Accepted Posters
|
Category 'N'- Microarrays' |
| Poster N01 |
| INSECTICIDE RESISTANCE MECHANISMS AND GENE EXPRESSION PROFILE IN MALARIA VECTOR - Anopheles gambiae s.s |
| ISAAC OYEWOLE- BABCOCK UNIVERSITY |
| SAMSON AWOLOLA (NIGERIA INSTITUTE OF MEDICAL RESEARCH- , PUBLIC HEALTH); Adedayo Oduola (NIGERIA INSTITUTE OF MEDICAL RESEARCH- , PUBLIC HEALTH); Lizette Koekemoer (National Institute for Communicable Diseases,, VectControl Reference Unit,); Maureen Coetzee (National Institute for Communicable Diseases,, VectControl Reference Unit,); Clare Strode (Liverpool School of Tropical Medicine,, Vector Group); |
| Short Abstract: Genes associated with insecticide resistance in Anopheles gambiae was performed using microarray techniques. The genes over-expressed in the resistant strain: cuticular genes (CPLC8 and CPLC#), a cytochrome P450 (CYP325A3 ), a sigma class GST (GSTS1- 2 ) and a thioredoxin peroxidase (TPX4 )gene . TPX2in the susceptible strain. |
| Long Abstract:Click Here |
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| Poster N02 |
| miRNA data integration |
| Pekka Kohonen- University of Turku |
| Pekka Kohonen (University of Turku, BTK/VTT MBT); Päivi Östling (VTT Technical Research Centre of Finland, Medical Biotechnology); Suvi-Katri Leivonen (VTT Technical Research Centre of Finland, Medical Biotechnology); Saija Haapa-Paananen (VTT Technical Research Centre of Finland, Medical Biotechnology); Maija Wolf (University of Helsinki, Institute for Molecular Medicine Finland ); Henrik Edgren (University of Helsinki, Institute for Molecular Medicine Finland ); Merja Perälä (VTT Technical Research Centre of Finland, Medical Biotechnology); Olli Kallioniemi (VTT Technical Research Centre of Finland, Medical Biotechnology); |
| Short Abstract: We used Agilent miRNA expression arrays for the miRNA profiling and Affymetrix hgu133plus2 arrays for the gene expression (mRNA) profiling. Differentially expressed miRNAs in a panel of 37 breast, prostate and brain cancer cell lines were identified and connected to genes and pathways that are negatively correlating with miRNAs. |
| Long Abstract:Click Here |
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| Poster N03 |
| Correcting Array Position Artifacts using GPU Computing |
| Dirar Homouz- University of Texas Medical Branch |
| Gang Chen (University of Texas Medical Branch, Institute for Translational Sciences); Andrzej Kudlicki (University of Texas Medical Branch, Biochemistry and Molecular Biology); |
| Short Abstract: The systematic excess correlations between different probes on Affymetrix® S98 chips are significantly dependent on the chip distance which points out to a positional artifact. We designed a model of the artifact, and an algorithm to correct it and provide new data sets with the corrected expression values. |
| Long Abstract:Click Here |
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| Poster N04 |
| Estimating the Proportion of Microarray Probes Expressed in an RNA sample |
| Wei Shi- The Walter and Eliza Hall Institute of Medical Research |
| Carolyn de Graaf (The Walter and Eliza Hall Institute of Medical Research, Molecular Medicine); Sarah Kinkel (The Walter and Eliza Hall Institute of Medical Research, Molecular Medicine); Ariel Achtman (The Walter and Eliza Hall Institute of Medical Research, Infection and Immunity); Tracey Baldwin (The Walter and Eliza Hall Institute of Medical Research, Molecular Medicine); Louis Schofield (The Walter and Eliza Hall Institute of Medical Research, nfection and Immunity); Hamish Scott (University of Adelaide, Adelaide Cancer Research Institute); Douglas Hilton (The Walter and Eliza Hall Institute of Medical Research, Molecular Medicine); Gordon Smyth (The Walter and Eliza Hall Institute of Medical Research, Bioinformatics); |
| Short Abstract: A novel method was proposed to estimate the transcriptome size by utilizing the negative controls available on Illumina expression BeadChips. Using this method, hematopoietic stem cells were found to have a larger transcriptome than progenitor cells. Aire knockout medullary thymic epithelial cells were shown to have significantly less expressed probes than matched wild-type cells. |
| Long Abstract:Click Here |
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| Poster N05 |
| gcExplorer: Interactive Exploration of Gene Clusters |
| Theresa Scharl- BOKU Vienna |
| No additional authors |
| Short Abstract: In this work we present new visualization methods for gene cluster solutions based on the neighborhood graph implemented in R package gcExplorer. For node representation different plot symbols visualizing single clusters are used displaying arbitrary properties of the corresponding data. |
| Long Abstract:Click Here |
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| Poster N06 |
| An Expression Profiling Data Repository for Lung Disease Gene Discovery |
| Soumyaroop Bhattacharya- University of Rochester |
| Thomas Mariani (University of Rochester, Pediatrics); Aditi Basu (Massachusetts General Hospital, Center for Human Genetic Research); |
| Short Abstract: Over the years,there has been tremendous increase in microarray studies to identify disease markers, but most are limited by insufficient sample size resulting in poor predictive inference. We have developed a repository of lung microarray data for improved inference by increasing sample size and ensuring consistent data processing. |
| Long Abstract:Click Here |
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| Poster N07 |
| The Exon Array Analyzer: a web interface for analyzing exon arrays |
| Pascal Gellert- Max-Planck-Institute |
| Shizuka Uchida (Max-Planck-Institute, Heart and Lung Research); Thomas Braun (Max-Planck-Institute, Heart and Lung Research); |
| Short Abstract: Affymetrix Exon Arrays (exon array) are splicing sensitive microarrays. Since their analysis is challenging, we developed a user-friendly web interface called "Exon Array Analyzer" (EAA). The EAA implements preprocessing, identification of differentially expressed exons and visualization, which makes it as a comprehensive tool for analyzing exon arrays. http://eaa.mpi-bn.mpg.de |
| Long Abstract:Click Here |
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| Poster N08 |
| Novel thermodynamics-based algorithm for probe-speci?c position-dependent hybridization free energy |
| Hosna Jabbari- University of British Columbia |
| Peter Clote (Boston College, Biology); |
| Short Abstract: We present a novel thermodynamics-based algorithm that computes the free energy of cross-hybridized structures causing non-speci?c binding by including position-dependent weights for nucleotides in the probe accounting for dependencies of binding strengths on distance from surface to compute the partition function for the DNA-RNA complexes. |
| Long Abstract:Click Here |
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| Poster N09 |
| Geometric representation of transcriptomic phase space provide by Molecular Dynamics MDS combine to SVD algorithms using references datasets. |
| Christophe Becavin- Institut des Hautes Études Scientifiques |
| Nicolas Tchitchek (Institut des Hautes Études Scientifiques, Systems Biology); Arndt Benecke (Institut des Hautes Études Scientifiques, Systems Biology); |
Short Abstract: We investigate the usefulness of combining Singular Value Decomposition to Molecular Dynamics driven Multidimensional Scaling.
Apply SVD-MDS algorithms to microarray datasets obtaining representation of transcriptomic phase-space, and compare geometric property of it through different datasets. |
| Long Abstract:Click Here |
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| Poster N10 |
| SNPMan: SNP Genotype Data Management System |
| Jongpill Choi- National Institute of Health, KCDC |
| Keun-Joon Park (National Institute of Health, KCDC, Center for Genome Science); Ki-Jung Park (National Institute of Health, KCDC, Center for Genome Science); Bok-Ghee Han (National Institute of Health, KCDC, Center for Genome Science); InSong Koh (College of Medicine, Hanyang University, Department of Physiology); |
| Short Abstract: We have developed a data management system (SNPMan) which uses both a file system and a database management system to efficiently manage the large scale SNP genotype data. The system supports easy management features for the large scale SNP genotype data with graphical user interface |
| Long Abstract:Click Here |
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| Poster N11 |
| Detecting mosaic structural variation in genomic microarray data |
| Jayne Hehir-Kwa- Radboud University Nijmegen Medical Centre |
| Ben Rodriguez-Santiago (Universitat Pompeu Fabra, Departament de Ciències Experimentals i de la Salut); Lisenka Vissers (Radboud University Nijmegen Medical Centre, Human Genetics); Rolph Pfundt (Radboud University Nijmegen Medical Centre, Human Genetics); Joris Veltman (Radboud University Nijmegen Medical Centre, Human Genetics); |
| Short Abstract: Genomic microarrays can robustly detect unbalanced structural variation. When such variation occurs mosaic the signal is obscured by the mix of affected and unaffected cells. Optimization of data analysis can improve the accurate detection of mosaic structural variation. Our method detects mosaic CNVs and UPDs by using allele-specific intensity data. |
| Long Abstract:Click Here |
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| Poster N12 |
| The LO-BaFL pipeline for microarray expression analysis |
| Cristina Baciu- UNC Charlotte |
| Jean-Luc Mougeot (Carolinas Medical Center, Charlotte, NC, Carolinas Neuromuscular/ALS-MDA Center, Neuroscience and Spine Institute); Jennifer Weller (University of North Carolina at Charlotte, Charlotte, NC 28223, Department of Bioinformatics and Genomics); |
| Short Abstract: We present a bioinformatics pipeline, LO-BaFL, for processing experiment measurements from long-oligo microarrays, modifying the short-oligo BaFL foundation. Pipeline steps filter probes for cross-hybridization, SNPs, and low binding affinity and samples for response range. A test case using Agilent 4x44k array measurements validates differential expression predictions with qRT-PCR assays. |
| Long Abstract:Click Here |
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| Poster N13 |
| Pathway reconstruction with cell-type-specific co-expression profiles in Arabidopsis root |
| Taeyun Oh- Yonsei university |
| Insuk Lee (Yonsei university, Biotechnology); |
Short Abstract: Multicelluar organisms reveal diverse transcriptional expression patterns in each cell-type, leading to the differential regulation of specific biological functions in subsets of cell layers.
For better understanding of cell-type-specific response to stress, we compare co-expression network derived from whole tissue and cell-type-specific transcription profiles in Arabidopsis root. |
| Long Abstract:Click Here |
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| Poster N14 |
| Identification of non reliable probes on customized Affymetrix Mouse430_2 platform |
| Noura Chelbat- Bioinformatik Institute, Johannes Kepler University |
| Ulrich Bodenhofer (Institute of Bioinformatics, Johannes Kepler University, Linz); Sepp Hochreiter (Institute of Bioinformatics, Johannes Kepler University, Linz); Kasim Adetayo (Center for Statistics, Hasselt University); Ziv Shkedy (Center for Statistics, Hasselt University); Willem Talloen (Johnson & Johnson Pharmaceutical Research & Development, Nonclinical Biostatistics); Hinrich W. H Göhlmann (Johnson & Johnson Pharmaceutical Research & Development, Nonclinical Biostatistics); |
| Short Abstract: Oligonucleotide probes on customized CDFs are based on the latest genome and transcriptome information. Non-reliable/bad probes miss signals which are detected by the majority of probes in a probe set. We detected on mice datasets that probes inconsistency can be discerned with accuracies between 60 and 70% even in alternative annotations |
| Long Abstract:Click Here |
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| Poster N15 |
| Accurate estimates of microarray target concentration from a simple sequence-independent Langmuir model |
| Cynthia Gibas- University of North Carolina at Charlotte |
| Raad Gharaibeh (University of North Carolina at Charlotte, Bioinformatics and Genomics); Anthony Fodor (University of North Carolina at Charlotte, Bioinformatics and Genomics); |
| Short Abstract: We introduce a universal simple model for estimation of target concentration from microarray signal. This model ignores all sequence-specific features of DNA probes and removes the need for detailed modeling of the sequence. It produces excellent predictions across several microarray platforms and accurately models the MAQC results. |
| Long Abstract:Click Here |
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| Poster N16 |
| Detection of oncogenic fusion transcripts by custom-made oligo microarrays |
| Gard Thomassen- Centre for Cancer Biomedicine |
| Marthe Løvf (Centre for Cancer Biomedicine, Department of Cancer Research); Guro E. Lind (Centre for Cancer Biomedicine, Department of Cancer Research); Ragnhild A. Lothe (Centre for Cancer Biomedicine, Department of Cancer Research); Rolf I. Skotheim (Centre for Cancer Biomedicine, Department of Cancer Research); |
| Short Abstract: The ability to detect neoplasia-specific fusion genes is important not only in cancer research, but also increasingly in clinical settings to ensure correct diagnosis and optimal treatment. We have developed a custom designed fusion-gene detection microarray enabling screening for all known fusion genes in a single experiment. |
| Long Abstract:Click Here |
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| Poster N17 |
| Comparative analysis of transcriptome and proteome E. coli cultivation data |
| Karoline Marisch- BOKU Vienna |
| Markus Luchner (BOKU Vienna, Biotechnology); Theresa Scharl (BOKU Vienna, Biotechnology); Karl Bayer (BOKU Vienna, Biotechnology); Gerald Striedner (BOKU Vienna, Biotechnology); |
| Short Abstract: An integrated approach of transcriptome (microarrays) and proteome (2D-DIGE) analysis was used for Escherichia coli production host comparison. Cluster analysis was applied to identify interactions and bottlenecks in the metabolism and the determination of host tuning targets for knock-out or insertion elements is intended for process optimization and strain engineering. |
| Long Abstract:Click Here |
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| Poster N18 |
| COLOMBOS: access port for bacterial cross-platform microarray compendia |
| Kristof Engelen- KULeuven |
| Qiang Fu (KULeuven, Department Of Microbial And Molecular Systems (M2S)); Pieter Meysman (KULeuven, Department Of Microbial And Molecular Systems (M2S)); Karen Lemmens (KULeuven, Department Of Microbial And Molecular Systems (M2S)); Carolina Fierro (KULeuven, Department Of Microbial And Molecular Systems (M2S)); Riet Desmet (KULeuven, Department Of Microbial And Molecular Systems (M2S)); Kathleen Marchal (KULeuven, Department Of Microbial And Molecular Systems (M2S)); Aminael Sanchez (KULeuven, Department Of Microbial And Molecular Systems (M2S)); |
| Short Abstract: COLOMBOS is a web based interface for exploring and analyzing comprehensive organism-specific cross-platform expression compendia of bacterial organisms. |
| Long Abstract:Click Here |
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| Poster N19 |
| Identification of oncogenic pathway activation indices in patients |
| Matthias Maneck- University of Regensburg |
| Stefan Bentink (Dana Farber Cancer Institute, Biostatistics); Rainer Spang (University of Regensburg, Computational Diagnostics); |
| Short Abstract: Tumor survival mechanisms interfere with cell signaling pathways, generating specific expression patterns within patients. Microarray experiments on cell lines by perturbations of oncogenic pathways generate potentially similar patterns. We developed a novel method to simultaneously analyze patients and cell line data, ensuring that generated signatures are informative both data sets. |
| Long Abstract:Click Here |
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| Poster N20 |
| A Pathway Based Correlation Method for Identifying Perturbation in Follicular Lymphoma from Microarray Gene Expression Data |
| Allison Tegge- University of Missouri |
| Gerald Arthur (University of Missouri, Pathology and Anatomical Sciences); Lynda Bennett (University of Missouri, Pathology and Anatomical Sciences); Charles Caldwell (University of Missouri, Pathology and Anatomical Sciences); Dong Xu (University of Missouri, Computer Science); Jianlin Cheng (University of Missouri, Computer Science); |
| Short Abstract: Identifying the molecular cause of a disease can help lead to more accurate disease diagnosis and better treatments. We use pair-wise pathway-level gene expression correlations between samples to identify molecular pathways that are perturbed in a disease state. This method has strong statistical power and identifies significantly perturbed pathways. |
| Long Abstract:Click Here |
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| Poster N21 |
| ArrayInitiative: a generic tool for creating custom Affymetrix CDFs |
| Christopher Overall- University of North Carolina - Charlotte |
| David Carr (Accelerated Technology Laboratories, Inc., Bioinformatics Research Division); Ehsan Tabari (University of North Carolina - Charlotte, Bioinformatics and Genomics); Jennifer Weller (University of North Carolina - Charlotte, Bioinformatics and Genomics); |
| Short Abstract: Microarray experiments frequently cannot be replicated: one cause is incorrectly mapped probes that don't represent the genes as an analyst intended. The ArrayInitiative application facilitates the creation of custom microarray definition files (e.g. Affymetrix CDFs) to represent alternative biological models, allowing for more consistent and accurate dataset processing. |
| Long Abstract:Click Here |
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| Poster N22 |
| A Transcript-level Gene Model Validation Tool |
| Timothy Tickle- University of North Carolina at Charlotte |
| Jennifer Weller (Univeristy of North Carolina at Charlotte, Department of Bioinformatics and Genomics); |
| Short Abstract: A gene model validation tool has been created to use transcript-level gene expression data from oligonucleotide exon arrays. To demonstrate its application, the intragenic junctions in several known isoforms are validated and the presence of currently unverified isoforms is predicted, in cells from tissues associated with ovarian cancer. |
| Long Abstract:Click Here |
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| Poster N23 |
| Comparative analysis of gene expression profiles in human breast cancer from microarray data using breast tissues and peripheral blood samples |
| Xing Li- Windber Research Institute |
| Prema Rapuri (WIndber Research Institute, Genomics); Jen Melley (WIndber Research Institute, Genomics); Gerianne Brilhart (WIndber Research Institute, Genomics); Vincent Wu (WIndber Research Institute, Biomedical Informatics); Leonid Kvecher (WIndber Research Institute, Biomedical Informatics); Brenda Deyarmin (WIndber Research Institute, Tissue Bank); Christina Progar (Walter Reed Army Medical Center, Clinical Breast Care Project); Jeff Hooke (Walter Reed Army Medical Center, Clinical Breast Care Project); Craig Shriver (Walter Reed Army Medical Center, Clinical Breast Care Project); Richard Mural (Windber Research Institute, ); Hai Hu (Windber Research Institute, Biomedical Informatics); |
| Short Abstract: We studied gene expression profiles of blood samples and breast tissues from invasive breast cancer and benign disease patients using microarray. The gene expression patterns from the two types of specimens are distinct. The functions of differentially expressed genes from the two groups are dramatically different. |
| Long Abstract:Click Here |
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| Poster N24 |
| ExonSVD: a new model for exon and splice junction microarrays |
| Peter Munson- NIH |
| No additional authors |
| Short Abstract: We introduce a new model for detection of alternative splicing from microarrays targeting individual exons or splice junctions. Variation of probe quality is permitted with a "responsiveness" parameter. Estimation uses a combination of linear and nonlinear techniques, and provides approximate p-values. No explicit probe filtering is required |
| Long Abstract:Click Here |
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| Poster N25 |
| Annotare – a tool for annotating microarray investigations and data |
| Ravi Shankar- Stanford University |
| Helen Parkinson (European Bioinformatics Institute, Array Express); Tony Burdett (European Bioinformatics Institute, Array Express); Emma Hastings (European Bioinformatics Institute, Array Express); Junmin Liu (University of Pennsylvania School of Medicine, Center for Bioinformatics); Stoeckert Chris (University of Pennsylvania School of Medicine, Center for Bioinformatics); Joseph White (Dana Farber Cancer Institute, Bioinformatics); Sarita Nair (Dana Farber Cancer Institute, Bioinformatics); Juli Klemm (National Cancer Intitute, caArray); Alvis Brazma (European Bioinformatics Institute, Array Express); Gavin Sherlock (Stanford University School of Medicine, Department of Genetics); Catherine Ball (Stanford University School of Medicine, Department of Biochemistry); Michael Miller (Teranode Corporation, Development); Rashmi Srinivasa (5AM Solutions, Inc., caArray); |
| Short Abstract: Encoded annotations on microarray investigations play a central role in analysis of microarray data. Developed through a collaborative effort involving different institutions, Annotare is an open-source software tool that bioinformaticians and bench biologists can use to annotate their investigations, and the relationships between biological materials and experimental data. |
| Long Abstract:Click Here |
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| Poster N26 |
| Timing the Expression of Ribosomal Proteins |
| Gang Chen- University of Texas Medical Branch |
| Andrzej Kudlicki (University of Texas Medical Branch, 1.Department of Biochemistry and Molecular Biology, and 2.Sealy Center for Molecular Medicine); |
Short Abstract: A model-based fitting method is applied to ribosomal genes in Saccharomyces cerevisiae in time-course gene
expression data to investigate whether the "just-in-time TRANSCRIPTION" paradigm applies to the regulatory processes of ribosome
synthesis. The results are verified by comparison with with published genetic experimental results. |
| Long Abstract:Click Here |
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| Poster N27 |
| Unsupervised clustering of high-dimensional datasets without prior knowledge using AutoSOME |
| Aaron Newman- University of California, Santa Barbara |
| Jim Cooper (Associate Professor, MCD Biology, UCSB); |
| Short Abstract: Without powerful computational methods, the outputs of microarray and deep-sequencing experiments are impenetrable to manual analysis. We addressed significant limitations with common clustering methods using a new strategy (called AutoSOME) that identifies both discrete and fuzzy relationships among systems-level gene expression patterns without prior knowledge of cluster number or geometry. |
| Long Abstract:Click Here |
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| Poster N28 |
| Modeling proteome dynamics using microarray data |
| Maga Rowicka- University of Texas Medical Branch at Galveston |
| Krzysztof Kuchta (University of Warsaw, Interdisciplinary Center for Mathematical and Computational Modeling); Andrzej Kudlicki (University of Texas Medical Branch at Galveston, Biochemistry and Molecular Biology); Krzysztof Ginalski (University of Warsaw, Interdisciplinary Center for Mathematical and Computational Modeling); |
| Short Abstract: Protein levels are most relevant physiologically, but measuring them genome-wide remains a challenge. We propose a computational approach and provide a web-based tool for simultaneous estimating of genome wide protein abundances based on much easier available microarray data. |
| Long Abstract:Click Here |
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| Poster N29 |
| SLAM: Gaussian Dynamic Linear Analysis of Methylated-chip Data |
| William Johnson- Brigham Young University |
| Yumei Li (University of Utah, Oncological Sciences); Brad Cairns (University of Utah, Oncological Sciences); Timothy Bahr (University of Utah, Oncological Sciences); Spencer Clark (Brigham Young University, Statistics); |
| Short Abstract: This paper presents an efficient and powerful algorithm, GausSian Dynamic Linear Anaysis of Methylated-chip data (SLAM), for profiling DNA methylation patterns from samples enriched for methylated DNA through immunoprecipitation (MeDIP) and hybridized on genome tiling microarrays (MeDIP-chip). |
| Long Abstract:Click Here |
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| Poster N30 |
| A Systems Biology Strategy to Accelerate the Development of Predictive Biomarkers for Novel Anti-cancer Agents |
| Heather Selby- University of Colorado Denver |
| Jihye Kim (University of Colorado Denver, Division of Medical Oncology); John Tentler (University of Colorado Denver, Division of Medical Oncology); Jennifer Diamond (University of Colorado Denver, Division of Medical Oncology); Todd Pitts (University of Colorado Denver, Division of Medical Oncology); Stephen Leong (University of Colorado Denver, Division of Medical Oncology); Wells Messersmith (University of Colorado Denver, Division of Medical Oncology); Gail Eckhardt (University of Colorado Denver, Division of Medical Oncology); Aik Choon Tan (University of Colorado Denver, Division of Medical Oncology); Antonio Jimeno (University of Colorado Denver, Division of Medical Oncology); |
| Short Abstract: We developed and implemented a systems biology strategy that aims to accelerate translational research from bench to bedside for novel anti-cancer agents. A bioinformatics workflow was created to extract significant genes and pathways correlated with drug sensitivity, along with integrative genomic classifiers to predict drug sensitivity. |
| Long Abstract:Click Here |
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| Poster N31 |
| Comparative analyses of time-course expression profiles of yeast ageing |
| Huanying Ge- Amgen |
| Min Wei (University of Southern California, Andrus Gerontology Center); Paola Fabrizio (University of Southern California, Andrus Gerontology Center); Jia Hu (University of Southern California, Andrus Gerontology Center); Chao Cheng (University of Southern California, Molecular and Computational Biology); Valter Longo (University of Southern California, Andrus Gerontology Center); Lei Li (University of Southern California, Molecular and Computational Biology); |
| Short Abstract: In an attempt to elucidate the underlying longevity-promoting mechanisms of yeast sch9?, which lives three times as long as wild type, we measured their time-course gene expression profiles. We interpreted their expression time differences by statistical inferences based on prior biological knowledge and compared them using volatility analysis. We identified significant expression alterations of genes involved in stress response, rRNA processing, and aerobic respiration. The results imply that the lack of SCH9 turns on the longevity programme that extends the lifespan through changes in metabolic pathways and protection mechanisms. Moreover, the expression of rRNA processing genes were more volatile over time in sch9?, and three associated cis-regulatory elements rRPE, PAC and GRE were identified using MEME. Deletion of AZF1 (TF that binds to GRE) reversed the lifespan extension of sch9?. Together, these dynamic and systemic views of the cellular biological activities will shed light on the mechanisms of ageing. |
| Long Abstract:Click Here |
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| Poster N32 |
| Cross-checking experimental results with publicly available gene expression data: a query-driven strategy |
| Riet De Smet- KULeuven |
| Riet De Smet (KULeuven, CMPG); Hong Sun (KULeuven, ESAT); Karen Lemmens (KULeuven, CMPG); Kathleen Marchal (KULeuven, CMPG); |
| Short Abstract: Interpreting a list of genes coming from an experimental assay with respect to publicly available gene expression data can reveal aspects of the genes that could not be directly derived from the assay. Here we present a computational tool that allows for cross-checking experimental results with a gene expression compendium. |
| Long Abstract:Click Here |
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| Poster N33 |
| An open-source pipeline of analyzing Illumina Methylation data |
| Pan Du- Northwestern University |
| Chiang-Ching Huang (Northwestern University, Department of Preventive Medicine); Lifang Hou (Northwestern University, Department of Preventive Medicine); Nadereh Jafari (Northwestern University, Center of Genetic Medicine); Warren Kibbe (Northwestern University, The Biomedical Informatics Center); Simon Lin (Northwestern University, The Biomedical Informatics Center); |
| Short Abstract: An open-source methylation data analysis pipeline will be presented. The pipeline includes data inputs, quality control, color-bias adjustment, normalization, methylation call modeling and estimation, and some visualization components. To validate the analysis pipeline, a methylation titration dataset has been designed. Comparisons of different analysis pipelines will also be addressed. |
| Long Abstract:Click Here |
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| Poster N34 |
| Parkinson Disease Microarray Analysis in Frontal Cortex |
| Alexandra Dumitriu- Boston University |
| Richard Myers (Boston University School of Medicine, Neurology); Paola Sebastiani (Boston University, Biostatistics); |
| Short Abstract: We performed the largest genome-wide expression study in brain tissue for Parkinson disease, using 29 neurologically healthy control and 34 PD samples from the Brodmann 9 frontal cortex area. Additional mRNA-Seq data for one control and one PD sample present in the microarray experiment were included in the analyses. |
| Long Abstract:Click Here |
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| Poster N35 |
| ROAST: rotation gene set tests for complex microarray experiments |
| Di Wu- Walter and Eliza Hall Institute of Medical Research |
| No additional authors |
| Short Abstract: Gene set tests test genes by sets prior-defined. They are valuable for increasing statistical power, organizing and interpreting results, and for relating expression patterns across different experiments. We present ROAST, a statistically rigorous gene set test which allows for gene-wise correlation while being applicable to almost any experimental design. |
| Long Abstract:Click Here |
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| Poster N36 |
| Identification of classifier genes from earthworm microarray data using an integrated statistical and machine learning approach |
| Nan Wang- The University Of Southern Mississippi |
| Ying Li (The University of Southern Mississippi, School of Computing); Joe Zhang (The University of Southern Mississippi, School of Computing); Ping Gong (US Army, ERDC); Edward Perkins (US Army, ERDC); |
| Short Abstract: Prediction of environmental risks that chemicals pose (TNT & RDX)demand rapid and accurate diagnostic assays. To discover novel biomarkers for toxicity evaluation, we identified earthworm differential expression biomarkers using statistical approach, the candidate biomarkers are weighted using ensembled tree-based algorithms and evaluated by SVM and Clustering methods. |
| Long Abstract:Click Here |
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| Poster N37 |
| Identification of common disease mechanisms and novel drug targets from multiple disease signatures using a causal network |
| Ben Sidders- Pfizer |
| Daniel Ziemek (Pfizer, Computational Sciences); Ketan Patel (Pfizer, Computational Sciences); Enoch Huang (Pfizer, Computational Sciences); Bryn Williams-Jones (Pfizer, eBiology); |
| Short Abstract: We have analysed gene expression signatures from 43 diseases using a large causal network to generate causally correct hypotheses for upstream regulators of the observed expression changes. We draw correlations between diseases based on shared hypotheses and identify novel pluripotent drug targets from these. |
| Long Abstract:Click Here |
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| Poster N38 |
| Global transcriptome analysis of the Escherichia coli O157 response to Houttuynia Cordata Thunb |
| Hyeung-Jin Jang- Kyung Hee University |
| Ki Suk Kim (Kyung Hee University, College of oriental medicine); |
| Short Abstract: This is the first study about Houttuynia Cordata Thunb(HCT)'s antibiotic mechanisms at the molecular level using microarray. The results of this study improve our understanding of the mode of action of HCT on E.coli O157:H7 and may show the usefulness of HCT fractions in the antibacterial treatment. |
| Long Abstract:Click Here |
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| Poster N39 |
| Computational workflows for miRNA-mRNA co-expression analysis |
| Ketan Patel- Pfizer Ltd |
| No additional authors |
| Short Abstract: We have developed a computational workflow to analyze miRNA/mRNA expression data from the same samples by computing weighted co-expression networks. We then apply different gene set enrichment algorithms and examine if clusters of co-expressed genes group together in a protein-protein interaction network. We apply our workflow to a real dataset. |
| Long Abstract:Click Here |
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| Poster N40 |
| The Transcriptional Network Governed by Interferon Regulatory Factor (IRF8) in Germinal Center B cells |
| Dong-Mi Shin- NIH |
| Chang-Hoon Lee (research professor, Department of molecular biology); Herbert Morse (lab chief, laboratory of immunopathology); |
| Short Abstract: Transcriptional network governed by IRF8 was studied by genome-wide ChIP-chip and expression profiling in human and mouse germinal center B cells. PU.1, a partner in DNA binding, was also studied. siIRF8 experiments allowed us to identify transcriptionally active targets. Taken together, significant pathways controlled by IRF8 have been proposed. |
| Long Abstract:Click Here |
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| Poster N41 |
| Widespread alternate splicing in the TLR signalling cascade contributes to functional diversification of the innate immune system |
| Alistair Chalk- Griffith University |
| Anthony Beckhouse (Griffith University, Eskitis Institute for Cell and Molecular Therapies); Katharine Irvine (University of Queensland, Institute for Molecular Bioscience); Jiyuan An (Griffith University, Eskitis Institute for Cell and Molecular Therapies ); Amanda Miotto (Griffith University, Eskitis Institute for Cell and Molecular Therapies ); Matthew Anderson (Griffith University, Eskitis Institute for Cell and Molecular Therapies ); Sandra Lo (Griffith University, Eskitis Institute for Cell and Molecular Therapies ); Nicholas Matigian (Griffith University, Eskitis Institute for Cell and Molecular Therapies ); Matthew Sweet (University of Queensland, Institute for Molecular Bioscience); Christine Wells (Griffith University, Eskitis Institute for Cell and Molecular Therapies); |
| Short Abstract: We applied Affymetrix exon arrays to lipopolysaccharide challenged human macrophages to detect alternative splicing in the human TLR signaling pathway, a key pathway in innate immunity. We used FIRMA and AltAnalyze to identify and annotate genes with transcript and protein domain changes. Alternative splicing is induced in this pathway. |
| Long Abstract:Click Here |
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| Poster N42 |
| Chipster: Biologist-friendly analysis software for high-throughput data |
| Aleksi Kallio- CSC - The Finnish IT Center for Science |
| Petri Klemelä (CSC - IT Center for Science, -); Taavi Hupponen (CSC - IT Center for Science, -); Massimiliano Gentile (CSC - IT Center for Science, -); Eija Korpelainen (CSC - IT Center for Science, -); |
| Short Abstract: Chipster (http://chipster.csc.fi/) offers a biologist-friendly access to up-to-date analysis methods and efficient visualizations for microarray and next generation sequencing data. Users can save and share automatic workflows, which provides an easy way for bioinformaticians to collaborate with biologists. Chipster is open source and the server installation packages are freely available. |
| Long Abstract:Click Here |
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| Poster N43 |
| Evaluating the Goodness of Pairwise Probability Models in Reconstructing Gene Regulatory Networks |
| Jarkko Salojärvi- University of Helsinki |
| Hamed Mahmoudi (Aalto University, Department of Information and Computer Science); Aymeric Fouquier d’Herouel (KTH-Royal Institute of Technology, ACCESS Linaeus Centre); Mikael Brosché (University of Helsinki, Department of Biosciences); |
Short Abstract: We evaluate how methods derived for inferring direct interactions in
spin-glass systems perform in inferring gene regulatory networks. The
simple mean-field approximation, graphical Gaussian model, has already
been applied with success, but the performance of higher order
approximations such as independent pairs, TAP, and Sessak-Monasson is
not yet known. |
| Long Abstract:Click Here |
|
| Poster N44 |
| Mining Large Gene Expression Corpora with Market-Basket Analysis to Find Condition-Dependent Correlations |
| Steve Rowley- sanofi aventis |
| Anatoly Ulyanov (sanofi aventis, Oncology Bioinformatics); |
| Short Abstract: We seek condition-dependent correlations in large gene expression corpora: overlapping subsets of genes and experiments, exhibiting high correlations. Reformulated in unsupervised learning, this is amenable to market basket analysis. We demonstrate computational tractability on a 6307-chip corpus, and summarize some QC artifacts, gene sets, and regulation rules learned. |
| Long Abstract:Click Here |
|
| Poster N45 |
| Multiplex Meta-Analysis for Microarrays |
| Alexander Morgan- Stanford University |
| Atul Butte (Stanford University, Biomedical Informatics & Pediatrics); Purvesh Khatri (Stanford University, Biomedical Informatics & Pediatrics); Keiichi Kodama (Stanford University, Biomedical Informatics & Pediatrics); Rong Chen (Stanford University, Biomedical Informatics & Pediatrics); |
| Short Abstract: Combining the results of studies using highly parallelized measurements of gene expression such as microarrays and RNAseq offer unique challenges in meta-analysis. We are investigating several different statistical methods of meta-analysis applied to gene expression studies measuring diverse biological processes including transplant rejection, aging, diabetes, and cancer. |
| Long Abstract:Click Here |
|
| Poster N46 |
| Large-scale expression profiling using a reduced representation of the transcriptome. |
| Rajiv Narayan- Broad Institute |
| Brian Geier (Broad Institute, Cancer Program); Joshua Gould (Broad Institute, Cancer Program); Aravind Subramanian (Broad Institute, Cancer Program); Todd Golub (Broad Institute, Cancer Program); |
| Short Abstract: We have developed a novel, low-cost and high-throughput expression profiling solution that is based on a reduced representation of the transcriptome. We will present the empirical foundations of our approach, performance results of gene inference models, as well as our efforts to expand the content of the existing Connectivity Map. |
| Long Abstract:Click Here |
|
| Poster N47 |
| GAGE: Generally Applicable Gene Set Enrichment |
| Weijun Luo- Cold Spring Harbor |
| Peter Woolf (University of Michigan, Bioinformatics Program, Biomedical Engineering and Chemical Engineering); Michael Zhang (Cold Spring Harbor Laboratory, Computational Biology and Bioinformatics); |
| Short Abstract: We recently published a novel gene set analysis method called GAGE. In this work, we further demonstrate the general applicability of GAGE to microarray studies with different sample sizes, experimental layouts, detection technologies, study designs, or heterogeneous experiments/datasets. Meanwhile, GAGE consistently shows robust performance and highly interpretable results. |
| Long Abstract:Click Here |
|
| Poster N48 |
| Computationally Efficient Analysis and Visualization of Exon Array Data |
| Ping Chen- University of Helsinki |
| Yizhou Hu (University of Helsinki, Molecular Cancer Biology Program); Sampsa Hautaniemi (University of Helsinki, Genome-Scale Biology Program); |
| Short Abstract: Exon microarray technology enables genome-wide quantification of transcript expression levels and facilitates revealing alternative splicing events. We introduce a computationally efficient methodology (MEAP) for exon array data preprocessing, analysis and visualization. Our results demonstrate that MEAP can rapidly preprocess hundreds of exon arrays, as well as identify differentially expressed genes. |
| Long Abstract:Click Here |
|
| Poster N49 |
| Ontology-guided Visual Exploration of the ArrayExpress Archive |
| Nils Gehlenborg- European Bioinformatics Institute |
| Nikolay Kolesnikov (United Kingdom, Functional Genomics Team); Alvis Brazma (United Kingdom, Functional Genomics Team); |
| Short Abstract: We have developed a web-based user interface built around a tree map visualization of the Experimental Factor Ontology (EFO) to provide an overview of the data sets in the ArrayExpress Archive and to enable ontology-guided browsing. |
| Long Abstract:Click Here |
|
| Poster N50 |
| Genome-scale percentage DNA methylation estimates from microarray data |
| Martin Aryee- Johns Hopkins University |
| Martin Aryee (USA, Oncology/Biostatistics); |
| Short Abstract: DNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in their infancy. Methylation sensitive restriction enzymes and microarrays provide a potential high-throughput, low-cost platform to allow methylation profiling. However, accurate absolute methylation estimates have been elusive due to systematic errors and unwanted variability. Previous microarray pre-processing procedures, mostly developed for expression arrays, fail to adequately normalize methylation-related data since they rely on key assumptions that are violated in the case of DNA methylation. We develop a normalization strategy tailored to DNA methylation data and an empirical Bayes percentage methylation estimator, that together yield accurate and precise absolute methylation estimates that can be compared across samples. |
| Long Abstract:Click Here |
|
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