Seminars/Events
UPCOMING SEMINARS:
Expression Divergence After Speciation or Gene Duplication
Xun Gu - Professor, Department of Genetics, Development, and Cell Biology-Genetics, Iowa State University
Tuesday, July 10th, 2007, 1:30pm676 N. St. Clair Street, 12th Floor
CHICAGO campus
NOTE: UNUSUAL TIME AND LOCATION FOR CBB SEMINAR!
PAST SEMINARS:
Statistical considerations on the process of discovering and validating biomarker candidates using MS platforms
Cliff Spiegelman - Professor, Department of Statistics, Texas A&M University
Friday, May 25th, 2007, 3pmCOOK 3118 A&B
NOTE: UNUSUAL TIME FOR CBB SEMINAR!
Co-sponsored by CBB, the Department of Statistics, and Cells to Society
ABSTRACT: Claims have been made that the application of supervised pattern recognition methodology can be used with MS proteomic data to achieve near perfect sensitivity and specificity for detecting early stage cancer. So far those claims have not been verified in part due to the use of less than optimal experimental design, but in the interim significant effort has been spent on proteomic biomarker discovery research (without significant positive results) largely using tandem MS platforms. Underpinning the proteomics studies are several key components including standardization of materials, bioinformatics, reagent development, MS improvements, and statistics. This presentation discusses the NCI CPTAC program generally and a related mouse studies project. Several areas where statistical design of experiment input is present will be discussed.
Evaluation of Partial Least Squares for Brain Region Connectivity Analysis
Devdutta Warhadpande – CBB Master's Student
Tuesday, May 22, 2007, 5pmABSTRACT: The field of functional neuro-imaging is growing rapidly and within it the sub-field of functional connectivity analysis is also growing. Among the various analytical methods developed for this network analysis is the partial least squares method. This method has been implemented as a MATLAB based program and was evaluated in this study for its capabilities. The study was conducted on a sample data acquired from Patrick Wong’s lab. The purpose of this paper is to report the findings briefly. The mechanics of the PLS program and the mathematical method are described in short. The results from the preliminary analysis are reported and discussed in an attempt to better interpret them. This research is still in progress and more work needs to be done.
RESEARCH ADVISORS:
Patrick Wong - School of Communication
JiPing Wang - Department of Statistics
Cubic Time Recognition and Enumeration of Directed Path Graphs and Their Application to Protein Protein Interaction Networks
Pattrapong Patrick Charoenpong - CBB Master's Student, Final Presentation and Examination
Friday, May 18, 2007, 12 noonCOOK 3118 A&B
NOTE: UNUSUAL TIME FOR CBB SEMINAR!
ABSTRACT: There exists a generalization of interval graphs known as rooted directed path graphs. These are graphs whose nodes and edges represent the intersection of intervals which lie along a rooted tree. I present a cubic time algorithm that for any given graph recognizes if the graph is a rooted directed path graph and concisely produces all possible tree interval representations the graph can represent. Rooted directed path graphs have an application in detecting relationships in protein-protein interaction graphs. Specifically, rooted directed path graphs may be used to uncover information about the ordering of a branched pathway from protein-protein interaction data by modeling proteins as intervals along the pathway.
Micromechanical Study of DNA-Protein Interactions and Chromosome Structure
John F. Marko - Professor, Biochemistry, Molecular Biology & Cell Biology, Weinberg College of Arts and Sciences
Tuesday, May 15th, 2007, 5pmCOOK 3118 A&B
ABSTRACT: I will discuss the use of micromechanical assays - essentially measurements of elasticity - as methods to study how DNA is organized by being folded by proteins, ultimately into whole chromosomes. I will discuss studies at three scales of complexity. First, I will present results of single-DNA micromanipulation studies of proteins which compact the chromosome in the bacterium E. coli. Next, I will show how similar techniques can be applied to the study of the dynamics of assembly of chromatin fiber onto a single DNA molecule. Finally I will discuss experiments which probe the internal organization of entire mitotic chromosomes isolated from dividing cells.
Opportunities to Use Bioinformatics to Understand Cellular and Organismal Responses to CO2
Gregory Beitel - Assistant Professor, Biochemistry, Molecular Biology & Cell Biology, Weinberg College of Arts and Sciences
Tuesday, May 8th, 2007, 5pmCOOK 3118 A&B
ABSTRACT: Bioinformatics has not yet been applied to any aspect of the cellular or organismal responses to the elevated CO2 levels present in about 40% of the >350,000 patients hospitalized by chronic obstruct pulmonary disease (COPD) each year. Currently COPD is the fourth leading cause of death in the US, exceeded only by cancer, heart disease and stroke. The cellular pathways that sense and respond to CO2 in non-neuronal cells have not yet been defined in any system, and we invite bioinformaticists to collaborate with us in understanding both the responses and the mechanisms of the responses to CO2. In this seminar we will present our current results and outline the opportunities to use bioinformatics approaches in understanding hypercapnia. In a collaboration between the Beitel lab (BMBCB, Evanston) and the Sporn lab (Pulmonary and Critical Care lab, Feinberg School of Medicine, Chicago), we have shown that in both Drosophila and vertebrate immune cells, the induction of host defense genes downstream of the NFkB-family of transcription factors is impaired by elevated CO2 levels. Responses are downregulated five to several hundred fold. Both groups have used genome wide microarray analysis to identify specific sets of genes that are regulated by CO2 that are distinct from genes regulated by hypoxia or environmental stress. Surprisingly, even exposures of up to 20% CO2 do not induce general stress response and heat shock proteins are downregulated rather than upregulated. We see a great opportunity for a bioinformatics analysis of this data to understand the systems biology of the responses to CO2 and how CO2 interacts with other stress response pathway. We also see an opportunity for bioinformatics analysis in understanding the mechanisms of the transcriptional response to CO2. Our current data suggests that at least one mode of CO2 regulation is exerted via transcriptional effects on individual genes, analogously to how O2 levels are transduced through the HIF transcription factor. We have obtained a 2.2 kb Drosophila promoter element that is CO2-responsive in a luciferase reporter construct. We are currently delineating the CO2-responsive elements in this promoter. Given that we have genes sets of genes that are regulated by or insensitive to CO2, as well as a promoter fragment that confers CO2-responsiveness, it should be possible to use bioinformatics to identify sequence elements that are unique to CO2-regulated genes. We would expect that such elements would be conserved and should be found upstream many genes identified in the array analysis.
Improving Model Predictions for RNA Interference Activities That Use Support Vector Machine Regression by Combining and Filtering Features
Andrew S. Peek - Director of Bioinformatics, Integrated DNA Technologies, Inc.
Tuesday, May 1, 2007, 5pmCOOK 3118A&B
ABSTRACT: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities. Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5’ most base of the guide strand is the most informative.
Geneious – An Integrated Bioinformatics Tool Suite to Speed up your Research
Tobias Thierer - Biomatters Ltd.
Tuesday, April 24, 2007, 5pmCOOK 3118A&B
ABSTRACT: The integrated and cross-platform bioinformatics tool suite Geneious, the basic version of which is free for academic use, has the declared goal of drastically speeding up bioinformatic research. Geneious packages all the fundamental bioinformatics tasks such as DNA or amino acid sequence alignments, inference of phylogenetic trees, 3d structures, publication searches and reference management, and it provides a highly intuitive and powerful interface to various databases such as the ones from NCBI. Like its role model Eclipse, it consists of a slim framework with numerous plugins and an open API, so anyone can write new plugins within hours using Java 5; Existing community plugins include MrBayes and transmembrane domain prediction. The commercial pro version also offers ClustalW integration, sequence and annotation editing, contig assembly, restriction analysis, p2p collaboration, ORF finding, PFAM integration and more. As one of the developers of Geneious, I will give a live demonstration of this software and answer any questions that may arise. This talk should be of interest to researchers in computational biology and bioinformatics, and to their students.
Neural Bases of Second Language Learning
Patrick C. M. Wong – Roxelyn and Richard Pepper Assistant Professor, Communication Sciences and Disorders, School of Communication
Tuesday, April 17th, 2007, 5pmABSTRACT: A remarkable characteristic of the human nervous system is its ability to learn to integrate novel (foreign) complex sounds into words. However, the neural plasticity underlying how adults learn to integrate novel sounds into words and the associated individual differences is largely unknown. Unlike English, most languages of the world, called tone languages, use pitch patterns to mark individual word meaning. I will report a series of experiments assessing the behavioral (including music experience), neurophysiologic (ERP and fMRI), and neuroanatomic correlates of learning to use these pitch patterns in words by English-speaking adults who have had no previous exposure to such usage. The association between degrees of learning and range of neural differences, from the rostral brainstem to Heschl’s Gyrus to the auditory association cortex, including possible efferent tuning effects, will be discussed.
Statistical Analysis of the Global Regulatory Role of Histone Acetylation in Saccharomyces cerevisiae
Ping Ma - Assistant Professor, Department of Statistics, University of Illinois at Urbana-Champaign
Tuesday, April 3, 2007, 5pmABSTRACT: Histone acetylation plays important but incompletely understood roles in gene regulation. A comprehensive understanding of the regulatory role of histone acetylation is difficult because many different histone acetylation patterns exist and their effects are confounded by other factors, such as the transcription factor binding sequence motif information and nucleosome occupancy. In this talk, I will present some of our work in analyzing genome-wide histone acetylation data using a few complementary statistical models and tested some important hypotheses on the global regulatory effect of histone acetylation.
Evaluation of Partial Least Squares for Brain Region Connectivity Analysis
Devdutta Warhadpande – CBB Master's Student
Tuesday, March 27th, 2007, 5pmABSTRACT: The field of functional neuro-imaging is growing rapidly and within it the sub-field of functional connectivity analysis is also growing. Among the various analytical methods developed for this network analysis is the partial least squares method. This method has been implemented as a MATLAB based program and was evaluated in this study for its capabilities. The study was conducted on a sample data acquired from Patrick Wong’s lab. The purpose of this paper is to report the findings briefly. The mechanics of the PLS program and the mathematical method are described in short. The results from the preliminary analysis are reported and discussed in an attempt to better interpret them. This research is still in progress and more work needs to be done.
RESEARCH ADVISORS:
Patrick Wong - School of Communication
JiPing Wang - Department of Statistics
A Model-based Approach to Selection of Tag SNPs
Lei Li - Assistant Professor, Biological Sciences & Mathematics Departments, University of Southern California
Tuesday, March 6th, 2007, 5pmABSTRACT: Single Nucleotide Polymorphisms (SNPs) are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identification of sets of tag SNPs that capture as much haplotype information as possible. Tag SNP selection is analogous to the problem of data compression in information theory. According to Shannon's framework, the optimal tag set maximizes the entropy of the tag SNPs subject to constraints on the number of SNPs. This approach requires an appropriate probabilistic model. Compared to simple measures of Linkage Disequilibrium (LD), a good model of haplotype sequences can more accurately account for LD structure. It also provides a machinery for the prediction of tagged SNPs and thereby to assess the performances of tag sets through their ability to predict larger sets of SNPs. Here, we compute the description code-lengths of SNP data for an array of models and we develop tag SNP selection methods based on these models and the strategy of enrtopy maximization. Using data sets from the HapMap and ENCODE projects, we show that the hidden Markov model introduced by Li and Stephens outperforms the other models in several aspects: description code-length of SNP data, information content of tag sets, and prediction of tagged SNPs. This is the first use of this model in the context of tag SNPs selection. Our study provides strong evidence that the tag sets selected by our method are better than those chosen by several existing methods. The results also suggest that information content evaluated with a good model is more sensitive for assessing the quality of a tagging set than the average rate of correct tagged SNPs prediction.