coli gene transcriptome data gives a set of potential regulatory links with promising biological significance for isobutanol stress and other conditions.

UNLABELLED: CellDesigner provides a user-friendly interface for graphical biochemical pathway description. Many pathway databases are not directly exportable to CellDesigner models. PathwayAccess is an extensible suite of CellDesigner plugins, which connect CellDesigner directly to pathway databases using respective Java application programming interfaces. The process is streamlined for creating new PathwayAccess plugins for specific pathway databases. Three PathwayAccess plugins, MetNetAccess, BioCycAccess and ReactomeAccess, directly connect CellDesigner to the pathway databases MetNetDB, BioCyc and Reactome. PathwayAccess plugins enable CellDesigner users to expose pathway data to analytical CellDesigner functions, curate their pathway databases and visually integrate pathway data from different databases using standard Systems Biology Markup Language and Systems Biology Graphical Notation. AVAILABILITY: Implemented in Java, PathwayAccess plugins run with CellDesigner version 4.0.1 and were tested on Ubuntu Linux, Windows XP and 7, and MacOSX. Source code, binaries, documentation and video walkthroughs are freely available at http://vrac.iastate.edu/~jlv.

SUMMARY: OmicsAnalyzer is a Cytoscape plug-in for visual omics-based network analysis that (i) integrates hetero-omics data for one or more species; (ii) performs statistical tests on the integrated datasets; and (iii) visualizes results in a network context. AVAILABILITY: Implemented in Java, OmicsAnalyzer runs with Cytoscape 2.6 and 2.7. Binaries, documentation and video walkthroughs are freely available at http://vrac.iastate.edu/~jlv/omicsanalyzer/ CONTACT: [email protected]; [email protected]

BACKGROUND: Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability. RESULTS: MetNetGE is an interactive visualization tool based on the Google Earth platform. MetNetGE features novel visualization techniques for pathway and ontology information display. Instead of simply showing hundreds of pathways in a complex graph, MetNetGE gives an overview of the network using the hierarchical pathway ontology using a novel layout, called the Enhanced Radial Space-Filling (ERSF) approach that allows the network to be summarized compactly. The non-tree edges in the pathway or gene ontology, which represent pathways or genes that belong to multiple categories, are linked using orbital connections in a third dimension. Biologists can easily identify highly activated pathways or gene ontology categories by mapping of summary experiment statistics such as coefficient of variation and overrepresentation values onto the visualization. After identifying such pathways, biologists can focus on the corresponding region to explore detailed pathway structure and experimental data in an aligned 3D tiered layout. In this paper, the use of MetNetGE is illustrated with pathway diagrams and data from E. coli and Arabidopsis. CONCLUSIONS: MetNetGE is a visualization tool that organizes biological networks according to a hierarchical ontology structure. The ERSF technique assigns attributes in 3D space, such as color, height, and transparency, to any ontological structure. For hierarchical data, the novel ERSF layout enables the user to identify pathways or categories that are differentially regulated in particular experiments. MetNetGE also displays complex biological pathway in an aligned 3D tiered layout for exploration.

BACKGROUND: Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. RESULTS: We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000) of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM) based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. CONCLUSION: This study reveals new insight into the topological properties of biological networks. The preferential hub-hub connections might be necessary for the formation of modular structure in gene co-expression networks. The study also reveals new insight into the organization of gene functional modules.

MOTIVATION: OmicsViz is a Cytoscape plug-in for mapping and visualizing large-scale omics datasets across species, including those with many-to-many mappings between homologs. This allows users to map their data onto pathways of related model organisms. Mapping schemas across species or different experimental protocols allow users to comparatively analyze the omics data. The data can also be visualized in parallel-coordinate plots. AVAILABILITY: The latest version of OmicsViz with documentation, tutorial and jar files can be downloaded from http://metnet.vrac.iastate.edu/MetNet_fcmodeler.htm

Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles which contain a wide range of intracellular molecules such as hormones, matrix proteins and neurotransmitters. Recent studies have shown this process is regulated by molecular interactions among a group of well defined and conserved proteins. To gain insight into the dynamics of these interactions, we use protein interaction network modeling to investigate exocytotic system (particularly in endocrine) computationally and mathematically. The protein interactions are formulated into ordinary differential equations (ODE). We then apply a novel mathematic approach to estimate model parameters from experimental data for SNAREs-only network (with three key proteins), which is a subset of this system. Our approach is able to sense temporal changes in protein concentration and ratios of multiple proteins and precisely reconstruct the dynamical process of exocytosis, including vesicle docking, priming and fusion. Additionally, the model suggests that initial concentration of synaptic proteins plays a crucial role in efficiency of vesicle fusion, based on system stability analysis.

Collaborative Filtering (CF) is widely used in web recommender systems, while most existing CF applications focus on transactions or page views within a single site. In this paper, we build a recommender system prototype, which suggests web sites to users, by collecting browsing events at routers without neither user nor website effort. 100 million HTTP flows, involving 11, 327 websites, are converted to user-site ratings using access frequency as the implicit rating metric. With this rating dataset, we evaluate six CF algorithms including one proposed algorithm based on IP address locality. Our experiments show that the recommendation from K nearest neighbors (Runn) performs the best by 50% p@10 (precision of top 10) and 53% p@5 (precision of top 5). Although the precision is far from ideal, our preliminary results suggest the potential value of such a centralized web site recommender system.

Summary: PathBinderH allows users to make queries that retrieve sentences and the abstracts containing them from PubMed. Another aspect of PathBinderH is that users can specify biological taxa in order to limit searches by mentioning either the specified taxa, or their subordinate taxa, in the biological taxonomy. Although the current project requires this function only for plant taxa, the principle is extensible to the entire taxonomy. Availability: www.plantgenomics.iastate.edu/PathBinderH. Source code and databases on request. Contact: [email protected] Supplementary information: A tutorial is at the tool Website. A longer paper is at class.ee.iastate.edu/berleant/s/paperPathBinderHreport.pdf

UNLABELLED: BarleyExpress is a web-based microarray experiment data submission tool for BarleyBase, a public data resource of Affymetrix GeneChip data for plants. BarleyExpress uses the Plant Ontology vocabularies and enhances the MIAME guidelines to standardize the annotation of microarray gene expression experiments. In addition, BarleyExpress provides explicit support for factorial experiment design and template loading methods to ease the submission process for large experiments. AVAILABILITY: http://barleybase.org SUPPLEMENTARY INFORMATION: BarleyExpress Users Manual.

MOTIVATION: Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and visualize due to the amount and diverse types of information that need to be represented. For example, pathway information gives indications of interactions. Experimental data, such as transcriptomics, proteomics and metabolomics data, give snapshots of the system state. Stereoscopic virtual environments provide a true three-dimensional representation of metabolic networks, which can be intuitively manipulated, and may help to manage the data complexity. RESULTS: MetNet3D, a 3D virtual reality system, allows a user to explore gene expression and metabolic pathway data simultaneously. Normalized gene expression data are processed in R and visualized as a 3D plot. Users can find a particular gene of interest or a cluster of genes that behave similarly and see how these genes function in metabolic networks from MetNetDB, a database of Arabidopsis metabolic networks, using animated network graphs. Interactive virtual reality, with its enhanced ability to display more information, makes such integration more effective by abstracting key relationships. AVAILABILITY: MetNet3D and some sample datasets are available at http://www.vrac.iastate.edu/research/sites/metnet/Download/Download.htm. SUPPLEMENTARY INFORMATION: Color snapshots and movies are available at http://www.vrac.iastate.edu/research/sites/metnet/Bioinformatics/SupplementaryInformation.htm.

Gene regulatory networks model regulation in living organisms. Fuzzy logic can effectively model gene regulation and interaction to accurately reflect the underlying biology. A new multiscale fuzzy clustering method allows genes to interact between regulatory pathways and across different conditions at different levels of detail. Fuzzy cluster centers can be used to quickly discover causal relationships between groups of coregulated genes. Fuzzy measures weight expert knowledge and help quantify uncertainty about the functions of genes using annotations and the gene ontology database to confirm some of the interactions. The method is illustrated using gene expression data from an experiment on carbohydrate metabolism in the model plant Arabidopsis thaliana. Key gene regulatory relationships were evaluated using information from the gene ontology database. A new regulatory relationship concerning trehalose regulation of carbohydrate metabolism was also discovered in the extracted network.

The Fuzzy Intrusion Recognition Engine (FIRE) is a network intrusion detection system that uses fuzzy systems to assess malicious activity against computer networks. A key part of an intrusion detection system is the selection of key features that can characterize the state of the network. This work uses interactive data visualization to analyze the features of several different intrusion detection scenarios using the DARPA Lincoln Labs test data. Visualizing the data helps to characterize which features are key for identifying intrusions and if they can be characterized as fuzzy sets or by Boolean variables. These inputs can then be input into a fuzzy cognitive map that serves to fuse the inputs to detect more complex attacks.

Interference suppression using the minimum dispersion (MD) criterion has good near-far resistance for direct-sequence code-division multiple-access (DS/CDMA) systems in the presence of additive non-Gaussian impulsive noise modeled as a symmetric alpha-stable (S/spl alpha/S) process. This paper applies least L/sub p/-norm (LP) interference suppression to DS/CDMA systems in a S/spl alpha/S non-Gaussian impulsive noise channel. Simulation results show that the LP interference suppression scheme increases the system capacity and BER performance at the cost of higher computational complexity. The proposed detector has superior near-far resistance to the adaptive MD detector.

Autonomous star recognition requires that many similar patterns must be distinguished from one another with a small training set. Since these systems are implemented on-board a spacecraft, the network needs to have low memory requirements and minimal computational complexity. Fast training speeds are also important since star sensor capabilities change over time. This paper compares two networks that meet these needs: radial basis function networks and neural logic networks. Neural logic networks performed much better than radial basis function networks in terms of recognition accuracy, memory needed, and training speed.

A fuzzy rule can have the shape of an ellipsoid in the input-output state spare of a system. Then an additive fuzzy system approximates a function by covering its graph with ellipsoidal rule patches. It averages rule patches that overlap. The best fuzzy rules cover the extrema or bumps in the function. Neural or statistical clustering systems can approximate the unknown fuzzy rules from training data. Neural systems can then both tune these rules and add rules to improve the function approximation. We use a hybrid neural system that combines unsupervised and supervised learning to find and tune the rules in the form of ellipsoids. Unsupervised competitive learning finds the first-order and second-order statistics of clusters in the training data. The covariance matrix of each cluster gives an ellipsoid centered at the vector or centroid of the data cluster. The supervised neural system learns with gradient descent. It locally minimizes the mean-squared error of the fuzzy function approximation. In the hybrid system unsupervised learning initializes the gradient descent. The hybrid system tends to give a more accurate function approximation than does the lone unsupervised or supervised system. We found a closed-form model for the optimal rules when only the centroids of the ellipsoids change. We used numerical techniques to find the optimal rules in the general case.