| Accepted Posters |
| Category 'G'- Functional Genomics' |
| Poster G01 |
| Prokaryotic-wide gold standards |
| Gabriel Moreno-Hagelsieb- Wilfrid Laurier University |
| Sarath Janga (MRC-Laboratory of Molecular Biology, MRC-Laboratory of Molecular Biology); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster G02 |
| Regulation of gene expression via mRNA secondary structure elements |
| Barrett Foat- Washington University, School of Medicine |
| Gary Stormo (Washington University, School of Medicine, Department of Genetics); |
| Short Abstract: To enable a more complete understanding of the regulation of gene expression, including mRNA decay, localization, and translation, I have developed an integrative computational approach that leverages both functional genomics data and nucleotide sequences to discover RNA secondary structure-defined cis-regulatory elements. |
| Long Abstract: Click Here |
| Poster G03 |
| Cross-species cluster co-conservation: a new method for generating protein interaction networks |
| Anis Karimpour-Fard- Center for Computational Pharmacology, University of Colorado School of Medicine |
| Corrella Detweiler (University of Colorado, MCDB); Ryan Gill (University of Colorado, Chemical and Biological Engineering); Lawrence Hunter (University of Colorado School of Medicine, Center for Computational Pharmacology); |
| Short Abstract: Co-conservation (phylogenetic profiles) is a well-established method for predicting functional relationships between proteins. Cluster co-conservation (CCC) has previously been limited to interactions within a single target species. We have extended CCC to develop protein interaction networks based on co-conservation between protein pairs across multiple species, cross-species cluster co-conservation (CS-CCC). |
| Long Abstract: Click Here |
| Poster G04 |
| The evolution of protein function driven by a multi-domain repertoire |
| Syed Ali- Centre for Bioinformatics, Imperial College London |
| Michael Sternberg (Centre for Bioinformatics, Imperial College London, Molecular Biosciences); |
| Short Abstract: We present a novel map of protein domain (SCOP) combinations to functions (GO) using co-occurrence scores, to allow a pan-genomic analysis of functional evolution. Using simple metrics to define change in domain organisation and function we analysed functional transfer via domains, showing a clear correlation between domain combination and function. |
| Long Abstract: Click Here |
| Poster G05 |
| Inference of the Molecular Mechanism of Action from Genetic Interaction and Gene Expression Data |
| Uros Petrovic- Jozef Stefan Institute |
| Tomaz Curk (University of Ljubljana, Faculty of Computer and Information Sciences); Mojca Mattiazzi (Jozef Stefan Institute, Department of Molecular and Biomedical Sciences); Blaz Zupan (University of Ljubljana, Faculty of Computer and Information Sciences); |
| Short Abstract: We propose a rationale and a set of rules which, rather than correlating findings from gene expression and genetic interaction data, uses each type of data independently to integrate the results into a single network, defining the nature and orientation of the relationship between the perturbing agent and pathway regulators. |
| Long Abstract: Click Here |
| Poster G06 |
| Data management and Biological Knowledge Representation in the Mouse |
| Li Ni- The Jackson Laboratory |
| Carol Bult (The Jackson Laboratory, Mouse Genome Informatics); Jim Kadin (The Jackson Laboratory, Mouse Genome Informatics); Joel Richardson (The Jackson Laboratory, Mouse Genome Informatics); Martin Ringwald (The Jackson Laboratory, Mouse Genome Informatics); Janan Eppig (The Jackson Laboratory, Mouse Genome Informatics); Judith Blake (The Jackson Laboratory, Mouse Genome Informatics); MGI Group (The Jackson Laboratory, Mouse Genome Informatics); |
| Short Abstract: Mouse Genome Informatics (MGI) incorporates multiple structured vocabularies in managing, annotating, and analyzing complex biological data. These include the Gene Ontology, the Mouse Anatomical Dictionary, and the Mouse Phenotype Ontology. The incorporation of multiple semantic standards enhances the utility of the MGI system for representing mouse biology. |
| Long Abstract: Click Here |
| Poster G07 |
| Liver Transcription Profiling Reveals a Major Role of TLR3 in Developing Metabolic Disorders in a Diet Induced Obesity Model |
| Chris Huang- Centocor R&D, Inc. |
| Linda Wu (Centocor R&D, Inc, Biology Research); Christine Ward (Centocor R&D, Inc, Biology Research); Vedrana Stojanovic-Susulic (Centocor R&D, Inc, Biology Research); |
| Short Abstract: By microarray analysis, approximately 1000 genes were differentially expressed in wild type mice on the high-fat diet compared to mice on normal chow diet. Intriguingly, the direction of changes was reversed in as much as a quarter of these genes in the TLR3 knockout mice also on high-fat diet. |
| Long Abstract: Click Here |
| Poster G08 |
| The effect of spectral pre-processing on metabolomics fingerprinting |
| Miroslava Cuperlovic-Culf- National Research Council |
| Adrian Culf (Mount Allison University, Chemistry); Nabil Belacel (National Research Council, Institute for Information Technology); Christa Wang (National Research Council , Institute for Information Technology); |
| Short Abstract: Optimization of pre-processing and analysis methods is needed in order to fully benefit from NMR metabolomics. Several different phasing, scaling and normalization strategies were tested for their effect on data variance. These differently pre-processed datasets were utilized for classification using different classification methods and different distance matrices calculation procedures. |
| Long Abstract: Click Here |
| Poster G09 |
| Feature Evaluation and Prediction of DNaseI Hypersensitive Sites from Human Genomic Sequences |
| Zhong Guo- University of Pittsburgh |
| Liang Zheng (University of Pittsburgh, Medicine); Takis Benos (University of Pittsburgh, Computational Biology); Vanathi Gopalakrishnan (University of Pittsburgh, Biomedical Informatics); |
| Short Abstract: Our study reveals that four important features (2kb upstream, CpG, MCS and cTFBS) are strongly associated with DNaseI hypersensitive sites (DHS). After training these features with different combinations, an effective support vector machine (SVM) classifier can achieve a satisfactory performance to distinguish DHS from the human genomic sequences. |
| Long Abstract: Click Here |
| Poster G10 |
| How to forget about clustering and be successful in your gene expression analysis |
| Daniele Merico- University of Toronto |
| Andrew Emili (University of Toronto, Banting and Best Department of Medical Research); Gary Bader (University of Toronto, Banting and Best Department of Medical Research); |
| Short Abstract: A classical approach to time-course gene expression analysis is to cluster the genes into discrete sets, and then compute functional enrichments. We propose an alternative approach, able to overcome the major drawbacks of clustering. We present the results obtained analyzing the transcriptional profile of a murine heart failure model. |
| Long Abstract: Click Here |
| Poster G11 |
| Conditional Random Pattern Model based Copy Number Variants Detection for Myelodysplastic Syndromes Using High Density Single Nucleotide Polymorphism Array |
| Xiaobo Zhou- TMHRI, Weill Medical College of Cornell University |
| Fuhai Li (TMHRI, Radiology); |
| Short Abstract: In this study, we propose a novel conditional random pattern (CRP) model based genome-wide copy number variants (CNVs) detection approach for Myelodysplastic Syndromes (MDSs) using high density single nucleotide polymorphism (SNP) array. In CRP model, more contextual cues are explored to detect CNVs by simultaneously considering several continuous SNP loci. |
| Long Abstract: Click Here |
| Poster G12 |
| Investigating functional evolution in gene families using microarray expression profiles |
| Owen Woody- University of Waterloo |
| Brendan McConkey (University of Waterloo, Biology); Andrew Doxey (University of Waterloo, Biology); |
| Short Abstract: We present a methodology for incorporating microarray expression profiles into studies of functional evolution in gene families. Various microarray normalizations, signal quantifications, and profile definitions were examined and their merits evaluated on several datasets. Our method provides an integrated perspective on sequence and regulatory evolution. |
| Long Abstract: Click Here |
| Poster G13 |
| Association Analysis-based Extraction of Functional Information from Protein Interaction and Microarray Gene Expression Data |
| Gaurav Pandey- University of Minnesota, Twin Cities |
| Michael Steinbach (University of Minnesota, Twin Cities, Department of Computer Science and Engineering); Rohit Gupta` (University of Minnesota, Twin Cities, Department of Computer Science and Engineering); Gowtham Atluri (University of Minnesota, Twin Cities, Department of Computer Science and Engineering); Tushar Garg (University of Minnesota, Twin Cities, Department of Computer Science and Engineering); Vipin Kumar (University of Minnesota, Twin Cities, Department of Computer Science and Engineering); Hui Xiong ( Rutgers, the State University of New Jersey, Management Science and Information Systems Department); Chris Ding (University of Texas at Arlington, Department of Computer Science and Engineering ); Xiaofeng He (Lawrance Berkeley National Lab, Department of Structural Biology and Computational and Theoretical Biology); Stephen Holbrook (Lawrance Berkeley National Lab, Department of Structural Biology and Computational and Theoretical Biology); Ya Zhang (Penn State University, Information Science and Technology); |
| Short Abstract: We demonstrate how association analysis techniques, such as hypercliques and h-confidence, can help extract accurate functional information from biological datasets efficiently. In particular, we illustrate how these technique can be used to extract functional modules from protein complex and microarray data, and eliminate noisy interactions from protein interaction networks. |
| Long Abstract: Click Here |
| Poster G14 |
| Modelling the evolution of gene expression in non-homogeneous datasets |
| Gerald Quon- University of Toronto |
| Quaid Morris (Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Computer Science); |
| Short Abstract: We have developed a novel statistical model to address the challenge of assessing conservation in gene expression in non-homogeneous datasets. We applied this model to a vertebrate gene expression dataset to demonstrate its ability to find both conserved and non-conserved instances of expression evolution. |
| Long Abstract: Click Here |
| Poster G15 |
| A High-Throughput System for Detecting and Analyzing Physical Gene Clusters |
| Debra Burhans- Canisius College |
| Roberto Lleras (Canisius College, Bioinformatics); Christopher Hoeflich (University at Buffalo, Computer Science and Engineering); Lyndsy Kron (University of Illinois, Computer Science); Doreen Ware (Cold Spring Harbor Laboratory, Computational Biology); |
| Short Abstract: We present Globorum, a system for analyzing and storing microarray data that enables a user to explore physical gene clusters. The system is based on the Pyxis program, developed for finding physical clusters in yeast microarray datasets. Globorum allows for the upload of any genome and data. |
| Long Abstract: Click Here |
| Poster G16 |
| A functional genomics analysis of central carbon metabolism evolution in yeasts |
| Mark Styczynski- Massachusetts Institute of Technology |
| Dawn Thompson (Massachusetts Institute of Technology, Broad Institute); Jenna Pfiffner (Massachusetts Institute of Technology, Broad Institute); Courtney French (Massachusetts Institute of Technology, Broad Institute); Aviv Regev (Massachusetts Institute of Technology, Broad Institute); |
| Short Abstract: We present a comparative functional genomics analysis of 13 different species of yeast, focusing on metabolite and metabolic flux profiling. We use these functional profiles, together with transcriptional data, to identify functional modules and reconstruct the evolution of central carbon metabolism modules in yeast. |
| Long Abstract: Click Here |
| Poster G17 |
| Bayesian Integration of Heterogeneous Datasets to Predict Gene Function in the Malaria Parasite Plasmodium falciparum: Prediction of novel exported proteins |
| Rachel Sealfon- Princeton University |
| Manuel Llinas (Princeton University, Molecular Biology); Olga Troyanskaya (Princeton University, Computer Science); |
| Short Abstract: The eukaryotic parasite Plasmodium falciparum is responsible for the most virulent form of malaria in humans. We use a Bayesian network to accurately integrate heterogeneous data sources and create a genome-wide functional interaction map for P. falciparum. Using this map, we identify novel candidate exported proteins. |
| Long Abstract: Click Here |
| Poster G18 |
| A strategy for mapping the complete genetic interaction network in yeast |
| Anastasia Baryshnikova- University of Toronto |
| Chad L. Myers (University of Minnesota, Department of Computer Science and Engineering); Michael Costanzo (University of Toronto, Banting and Best Department of Medical Research, Department of Molecular Genetics, and Terrence Donnelly Center for Cellular and Biomolecular Research); Judice Koh (University of Toronto, Banting and Best Department of Medical Research, Department of Molecular Genetics, and Terrence Donnelly Center for Cellular and Biomolecular Research); Matthew A. Hibbs (Princeton University, Lewis-Sigler Institute for Integrative Genomics, and Department of Computer Science); Olga G. Troyanskaya (Princeton University, Lewis-Sigler Institute for Integrative Genomics, and Department of Computer Science); Gary D. Bader (University of Toronto, Terrence Donnelly Centre for Cellular and Biomolecular Research); Charles Boone (University of Toronto, Banting and Best Department of Medical Research, Department of Molecular Genetics, and Terrence Donnelly Center for Cellular and Biomolecular Research); |
| Short Abstract: Synthetic genetic array (SGA) experiments provide high-throughput genetic interaction data. We have developed a computational strategy to map the yeast genetic interaction network, and a quantitative measure of genetic interactions. We show how genetic interactions can help to identify functionally related genes, and compare genetic interactions to other genomic datasets. |
| Long Abstract: Click Here |
| Poster G19 |
| Computational Identification of functional related gene in Malaria parasites |
| Jelili Oyelade- Covenant University |
| Ezekiel Adebiyi (Covenant University, Ota, Computer & Information Sciences); Clarance Yah (Covenant University, Ota, Biological Sciences); Grace Olasehinde (Covenant University, Ota, Biological Sciences); |
| Short Abstract: P. falciparum, is the most deadly form of malaria. Several computational methods have been used to identify genes clustering. Grouping of genes that are co-regulated in a metabolic pathway is very important in drug prediction. We applied k-means clustering tool to classify genes into their various metabolic pathways. |
| Long Abstract: Click Here |
| Poster G20 |
| Genomic Profiling of Blood for Stroke Diagnosis |
| Kory Johnson- NINDS, NIH |
| Alison Baird (SUNY Downstate Medical Center, Department of Neurology); Yang Fann (NINDS, NIH, DIR, ITP); |
| Short Abstract: To augment stroke management in the emergency setting, a blood-based genomic-based stroke diagnosis system was devised. The system includes three classification models: General Stroke Event, Hemorrhagic Stroke Event, and Ischemic Stroke Event. Such that, system accuracy, under cross-validation, is ~91% for stroke event and ~77% for stroke type. |
| Long Abstract: Click Here |
| Poster G21 |
| Development of Bovine Peptide Array to Measure Kinome Responses of Bovine Monocytes to LPS or CpG Treatment |
| shakiba jalal- University of Saskatchwan |
| No additional authors |
| Short Abstract: We report the construction of a bovine array of 300 unique peptides and its application for kinome analysis of monocytes stimulated with lipopolysaccharide (LPS) or CpG-ODNs; ligands for Toll-like receptors (TLR) 4 and 9, respectively. |
| Long Abstract: Click Here |
| Poster G22 |
| Connect the dots: Exposing hidden protein family connections from the entire sequence tree |
| Yaniv Loewenstein- The Hebrew University of Jerusalem |
| Michal Linial (The Hebrew University of Jerusalem, Department of biological chemistry); |
| Short Abstract: We present an alternative to profile methods for detecting remotely homologous protein families, based on a global scheme organizing all sequences into an evolutionary-driven tree. Our superfamily predictions are validated using Pfam clans, SCOP and SUPERFAMILY. We systematically supply hundreds of previously overlooked evolutionary connections - detailed examples are provided. |
| Long Abstract: Click Here |
| Poster G23 |
| Variation on regulatory motifs is associated with distinct binding site occurrences |
| Huai-Kuang Tsai- Academia Sinica |
| Sufeng Chiang (Academia Sinica, Institute of Information Science); |
| Short Abstract: We find that in Saccharomyces cerevisiae, more than 80% of the degenerate positions are associated with occurrence of other TFs. We further demonstrate that for a TF, there is a positive correlation between the number of its co-occurring TFs and the number of the degenerate positions in its consensus. |
| Long Abstract: Click Here |
| Poster G24 |
| A computational pipeline for the prediction of transcription factor binding sites in Arabidopsis thaliana applied to tissue-specific expression and abiotic stress response. |
| Ryan Austin- University of Toronto |
| Nicholas Provart (University of Toronto, Cell & Systems Biology); |
| Short Abstract: A combination of popular cis-prediction programs and an in-house heuristical approach have been used to assess the enrichment of cis-regulatory motifs under a non-parametrically determined, additive model. The promoters of tightly coregulated gene clusters specific to tissue expression and abiotic stress reveal many modular elements. |
| Long Abstract: Click Here |
| Poster G25 |
| Exploring relationship between biological entities accross domains |
| Charles Hefer- University of Pretoria |
| Fourie Joubert (University of Pretoria, Bioinformatics and Computational Biology Unit); |
| Short Abstract: Across biological domains, biological entities display a high degree of relatedness. These relations can be used to evaluate additional functions and annotations of unknown entities. Relationship exploration between entities in a sequence workbench based on analysis results and homology between sequences is presented here. |
| Long Abstract: Click Here |
| Poster G26 |
| Probeset quality hinders analysis of Affymetrix exon data: Improved filters are effective |
| Peter Munson- National Institutes of Health |
| Jennifer Barb (National Institutes of Health, Center for Information Technology); |
| Short Abstract: Screening for alternative splicing of mRNA is facilitated with the Affymetrix human Exon microarray, but analysis of such data remains problematic. We investigate the shortcomings of a standard ANOVA model and propose several statistical and bioinformatics solutions, illustrated on several data sets. We also describe publicly-available analysis scripts. |
| Long Abstract: Click Here |
| Poster G27 |
| System Support for Collaborative Genomics Visualizations |
| Lars Bongo- Princeton University |
| Daniel Stødle (University of Tromsø, Computer Science); Grant Wallace (Princeton University, Computer Science); Tore Larsen (University of Tromsø, Computer Science); Kai Li (Princeton University, Computer Science); Olga Troyanskaya (Princeton University, Lewis-Sigler Institute for Integrative Genomics); |
| Short Abstract: We have developed a methodology for integrative analysis and visualization of functional genomics experiment data for co-located users, and for users communicating over the Internet. It provides multi-user gesture-based interaction with applications run on display walls, and efficient compression of genomics visualizations for interactive multi-location collaboration. |
| Long Abstract: Click Here |
| Accepted Posters |
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