| Accepted Posters |
| Category 'S'- Regulation' |
| Poster S01 |
| Conserved regulatory processes revealed by RNA half-life |
| Caroline Friedel- Ludwig-Maximilians-Universität München |
| Lars Dölken (Ludwig-Maximilians-Universität München, Max von Pettenkofer-Institut); Zsolt Ruzsics (Ludwig-Maximilians-Universität München, Max von Pettenkofer-Institut); Ulrich H. Koszinowski (Ludwig-Maximilians-Universität München, Max von Pettenkofer-Institut); Ralf Zimmer (Ludwig-Maximilians-Universität München, LFE Bioinformatik, Institut für Informatik); |
| Short Abstract: We determined precise transcript half-lives using a novel approach for measuring both newly transcribed and total RNA. Our results show that transcript half-life is conserved and specifically correlated to gene function and regulation. This allows the identification of unknown mechanisms in transcriptional regulation for protein complexes and important cellular processes. |
| Long Abstract: Click Here |
| Poster S02 |
| Comparison and improvement of models for the binding site of transcription factor p53 |
| Ji-Hyun Lim- University of St Andrews |
| Richard Iggo (University of Bordeaux, INSERM Unit U916, Institut Bergonie); Daniel Barker (University of St Andrews, School of Biology); |
| Short Abstract: A more accurate characterisation of the p53 response element may help us better understand and predict functional p53 binding sites. To improve the p53 response element model we test methods based on PWMs, hidden Markov models, information theory and more sophisticated approaches incorporating dependencies between the motif base positions. |
| Long Abstract: Click Here |
| Poster S03 |
| Exploring gene regulation through PARCDB-eQTL workflow |
| Priscila Darakjian- Oregon Health and Science University |
| Sunita Kawane (Oregon Health & Science University, BMIP); Daniel Bottomly (Oregon Health & Science University, BMIP); Nicole Walter (Oregon Health & Science University, Behavioral Neuroscience, Portland Alcohol Research Center); Robert Hitzemann (Oregon Health & Science University, Behavioral Neuroscience, Portland Alcohol Research Center); Shannon McWeeney (Oregon Health & Science University, OHSU Knight Cancer Institute); |
| Short Abstract: Expression quantitative trait loci (eQTL) analysis has aided our understanding of gene regulation and gene regulatory networks. To maximize the potential of such data, we have developed a computational workflow that allows dynamically mining eQTL data (based on Affymetrix and Illumina gene expression) for cis and trans-regulated genes. |
| Long Abstract: Click Here |
| Poster S04 |
| Systematic Analysis of Human 5’UTR Introns Reveals Their Importance in Expression |
| Can Cenik- Harvard Medical School |
| Adnan Derti (Harvard Medical School, Department of Biological Chemistry and Molecular Pharmacology ); Joe Mellor (Harvard Medical School, Department of Biological Chemistry and Molecular Pharmacology ); Gabriel Berriz (Harvard Medical School, Department of Biological Chemistry and Molecular Pharmacology ); Frederick Roth (Harvard Medical School, Department of Biological Chemistry and Molecular Pharmacology ); |
| Short Abstract: In a genome-scale analysis, we find that 5'UTR introns have a profound length-dependent effect on expression and genes with 5'UTR introns are overrepresented among regulatory genes. Our results have implications for the evolution of these much neglected class of introns |
| Long Abstract: Click Here |
| Poster S05 |
| A probabilistic model for competitive binding of transcription factors |
| Kirsti Laurila- Tampere University of Technology |
| Harri Lähdesmäki (Tampere University of Technology, Department of Signal Processing); |
| Short Abstract: Transcriptional regulation is to a large extent controlled by transcription factors binding to DNA. We have developed a probabilistic model that predicts simultaneously binding of several transcription factors. Modeling results show improvement compared to the cases where the individual prediction results of individual predictions are combined. |
| Long Abstract: Click Here |
| Poster S06 |
| Analysis of Chromatin Dynamics Using Public Large-scale Nucleosome Maps |
| Yoshiaki Tanaka- University of Tokyo |
| Itsuki Yoshimura (University of Tokyo, Medicine); Kenta Nakai (University of Tokyo, Medical Genome Sciences); |
| Short Abstract: Nucleosome organization is essential for understanding cellular processes such as transcription and replication, but it is not fully understood which nucleosomes are more dynamic or static. In this study, we merged multiple genome-scale nucleosome maps, and shows new aspects of chromatin statuses. |
| Long Abstract: Click Here |
| Poster S07 |
| Pseudogenes as a source of trans-NATs that regulate their parental genes |
| Enrique Muro- Max-Delbrück-Centrum für Molekulare Medizin |
| Enrique M. Muro (Max-Delbrück-Centrum für Molekulare Medizin , Computational Biology and Data Mining lab); Miguel A. Andrade-Navarro (Max-Delbrück-Centrum für Molekulare Medizin , Computational Biology and Data Mining lab); |
| Short Abstract: We present the hypothesis that pseudogenes, after some evolution, could be a source of natural antisense transcripts that target and regulate their respective parental genes (trans- NATs). When a pseudogene is produced (duplicated or retrotransposed), a putative trans- NAT to the parental gene is already there by default if the parental gene had already coded a cis-NAT; further evolution could erase or tune any of the two NAT copies. In order to find evidence for this hypothesis we used the public dataset of human ESTs stored at the NCBI’s GenBank database and made a genome wide screening of pseudogenes following our experimentally verified method [1]. We found 180 trans- NATs inside pseudogenes, which are transcribed and are potential candidates to regulate the respective parental genes. We aligned our candidates against their respective parental genes and compared their sequences to study the mutations that appeared after the pseudogene formation. If the trans-NAT had no functionality a flat pattern of conservation should have been obtained. Instead, we found high conservation in a region of 300 nucleotides upstream the 3’-end of the trans-NAT that points to a functional role. We illustrate our results with details about some of the cases found. The method is new and original and there are only few experimental evidences that support this work. The method and a complete list of predictions will be freely available. |
| Long Abstract: Click Here |
| Poster S08 |
| Identifying targets of transcriptionally regulated transcription factors using dynamical models |
| Antti Honkela- Helsinki University of Technology |
| Neil D. Lawrence (University of Manchester, School of Computer Science); Magnus Rattray (University of Manchester, School of Computer Science); |
| Short Abstract: We apply a Gaussian process cascaded differential equation regulationmodel to identify targets of transcriptionally regulated transcriptionfactors from wild type time series expression data. The results showsignificant improvements over ranking by differential expression inknock-outs and other alternative methods. |
| Long Abstract: Click Here |
| Poster S09 |
| Detecting Condition-Specific Transcription Regulators using Linear Modeling and Combinatorial Search |
| Konstantin Tretjakov- University Of Tartu |
| Jaak Vilo (University of Tartu, Institute of Computer Science); |
| Short Abstract: We consider the problem of computational reconstruction of transcriptional regulatory networks and formulate it as a task of detecting a small set of influential transcriptional regulators, which can appropriately represent the expression of all genes in a given microarray dataset. Experiments show that this approach can lead to fruitful results. |
| Long Abstract: Click Here |
| Poster S10 |
| Heavy metal resistance in Cupriavidus metallidurans: towards the reconstruction of regulatory networks |
| Pieter Monsieurs- SCK-CEN |
| Abderrafi Benotmane (SCK-CEN, Molecular and Cellular Biology); Sebastien Monchy (SCK-CEN, Molecular and Cellular Biology); Paul Janssen (SCK-CEN, Molecular and Cellular Biology); Rob Van Houdt (SCK-CEN, Molecular and Cellular Biology); Hugo Moors (SCK-CEN, Molecular and Cellular Biology); Natalie Leys (SCK-CEN, Molecular and Cellular Biology); Max Mergeay (SCK-CEN, Molecular and Cellular Biology); |
| Short Abstract: The soil bacterium Cupriavidus metallidurans CH34 can survive in harsh environments containing high concentrations of heavy metals. High-throughput data suggest that a complex regulatory network is underlying the heavy metal resistance. This transcriptional regulation can be deciphered using a combination of gene expression and sequence motif analysis. |
| Long Abstract: Click Here |
| Poster S11 |
| Analysing ChIPseq Datasets of Transcription Factor Binding Sites in the Cloud |
| Caroline Johnston- Kings College London |
| Diogo Castro (NIMR, Molecular Neurobiology); Daniella Dreschel (NIMR, Molecular Neurobiology); Angela Bithell (NIMR, Molecular Neurobiology); Francois Guillemot (NIMR, Molecular Neurobiology); Noel Buckley (KCL, Neuroscience); |
| Short Abstract: We used ChIPseq to investigate the binding targets of transcription factors involved in the regulation of neural cell development. Using compute resources from Amazon EC2, we have compared the performance of a number of alignment and peak-finding algorithms on our data. |
| Long Abstract: Click Here |
| Poster S12 |
| Computational simulation of transcription factor binding for the prediction of regulatory regions in DNA sequences |
| Rebecca Hunt Newbury- Center for Molecular Medicine and Therapeutics/Child and Family Research Institute, University of British Columbia |
| David Arenillas (Centre for Molecular Medicine and Therapeutics/Child and Family Research Institute, University of British Columbia, Department of Medical Genetics); Peter Sudmant (Centre for Molecular Medicine and Therapeutics/Child and Family Research Institute, University of British Columbia, Department of Medical Genetics); Dimas Yusuf (Centre for Molecular Medicine and Therapeutics/Child and Family Research Institute, University of British Columbia, Department of Medical Genetics); Diane Wu (Centre for Molecular Medicine and Therapeutics/Child and Family Research Institute, University of British Columbia, Department of Medical Genetics); Wyeth Wasserman (Centre for Molecular Medicine and Therapeutics/Child and Family Research Institute, University of British Columbia, Department of Medical Genetics); |
| Short Abstract: Transcription factors (TFs) are key regulatory proteins in gene expression, controlling cell differentiation and transient environmental responses. We introduce a transcriptional regulation simulation that incorporates TF searching of DNA for target sites and interactions between TFs, ultimately reporting the occupancy of a TF within a region of DNA. |
| Long Abstract: Click Here |
| Poster S13 |
| Exploring the Genetics of Gene Expression with Random Forests |
| Jacob Michaelson- TU Dresden |
| Rudi Alberts (Helmholtz Centre for Infection Research, ); Klaus Schughart (Helmholtz Centre for Infection Research, ); Andreas Beyer (Biotechnology Center, TU Dresden, Cellular Networks and Systems Biology); |
| Short Abstract: The use of Random Forests as an eQTL mapping method is discussed, and its ability to recover known pathway information is compared with widely-used eQTL mapping methods. We also present a novel means for detecting multi-locus interactions using Random Forests. |
| Long Abstract: Click Here |
| Poster S14 |
| A Transcription Factor Affinity Based Code for Mammalian Transcription Initiation |
| Molly Megraw- Duke University |
| Fernando Pereira (University of Pennsylvania, Computer Science); Shane Jensen (University of Pennsylvania, Statistics); Artemis Hatzigeorgiou (Alexander Fleming Research Institute, Molecular Oncology); Uwe Ohler (Duke University, Institute for Genome Sciences and Policy); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster S15 |
| Global mapping of protein-DNA interactions in vivo by digital genomic footprinting |
| Xiaoyu Chen- University Of Washington |
| Jay Hesselberth (University of Washington, Genome Sciences); Zhihong Zhang (University of Washington, Genome Sciences); Peter Sabo (University of Washington, Genome Sciences); Richard Sandstrom (University of Washington, Genome Sciences); Alex Reynolds (University of Washington, Genome Sciences); Robert Thurman (University of Washington, Genome Sciences); Shane Neph (University of Washington, Genome Sciences); Michael Kuehn (University of Washington, Genome Sciences); William Noble (University of Washington, Genome Sciences); Stanley Fields (University of Washington, Genome Sciences); John Stamatoyannopoulos (University of Washington, Genome Sciences); |
| Short Abstract: We developed a digital approach to assay regulatory proteinoccupancy on DNA in vivo by dense mapping ofDNaseI cleavages from intact nuclei using massivelyparallel DNA sequencing. Analysis of DNaseI cleavagesacross the yeast genome revealed thousandsof regulatory protein footprints, enabling de novoderivation of factor binding motifs and the identification ofnew binidng sites for major regulators. |
| Long Abstract: Click Here |
| Poster S16 |
| Identifying switches of combinatorial regulation between different cellular conditions |
| Florian Markowetz- Cancer Research UK Cambridge Research Institute |
| Ewa Szczurek (Max-Planck Institute for Molecular Genetics, Computational Molecular Biology); Irit Gat-Viks (Broad Institute of MIT and Harvard, Computational Biology); Martin Vingron (Max-Planck Institute for Molecular Genetics, Computational Molecular Biology); |
| Short Abstract: Understanding molecular changes between different cellular conditions is an important step in elucidating disease mechanisms, including cancer. We propose a pathway-centric approach to identify how transcriptional regulation downstream of a signalling pathway changes between conditions by combining prior knowledge on the pathway structure with transcriptional phenotypes of gene perturbation experiments. |
| Long Abstract: Click Here |
| Poster S18 |
| Promatch: A Computational Method for Identifying Conserved Transcription Factor Binding Sites |
| Quinn Snell- Brigham Young University |
| W. Evan Johnson (Brigham Young University, Statistics); Mark Clement (Brigham Young University, Computer Science); Colin Rogerson (Brigham Young University, Statistics); Kendell Clement (Brigham Young University, Computer Science); Susan Mango (University of Utah, Oncological Science); |
| Short Abstract: Functional transcription factor binding sites are often conserved multiple species. We have developed a method to incorporate the conservation scores into motif searching. We have shown that the method is highly effective in finding important binding sites. We have implemented our method into a user-friendly web server. |
| Long Abstract: Click Here |
| Poster S19 |
| In silico identification of a core regulatory network of OCT4 in human embryonic stem cells using an integrated approach |
| Lukas Chavez- Max-Planck-Institute for Molecular Genetics |
| Abha Bais (Max-Planck-Institute for Molecular Genetics, Department of Vertebrate Genomics); James Adjaye (Max-Planck-Institute for Molecular Genetics, Department of Vertebrate Genomics); Ralf Herwig (Max-Planck-Institute for Molecular Genetics, Department of Vertebrate Genomics); |
| Short Abstract: We have carried out an integrated analysis of high-throughput data (ChIP-on-chip and RNAi experiments along with promoter sequence analysis of putative target genes) and identified a core OCT4 regulatory network in human embryonic stem cells consisting of 33 target genes. |
| Long Abstract: Click Here |
| Poster S20 |
| In silico discovery of long-range cis-regulatory modules in humans |
| Geoff Macintyre- University of Melbourne |
| James Bailey (University of Melbourne, Computer Science and Software Engineering); Adam Kowalczyk (NICTA, Cancer Genomics); Izhak Haviv (Baker IDI Heart and Diabetes Institute, The Blood and DNA profiling Facility); |
| Short Abstract: We provide an in silico approach that accurately predicts interacting transcription factors (TFs) and their cis-regulatory module (CRM) locations in humans. Our method is successful in identifying long-range CRMs (searches 800kb encompassing target genes). Model output provides candidate interacting TFs for further experimental analysis of gene regulatory programs. |
| Long Abstract: Click Here |
| Poster S21 |
| Analysis of alternative splicing by next generation sequencing |
| Richard Hugues- Max Planck Institute for Molecular Genetic |
| Marcel H. Schulz (Max Planck Institute for Molecular Genetic, Computational Molecular Biology); Marc Sultan (Max Planck Institute for Molecular Genetic, Vertebrate Genomic); Asja Nürnberger (Max Planck Institute for Molecular Genetic, Vertebrate Genomic); Sabine Schrinner (Max Planck Institute for Molecular Genetic, Vertebrate Genomic); Daniela Balzereit (Max Planck Institute for Molecular Genetic, Vertebrate Genomic); Emilie Dagand (Max Planck Institute for Molecular Genetic, Vertebrate Genomic); Hans Lehrach (Max Planck Institute for Molecular Genetic, Vertebrate Genomic); Stefan haas (Max Planck Institute for Molecular Genetic, Computational Molecular Biology); Martin Vingron (Max Planck Institute for Molecular Genetic, Computational Molecular Biology); Marie-Laure Yaspo (Max Planck Institute for Molecular Genetic, Computational Molecular Biology); |
| Short Abstract: Second generation sequencing is now opening unprecedented new routes to address the analysis of entire transcriptomes. Here, we developped methods allowing the prediction and quantification of alternative splicing events from RNA-Seq data and validated a significant number of prediction by RT-PCR. |
| Long Abstract: Click Here |
| Poster S22 |
| How to predict the role of metabolites in sensing and signaling? |
| Sebastian Klie- Max-Planck Institute of Molecular Plant Physiology |
| Zoran Nikoloski (University of Potsdam, Institute of Biology and Biochemistry); Joachim Selbig (University of Potsdam, Institute of Biochemistry and Biology); |
| Short Abstract: Recent advances of high-throughput technologies, couple with existing structured biological knowledge, render it possible to predict the role of metabolites in sensing and signaling. Here, we propose a data-driven network-based approach for predicting putative candidates for sensing and signaling molecules based on data sets from E. coli. |
| Long Abstract: Click Here |
| Poster S23 |
| A fast and ?exible tool for 3C primer set generation |
| Sebastian Fröhler- Max-Delbrueck-Center for Molecular Medicine |
| Christoph Dieterich (Max-Delbrueck-Center for Molecular Medicine, Berlin Institute for Medical Systems Biology); |
| Short Abstract: We present the very first fully automated 3Cprimer design suite. Our software is highly flexiblemaking it useful for various variants of 3C.By using in-silico pcr, we efficiently screen a vastamount of candidate primer pair combinations for theoptimal set of primer pairs.We will present our algorithms and evaluate oursoftware in a wet-lab 3C experiment. |
| Long Abstract: Click Here |
| Poster S24 |
| Unravelling patterns of transcription factor binding sites |
| Anna-Lena Kranz- IPMB/University of Heidelberg |
| Roland Eils (DKFZ/University of Heidelberg/IPMB/Bioquant, Theoretical Bioinformatics/Bioinformatics and Functional Genomics); Rainer Koenig (University of Heidelberg/IPMB/DKFZ, Bioinformatics and Functional Genomics/Theoretical Bioinformatics); |
| Short Abstract: To gain new insights into transcriptional regulation we conducted an in silico promoter scan on the human genome. The distribution of binding sites of transcription factors showed distinct binding patterns which, in combination with networks of transcription factor interactions, may indicate new binding hypotheses of transcription factors to the DNA. |
| Long Abstract: Click Here |
| Poster S25 |
| m:Profiler - A Web Tool for Regulatory Motif Analyses |
| Meelis Kull- University of Tartu |
| Mirko Adari (University of Tartu, Department of Computer Science); Aivo Paas (University of Tartu, Department of Computer Science); Indrek Tamming (University of Tartu, Department of Computer Science); Jaak Vilo (University of Tartu, Department of Computer Science); |
| Short Abstract: m:Profiler is a web tool to perform computational analyses related to regulatory motifs and visualize the results.It has a set of built-in types of analyses, e.g. the enrichment analysis of all Transfac motifs in a given list of promoters.Analyses and visualizations can include data about TSSs, CRMs, chromatin, conservation etc. |
| Long Abstract: Click Here |
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