Julie A. Dickerson

A New Resource for Cereal Genomics: 22K Barley GeneChip Comes of Age

In recent years, access to complete genomic sequences, coupled with rapidly accumulating data related to RNA and protein expression patterns, has made it possible to determine comprehensively how genes contribute to complex phenotypes. However, for major crop plants, publicly available, standard platforms for parallel expression analysis have been limited. We report the conception and design of the new publicly available, 22K Barley1 GeneChip probe array, a model for plants without a fully sequenced genome. Array content was derived from worldwide contribution of 350,000 high-quality ESTs from 84 cDNA libraries, in addition to 1,145 barley (Hordeum vulgare) gene sequences from the National Center for Biotechnology Information nonredundant database. Conserved sequences expressed in seedlings of wheat (Triticum aestivum), oat (Avena strigosa), rice (Oryza sativa), sorghum (Sorghum bicolor), and maize (Zea mays) were identified that will be valuable in the design of arrays across grasses. To enhance the usability of the data, BarleyBase, a MIAME-compliant, MySQL relational database, serves as a public repository for raw and normalized expression data from the Barley1 GeneChip probe array. Interconnecting links with PlantGDB and Gramene allow BarleyBase users to perform gene predictions using the 21,439 non-redundant Barley1 exemplar sequences or cross-species comparison at the genome level, respectively. We expect that this first generation array will accelerate hypothesis generation and gene discovery in disease defense pathways, responses to abiotic stresses, development, and evolutionary diversity in monocot plants.

Fuzzy function approximation with ellipsoidal rules

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.

PLEXdb: gene expression resources for plants and plant pathogens

PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control.

Fuzzy network profiling for intrusion detection

The Fuzzy Intrusion Recognition Engine (FIRE) is an anomaly-based intrusion detection system that uses fuzzy logic to assess whether malicious activity is taking place on a network. It uses simple data mining techniques to process the network input data and help expose metrics that are particularly significant to anomaly detection. These metrics are then evaluated as fuzzy sets. FIRE uses a fuzzy analysis engine to evaluate the fuzzy inputs and trigger alert levels for the security administrator. This paper describes the components in the FIRE architecture and explains their roles. Particular attention is given to explaining the benefits of data mining and how this can improve the meaningfulness of the fuzzy sets. Fuzzy rules are developed for some common intrusion detection scenarios. The results of tests with actual network data and actual malicious attacks are described. The FIRE IDS can detect a wide-range of common attack types.

Virtual worlds as fuzzy cognitive maps

Fuzzy cognitive maps (FCMs) can structure virtual worlds. FCMs link causal events, values, goals, and trends in a fuzzy feedback dynamical system. They direct actors in virtual worlds as the actors react to events and to one another. In nested FCMs each causal concept can control its own FCM. This combines levels of fuzzy systems that can choose goals or move objects. Adaptive FCMs change as causal patterns change. They adapt with differential Hebbian learning. FCMs are applied to an undersea virtual world of dolphins.<<ETX>>

Fuzzy intrusion detection

The Fuzzy Intrusion Recognition Engine (FIRE) is a network intrusion detection system that uses fuzzy systems to assess malicious activity against computer networks. The system uses an agent-based approach to separate monitoring tasks. Individual agents perform their own fuzzification of input data sources. All agents communicate with a fuzzy evaluation engine that combines the results of individual agents using fuzzy rules to produce alerts that are true to a degree. Several intrusion scenarios are presented along with the fuzzy systems for detecting the intrusions. The fuzzy systems are tested using data obtained from networks under simulated attacks. The results show that fuzzy systems can easily identify port scanning and denial of service attacks. The system can be effective at detecting some types of backdoor and Trojan horse attacks.

Arabidopsis gene co-expression network and its functional modules

BackgroundBiological 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.ResultsWe 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.ConclusionThis 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.

Comparative analysis of grapevine whole-genome gene predictions, functional annotation, categorization and integration of the predicted gene sequences

BackgroundThe first draft assembly and gene prediction of the grapevine genome (8X base coverage) was made available to the scientific community in 2007, and functional annotation was developed on this gene prediction. Since then additional Sanger sequences were added to the 8X sequences pool and a new version of the genomic sequence with superior base coverage (12X) was produced.ResultsIn order to more efficiently annotate the function of the genes predicted in the new assembly, it is important to build on as much of the previous work as possible, by transferring 8X annotation of the genome to the 12X version. The 8X and 12X assemblies and gene predictions of the grapevine genome were compared to answer the question, “Can we uniquely map 8X predicted genes to 12X predicted genes?” The results show that while the assemblies and gene structure predictions are too different to make a complete mapping between them, most genes (18,725) showed a one-to-one relationship between 8X predicted genes and the last version of 12X predicted genes. In addition, reshuffled genomic sequence structures appeared. These highlight regions of the genome where the gene predictions need to be taken with caution. Based on the new grapevine gene functional annotation and in-depth functional categorization, twenty eight new molecular networks have been created for VitisNet while the existing networks were updated.ConclusionsThe outcomes of this study provide a functional annotation of the 12X genes, an update of VitisNet, the system of the grapevine molecular networks, and a new functional categorization of genes. Data are available at the VitisNet website (http://www.sdstate.edu/ps/research/vitis/pathways.cfm).

VitisNet: “Omics” Integration through Grapevine Molecular Networks

Background Genomic data release for the grapevine has increased exponentially in the last five years. The Vitis vinifera genome has been sequenced and Vitis EST, transcriptomic, proteomic, and metabolomic tools and data sets continue to be developed. The next critical challenge is to provide biological meaning to this tremendous amount of data by annotating genes and integrating them within their biological context. We have developed and validated a system of Grapevine Molecular Networks (VitisNet). Methodology/Principal Findings The sequences from the Vitis vinifera (cv. Pinot Noir PN40024) genome sequencing project and ESTs from the Vitis genus have been paired and the 39,424 resulting unique sequences have been manually annotated. Among these, 13,145 genes have been assigned to 219 networks. The pathway sets include 88 “Metabolic”, 15 “Genetic Information Processing”, 12 “Environmental Information Processing”, 3 “Cellular Processes”, 21 “Transport”, and 80 “Transcription Factors”. The quantitative data is loaded onto molecular networks, allowing the simultaneous visualization of changes in the transcriptome, proteome, and metabolome for a given experiment. Conclusions/Significance VitisNet uses manually annotated networks in SBML or XML format, enabling the integration of large datasets, streamlining biological functional processing, and improving the understanding of dynamic processes in systems biology experiments. VitisNet is grounded in the Vitis vinifera genome (currently at 8x coverage) and can be readily updated with subsequent updates of the genome or biochemical discoveries. The molecular network files can be dynamically searched by pathway name or individual genes, proteins, or metabolites through the MetNet Pathway database and web-portal at http://metnet3.vrac.iastate.edu/. All VitisNet files including the manual annotation of the grape genome encompassing pathway names, individual genes, their genome identifier, and chromosome location can be accessed and downloaded from the VitisNet tab at http://vitis-dormancy.sdstate.org.

BarleyBase—an expression profiling database for plant genomics

BarleyBase (BB) (www.barleybase.org) is an online database for plant microarrays with integrated tools for data visualization and statistical analysis. BB houses raw and normalized expression data from the two publicly available Affymetrix genome arrays, Barley1 and Arabidopsis ATH1 with plans to include the new Affymetrix 61K wheat, maize, soybean and rice arrays, as they become available. BB contains a broad set of query and display options at all data levels, ranging from experiments to individual hybridizations to probe sets down to individual probes. Users can perform cross-experiment queries on probe sets based on observed expression profiles and/or based on known biological information. Probe set queries are integrated with visualization and analysis tools such as the R statistical toolbox, data filters and a large variety of plot types. Controlled vocabularies for gene and plant ontologies, as well as interconnecting links to physical or genetic map and other genomic data in PlantGDB, Gramene and GrainGenes, allow users to perform EST alignments and gene function prediction using Barley1 exemplar sequences, thus, enhancing cross-species comparison.