Julie Dickerson

ABSTRACT Network centrality measures allow ranking of nodes and edges based on their importance to the network topology. Closeness centrality [1] and shortest path betweenness centrality [2] are two of the most popular and well-utilized centrality measures that have provided good results [3,4,5,6]. Both of these centralities rely exclusively on topological features of the network [7] to calculate node importance. We propose an improvement to these path length based centrality measures that incorporate node-specific metadata to provide biologically relevant node ranking. We choose gene annotations and gene ontology (GO) evidences as our metadata to highlight the new approach. Application of the newly proposed centrality measures to synthetic networks, and pathogen infected barley's gene co-expression networks resulted in a significantly better prioritization of the nodes. We compared our results against unmodified centrality measures applied to the same networks. Our proposed improvements provide a new avenue for tailoring centrality measures for biological networks, and hold great potential for further improvement of random walk based [8] and motif-based centrality [9] measures.

Cluster analysis is an exploratory data mining technique that involves grouping data points together based on their similarity. Objects or data points are often similar to points in more than one cluster; this is typically quantified by a measure of membership in a cluster, called fuzziness. Visualizing membership degrees in multiple clusters is the main topic of this paper. We use Orca, a java-based high-dimensional visualization environment, as the implementation platform to test several approaches, including convex hulls, glyphs, coloring schemes, and 3D plots.

A fuzzy system controls gaps between cars in single lane platoons. Fuzzy controllers create, maintain, and divide platoons on the highway. Each car's controller uses only data from sensors on the car. Tightly coupled platoons avoid the “slinky effect“ by dropping back during platoon maneuvers. When the lead car reaches its goal, the follower cars return to the proper platoon

An additive fuzzy system can control the throttle of cars in single lane platoons. The system used fuzzy controllers for velocity control and gap control. Fuzzy controllers create, maintain, and divide platoons on the highway. Each car's controller uses data from its car and the car in front of it. Cars drop back during platoon maneuvers to avoid the “slinky

This paper describes a testbed for experimentally evaluating stepping stone attack attribution techniques. There is a lack of comprehensive experimental evaluation of many different stepping stone attack detection schemes. Therefore, there are no objective, comparable evaluation results on the effectiveness and limitations of these schemes. In this research, we designed and built a scalable testbed environment that can evaluate all

Knowledge of protein subcellular locations can help decipher a protein's biological function. This work proposes new features: sequence-based: Hybrid Amino Acid Pair (HAAP) and two structure-based: Secondary Structural Element Composition (SSEC) and solvent accessibility state frequency. A multi-class Support Vector Machine is developed to predict the locations. Testing on two established data sets yields better prediction accuracies than the best available systems. Comparisons with existing methods show comparable results to ESLPred2. When StruLocPred is applied to the entire Arabidopsis proteome, over 77% of proteins with known locations match the prediction results. An implementation of this system is at http://wgzhou.ece. iastate.edu/StruLocPred/.

BarleyBase (http://barleybase.org/) and its successor, PLEXdb (http://plexdb.org/), are public resources for large-scale gene expression analysis for plants and plant pathogens. BarleyBase/PLEXdb provides a unified web interface to support the functional interpretation of highly parallel microarray experiments integrated with traditional structural genomics and phenotypic data. Users can perform hypothesis building queries from multiple interlinked resources, e.g., a particular gene, a protein class, EST entries, and physical or genetic map position-all coupled to highly parallel gene expression, for a variety of crop and model plant species, from a large array of experimental or field conditions. Array data are interlinked to analytical and biological functions (e.g., Gene and Plant Ontologies, BLAST, spliced alignment, multiple alignment, regulatory motif identification, and expression analysis), allowing members of the community to access and analyze comparative expression experi...

ABSTRACT RNA-seq technology promises a comprehensive picture of transcriptome. The traditional way of studying differential expression gene is questionable because it fails to consider alternative transcription and post-transcriptional modification. Although some studies have shown that transcript variants from a gene are predominantly generated from alternative transcription, including alternative promoters and transcriptional terminations, rather than splicing mechanisms, more computation methods focus on alternative splicing detection and quantification. Here we are only interested in methods which are able to detect condition-specific difference using RNA-seq and we categorize them into two major classes: Region Quantification (RQ) and Isoform Quantification (IQ). RQ breaks down the gene structure into"horizontally parallel pieces", exon units for example, and quantifies the expression in these "small pieces" and compares them across different conditions. While IR seeks to separate gene expression into "vertically parallel isoform", which itself is a challenging task but is more biologically meaningful, and compares a gene's isoform compositions across different conditions. In addition, based on their ability to localize significantly different regions we can further classify them into "gene-centric" or "exon-centric" method. The combination of two classification strategies yields 4 categories and we choose one representative for each category. These four representatives are Cufflinks-Cuffdiff package, DEXSeq, DiffSplice and SplicingCompass. We evaluate their performance on alternative splicing analysis using three experiments. The first experiment uses a published RNA-seq data of Arabidopsis under cold condition (NCBI SRA009031). The second experiment is a simulation study using a custom simulator by which we adopt negative binomial model to account for variability across biological replicates. The last experiment makes use of RT-PCR to evaluate the results from different methods.

Directed laboratory evolution is a common technique to obtain an evolved bacteria strain with a desired phenotype. This technique is especially useful as a supplement to rational engineering for complex phenotypes such as increased biocatalyst tolerance to toxic compounds. However, reverse engineering efforts are required in order to identify the mutations that occurred, including single nucleotide polymorphisms (SNPs), insertions/deletions (indels), duplications, and rearrangements. In this protocol, we describe the steps to (1) obtain and sequence the genomic DNA, (2) process and analyze the genomic DNA sequence data, and (3) verify the mutations by Sanger resequencing.

ABSTRACT Genome-scale models of metabolism are becoming increasingly important to understanding the relationship between genotype and phenotype in an organism on a systems level. There are many tools being developed which rely on a genome-scale metabolic reconstruction as input in order to suggest engineering interventions to improve or modify a strain's metabolism. As more organisms are being sequenced, the information available to reconstruct these models also increases. There are relatively few genome-scale models compared to available genome-scale reconstructions due to the difficulty and time required to create one. In a metabolic engineering context, a genome-scale model can be used to predict engineering interventions that will produce a desired change in an organism's metabolism. Such efforts often consist of iterative small engineering changes to an organism which must be individually analyzed and interpreted, and often updated with the results of analysis on a previous strain. Existing tools for semi-automated genome-scale model generation do not address the issue of updating existing genome-scale models to accommodate new data. A common practice in databases representing metabolic reactions is to represent all possible substrates which are compatible with an enzyme as a single generic metabolite representing the class of substrates which bind to the enzyme. These reactions are referred to as generic reactions, and are not suitable for use in genome-scale modeling, which requires only exact metabolite species to be represented. We have developed a new software for generating genome-scale metabolic models. This software facilitates modification of a base version of a Pathway Genome Database (PGDB) to align it with knowledge of a developed strain. It allows a group to maintain customized data content in an existing BioCyc database, while still being able to integrate newly released updates in a semi-automated fashion. Changes made to the engineered strain also need to be added to any metabolic models of that strain for use in constraint-based analysis. This software can be used to generate metabolic models from a strain specific database in a semi-automated process.

ABSTRACT Vitamin, metabolite, and homocysteine levels were measured in 166 patients with a history of ischemic stroke. An interactive relationship between vitamin, metabolite, and homocysteine levels was sought. The data analysis used the fuzzy c-means algorithm and fuzzy conditional clustering. The number of clusters in each case was determined by the Xie-Beni Index. In conditional fuzzy clustering, the data was clustered based on the membership value of one of the sets. Different combinations of vitamins, homocysteine, and cysthionine levels were tested. While many patients had low levels of vitamins or high homocysteine, these did not correlate in all instances. Normal vitamin levels were associated with normal homocysteine levels. Homocysteine levels did not appear to depend on vitamin levels alone. Other factors such as vitamin receptor abnormalities may dictate individual needs and ultimately play a role in the interaction between vitamin levels and homocysteine