Discovering Functional Modules by Clustering Gene Co-expression Networks

Fine-grained hardware protection could provide a powerful and effective means for isolating untrusted code. However, previous techniques for providing fine-grained protection in hardware have lead to poor performance. Legba has been proposed as a new caching architecture, designed to reduce the granularity of protection, without slowing down the processor. Unfortunately, the designers of Legba have not attempted an implementation. Instead, all of their analysis is based purely on simulations. We present an implementation of the Legba design on a MIPS Core Processor, along with an analysis of our observations and results. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Implementing Legba: Fine-Grained Memory Protection Aaron Sheffield, Ross T. Sowell, Mike Wilson Department of Computer Science & Engineering Washington University in St. Louis One Brookings Drive, Campus Box 1045 St. Louis, Missouri 63130-4899 {ajs6, mlw2}@cec.wustl.edu rsowell@gmail.com Abstract—Fine-grained hardware protection could provide a powerful and effective means for isolating untrusted code. However, previous techniques for providing fine-grained protection in hardware have lead to poor performance. Legba has been proposed as a new caching architecture, designed to reduce the granularity of protection, without slowing down the processor. Unfortunately, the designers of Legba have not attempted an implementation. Instead, all of their analysis is based purely on simulations. We present an implementation of the Legba design on a MIPS Core Processor, along with an analysis of our observations and results.Fine-grained hardware protection could provide a powerful and effective means for isolating untrusted code. However, previous techniques for providing fine-grained protection in hardware have lead to poor performance. Legba has been proposed as a new caching architecture, designed to reduce the granularity of protection, without slowing down the processor. Unfortunately, the designers of Legba have not attempted an implementation. Instead, all of their analysis is based purely on simulations. We present an implementation of the Legba design on a MIPS Core Processor, along with an analysis of our observations and results.

When multiple participants work on a workflow that represents a large, collaborative activity, it is important to have a well defined process to determine the portions of the workflow that each participant is responsible for executing. In this paper, we describe a process and related algorithms required to assign tasks in a workflow, to hosts that are willing to carry out the execution of these tasks, and thereby contributing to the completion of the activity. This problem is a stylized form of the multi-processor scheduling algorithm which has been shown to be NP-Hard. Further complicating the issue is that we are targeting our approach to mobile ad hoc networks, where hosts are physically mobile, communication links are frequently interrupted, and spatiotemporal considerations become increasingly important. We describe a distributed approach to task allocation in mobile ad hoc networks that employs heuristics to assign tasks in a workflow to mobile hosts based on their capabilities and their mobility patterns. We have implemented our algorithms in the context of CiAN, a workflow management system (WfMS) supporting collaborations in a mobile environment. In addition, we also present performance data of our algorithm and compare it to naive and brute force approaches.

The field of manifold learning provides powerful tools for parameterizing high-dimensional data points with a small number of parameters when this data lies on or near some manifold. Images can be thought of as points in some high-dimensional image space where each coordinate represents the intensity value of a single pixel. These manifold learning techniques have been successfully applied to simple image sets, such as handwriting data and a statue in a tightly controlled environment. However, they fail in the case of natural image sets, even those that only vary due to a single degree of freedom, such as a person walking or a heart beating. Parameterizing data sets such as these will allow for additional constraints on traditional computer vision problems such as segmentation and tracking. This dissertation explores the reasons why classical manifold learning algorithms fail on natural image sets and proposes new algorithms for parameterizing this type of data. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 WASHINGTON UNIVERSITY THE HENRY EDWIN SEVER GRADUATE SCHOOL DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MANIFOLD LEARNING FOR NATURAL IMAGE SETS by Richard M. Souvenir, M.S. Computer Science, B.S. Applied Science Prepared under the direction of Professor Robert Pless A dissertation presented to the Henry Edwin Sever Graduate School of Washington University in partial fulfillment of the requirements for the degree of DOCTOR OF SCIENCE August 2006 Saint Louis, Missouri WASHINGTON UNIVERSITY THE HENRY EDWIN SEVER GRADUATE SCHOOL DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energy-aware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Adaptive Service Provisioning for Wireless Sensor Networks Chien-Liang Fok, Gruia-Catalin Roman and Chenyang Lu Washington University in St. Louis {liang, roman, lu}@cse.wustl.edu Abstract—Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energyaware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process, demonstrating the system’s efficacy.Service provisioning has gained significant attention as a promising programming model for heterogeneous wireless sensor networks. Its key idea is to exploit the decoupling of service providers and consumers to enable platform-independent applications that are dynamically bound to platform-specific services. We explore novel adaptive service binding strategies that are able to cope with network dynamics and to promote energy conservation. To achieve this goal, we developed policies and algorithms that automatically switch providers in response to network topology changes and adapt application behavior when opportunities for energy savings surface. The latter is accomplished by providing limited information about the energy consumption associated with using various services, by systematically exploiting opportunities for sharing service invocations, and by exploiting the broadcast nature of wireless communication in WSNs. The policies and algorithms have been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can significantly increase service availability and enable energyaware service binding decisions that result in increased energy efficiency, while imposing minimal additional burden on the application, service, and device developers. Applications involving medical patient monitoring and structural health monitoring are used in the evaluation process, demonstrating the system’s efficacy.

Modern computer architectures have evolved from uni-processor platforms to multi-processor and multi-core platforms, but traditional real-time distributed middleware such as RT-CORBA has not kept pace with that evolution. To address those issues, this paper describes the design and implementation of MCFlow, a new real-time distributed middleware for dependent task graphs running on multi-core platforms. MCFlow provides the following contributions to the state of the art in real-time middleware: (1) it provides an efficient C++ based component model through which computations can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) it allows optimizations for inter-component communication to avoid data copying without sacrificing the parallel executability of data dependent tasks; (3) it strictly separates timing and functional concerns of an application so that they can evolve and can be configured independently; and (4) it provides a novel event dispatching architecture that uses lock free algorithms to avoid mutex locking and reduce memory contention, CPU context switching, and priority inversion. We also present an empirical evaluation that demonstrates the efficacy of our approach. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 The Design and Implementation of MCFlow: a Real-time Multi-core Aware Middleware for Dependent Task Graphs Huang-Ming Huang, Christopher Gill, Chengyang Lu Department of Computer Science and Engineering, Washington University St. Louis, MO, USA {hh1, cdgill, lu}@cse.wustl.edu Abstract—Modern computer architectures have evolved from uni-processor platforms to multi-processor and multi-core platforms, but traditional real-time distributed middleware such as RT-CORBA has not kept pace with that evolution. To address those issues, this paper describes the design and implementation of MCFlow, a new real-time distributed middleware for dependent task graphs running on multi-core platforms. MCFlow provides the following contributions to the state of the art in real-time middleware: (1) it provides an efficient C++ based component model through which computations can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) it allows optimizations for inter-component communication to avoid data copying without sacrificing the parallel executability of data dependent tasks; (3) it strictly separates timing and functional concerns of an application so that they can evolve and can be configured independently; and (4) it provides a novel event dispatching architecture that uses lock free algorithms to avoid mutex locking and reduce memory contention, CPU context switching, and priority inversion. We also present an empirical evaluation that demonstrates the efficacy of our approach.Modern computer architectures have evolved from uni-processor platforms to multi-processor and multi-core platforms, but traditional real-time distributed middleware such as RT-CORBA has not kept pace with that evolution. To address those issues, this paper describes the design and implementation of MCFlow, a new real-time distributed middleware for dependent task graphs running on multi-core platforms. MCFlow provides the following contributions to the state of the art in real-time middleware: (1) it provides an efficient C++ based component model through which computations can be configured flexibly for execution within a single core, across cores of a common host, or spanning multiple hosts; (2) it allows optimizations for inter-component communication to avoid data copying without sacrificing the parallel executability of data dependent tasks; (3) it strictly separates timing and functional concerns of an application so that they can evolve and can be configured independently; and (4) it provides a novel event dispatching architecture that uses lock free algorithms to avoid mutex locking and reduce memory contention, CPU context switching, and priority inversion. We also present an empirical evaluation that demonstrates the efficacy of our approach. Keywords-component; middleware

Protein databases have grown exponentially over the last decade. This exponential growth has made extracting valuable information from these databases increasingly time consuming. This project presents a new method of accelerating a commonly used program for performing similarity searching on protein databases, BLASTP. This project describes the design and implementation of Mercury BLASTP, a customized hardware accelerated variant of BLASTP. This project focuses on the gapped alignment stage of Mercury BLASTP and provides design details and implementation results. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Acceleration of Gapped Alignment in BLASTP Using the Mercury System Brandon Harris bbh2@wustl.edu Abstract Protein databases have grown exponentially over the last decade. This exponential growth has made extracting valuable information from these databases increasingly time consuming. This project presents a new method of accelerating a commonly used program for performing similarity searching on protein databases, BLASTP. This project describes the design and implementation of Mercury BLASTP, a customized hardware accelerated variant of BLASTP. This project focuses on the gapped alignment stage of Mercury BLASTP and provides design details and implementation results.Protein databases have grown exponentially over the last decade. This exponential growth has made extracting valuable information from these databases increasingly time consuming. This project presents a new method of accelerating a commonly used program for performing similarity searching on protein databases, BLASTP. This project describes the design and implementation of Mercury BLASTP, a customized hardware accelerated variant of BLASTP. This project focuses on the gapped alignment stage of Mercury BLASTP and provides design details and implementation results.

Game theory is an important field in economics; it studies how people make decisions amid conflict and cooperation. Various experiments have been carried to study the way people play those games, and economists study those data for various purposes. There has been a rise of need for using artificial agents to simulate the game, since we could save the cost of hiring human subjects for the experiments, and we could gain more control over the experiment settings. Type of Report: MS Project Report Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 EWA Model with Recency Effect and Limited Memory Hang Xie xiehang@go.wustl.edu Yixin Chen chen@cse.wustl.edu Washington University One Brookings Drive St. Louis, MO 63130 United States

Recent advances in High-Throughput Sequencing (HTS) technology have greatly facilitated the researches in bioinformatics field. With the ultra-high sequencing speed and improved base-calling accuracy, Illumina Genome Analyzer is currently the most widely used platform in the field. To use the raw reads generated from the sequencing machine, the 3’ adapter sequence attached to the real read in the process of ligation needs to be correctly trimmed. This is often done by some inhouse scripts or different packages with various parameters. They either use the Smith-Waterman algorithm or search for an exact match of the 3’ adapter sequence. In this report, I investigated methodologies as well as the strengths and weaknesses of five representative mainstream adapter trimming tools in order to suggest a direction for other researchers. Furthermore, four sets of detailed analysis were performed to evaluate the performances of these tools. I demonstrated that my adapter trimming method is flexible, accurate and efficient for Next Generation Sequencing (NGS) analysis. Type of Report: MS Project Report Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Master’s Project Report LCScanner: An Efficient and Accurate Trimming Tool for Illumina Next Generation Sequencing Reads by Xiang Zhou Master of Engineering in Computer Science Washington University in St. Louis, 2012 Research Advisor: Professor Weixiong Zhang

Gene expression data for multiple biological and environmental conditions is being collected for multiple species. Functional modules and subnetwork biomarkers discovery have traditionally been based on analyzing a single gene expression dataset. Research has focused on discovering modules from multiple gene expression datasets. Gene coexpression network mining methods have been proposed for mining frequent functional modules. Moreover, biclustering algorithms have been proposed to allow for missing coexpression links. Existing approaches report a large number of edgesets that have high overlap. In this work, we propose an algorithm to mine frequent dense modules from multiple coexpression networks using a post-processing data summarization method. Our algorithm mines a succinct set of representative subgraphs that have little overlap which reduce the downstream analysis of the reported modules. Experiments on human gene expression data show that the reported modules are biologically significant as evident by Gene Ontology molecular functions and KEGG pathways enrichment.

Based on the previously proposed Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, in this paper, we focus on the application of QPSO in gene expression data clustering which can be reduced to an optimization problem. The proposed clustering algorithm partitions the N patterns of the gene expression dataset into user-defined K categories to minimize the fitness function of Total Within-Cluster Variation. Thus a partition with high performance is obtained. The experiment results on four gene expression data sets show that our QPSO-based clustering algorithm will be an effective and promising tool for gene expression data analysis.

Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms. Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. Our methodology is to apply a clustering algorithm to the data from all but one experimental condition. The remaining condition is used to assess the predictive power of the resulting clusters-meaningful clusters should exhibit less variation in the remaining condition than clusters formed by chance. We successfully applied our methodology to compare six clustering algorithms on four gene expression data sets. We found our quantitative measures of cluster quality to be positively correlated with external standards of cluster quality.

Gene expression data clustering provides a powerful tool for studying functional relationships of genes in a biological process. Identifying correlated expression patterns of genes represents the basic challenge in this clustering problem. This paper describes a new framework for representing a set of multi-dimensional gene expression data as a Minimum Spanning Tree (MST), a concept from the graph theory. A key property of this representation is that each cluster of the expression data corresponds to one subtree of the MST, which rigorously converts a multi-dimensional clustering problem to a tree partitioning problem. We have demonstrated that though the inter-data relationship is greatly simplified in the MST representation, no essential information is lost for the purpose of clustering. Two key advantages in representing a set of multi-dimensional data as an MST are: (1) the simple structure of a tree facilitates efficient implementations of rigorous clustering algorithms, which otherwise are highly computationally challenging; and (2) as an MST-based clustering does not depend on detailed geometric shape of a cluster, it can overcome many of the problems faced by classical clustering algorithms. Based on the MST representation, we have developed a number of rigorous and efficient clustering algorithms, including two with guaranteed global optimality. We have implemented these algorithms as a computer software EXpression data Clustering Analysis and VisualizATiOn Resource (EXCAVATOR). To demonstrate its effectiveness, we have tested it on three data sets, i.e. expression data from yeast Saccharomyces cerevisiae, expression data in response of human fibroblasts to serum, and Arabidopsis expression data in response to chitin elicitation. The test results are highly encouraging. EXCAVATOR is available on request from the authors.

BackgroundGene set enrichment analysis (GSEA) is an important approach to the analysis of coordinate expression changes at a pathway level. Although many statistical and computational methods have been proposed for GSEA, the issue of a concordant integrative GSEA of multiple expression data sets has not been well addressed. Among different related data sets collected for the same or similar study purposes, it is important to identify pathways or gene sets with concordant enrichment.MethodsWe categorize the underlying true states of differential expression into three representative categories: no change, positive change and negative change. Due to data noise, what we observe from experiments may not indicate the underlying truth. Although these categories are not observed in practice, they can be considered in a mixture model framework. Then, we define the mathematical concept of concordant gene set enrichment and calculate its related probability based on a three-component multivariate normal mixture model. The related false discovery rate can be calculated and used to rank different gene sets.ResultsWe used three published lung cancer microarray gene expression data sets to illustrate our proposed method. One analysis based on the first two data sets was conducted to compare our result with a previous published result based on a GSEA conducted separately for each individual data set. This comparison illustrates the advantage of our proposed concordant integrative gene set enrichment analysis. Then, with a relatively new and larger pathway collection, we used our method to conduct an integrative analysis of the first two data sets and also all three data sets. Both results showed that many gene sets could be identified with low false discovery rates. A consistency between both results was also observed. A further exploration based on the KEGG cancer pathway collection showed that a majority of these pathways could be identified by our proposed method.ConclusionsThis study illustrates that we can improve detection power and discovery consistency through a concordant integrative analysis of multiple large-scale two-sample gene expression data sets.

MotivationDeciphering gene interaction networks (GINs) from time-course gene expression (TCGx) data is highly valuable to understand gene behaviors (e.g., activation, inhibition, time-lagged causality) at the system level. Existing methods usually use a global or local proximity measure to infer GINs from a single dataset. As the noise contained in a single data set is hardly self-resolved, the results are sometimes not reliable. Also, these proximity measurements cannot handle the co-existence of the various in vivo positive, negative and time-lagged gene interactions.Methods and resultsWe propose to infer reliable GINs from multiple TCGx datasets using a novel conserved subsequential pattern of gene expression. A subsequential pattern is a maximal subset of genes sharing positive, negative or time-lagged correlations of one expression template on their own subsets of time points. Based on these patterns, a GIN can be built from each of the datasets. It is assumed that reliable gene interactions would be detected repeatedly. We thus use conserved gene pairs from the individual GINs of the multiple TCGx datasets to construct a reliable GIN for a species. We apply our method on six TCGx datasets related to yeast cell cycle, and validate the reliable GINs using protein interaction networks, biopathways and transcription factor-gene regulations. We also compare the reliable GINs with those GINs reconstructed by a global proximity measure Pearson correlation coefficient method from single datasets. It has been demonstrated that our reliable GINs achieve much better prediction performance especially with much higher precision. The functional enrichment analysis also suggests that gene sets in a reliable GIN are more functionally significant. Our method is especially useful to decipher GINs from multiple TCGx datasets related to less studied organisms where little knowledge is available except gene expression data.

BackgroundA complete understanding of the regulatory mechanisms of gene expression is the next important issue of genomics. Many bioinformaticians have developed methods and algorithms for predicting transcriptional regulatory mechanisms from sequence, gene expression, and binding data. However, most of these studies involved the use of yeast which has much simpler regulatory networks than human and has many genome wide binding data and gene expression data under diverse conditions. Studies of genome wide transcriptional networks of human genomes currently lag behind those of yeast.ResultsWe report herein a new method that combines gene expression data analysis with promoter analysis to infer transcriptional regulatory elements of human genes. The Z scores from the application of gene set analysis with gene sets of transcription factor binding sites (TFBSs) were successfully used to represent the activity of TFBSs in a given microarray data set. A significant correlation between the Z scores of gene sets of TFBSs and individual genes across multiple conditions permitted successful identification of many known human transcriptional regulatory elements of genes as well as the prediction of numerous putative TFBSs of many genes which will constitute a good starting point for further experiments. Using Z scores of gene sets of TFBSs produced better predictions than the use of mRNA levels of a transcription factor itself, suggesting that the Z scores of gene sets of TFBSs better represent diverse mechanisms for changing the activity of transcription factors in the cell. In addition, cis-regulatory modules, combinations of co-acting TFBSs, were readily identified by our analysis.ConclusionBy a strategic combination of gene set level analysis of gene expression data sets and promoter analysis, we were able to identify and predict many transcriptional regulatory elements of human genes. We conclude that this approach will aid in decoding some of the important transcriptional regulatory elements of human genes.