Accepted Posters

Category 'S'- Regulation'
Poster S01
A Concordance Approach for ChIP-Seq Binding Site Detection Facilitates Localization of Beta-catenin in Colorectal Cancer Cells.
Daniel Bottomly- OHSU
Sydney Kyler (The Pennsylvania State University, The Department of Biochemistry & Molecular Biology); Gregory Yochum (The Pennsylvania State University, The Department of Biochemistry & Molecular Biology/Program in Cellular and Molecular Biology/The Pennsylvania State Hershey Cancer Institute); Shannon McWeeney (OHSU, OCTRI/Knight Cancer Institute/DMICE);
Short Abstract: A concordance computational approach was utilized to identify beta-catenin binding regions using ChIP-Seq data from HCT116 cells. A de novo motif search showed that both TCF4 and AP-1 motifs were overrepresented in binding regions. Further, these regions bound beta-catenin, TCF4 and c-Jun in vivo with nearby genes being transcribed.
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Poster S02
The computational discovery and evolutionary implications of regulatory motif patterns responsible for transcriptional activation of ribosome biogenesis
Robert Gross- Dartmouth College
Viktor Martyanov (Dartmouth College, Biology);
Short Abstract: The transcriptional regulation of ribosome biogenesis genes from 26 fungal species was examined by using the motif finder SCOPE. Motifs were organized in patterns involving two motif occurrences with conserved intermotif distances (modules). Two different fungal evolutionary branches had modules that displayed distinct patterns of motifs and intermotif distances.
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Poster S03
Evaluating methods for Expression Quantitative Trait Loci (eQTL) detection in complex mouse crosses
Priscila Darakjian- Oregon Health and Science University
Sunita Kawane (Oregon Health & Science University, Oregon Clinical and Translational Research Institute (OCTRI)); Ovidiu Iancu (Oregon Health & Science University, Behavioral Neuroscience); Robert Hitzemann (Oregon Health & Science University, Behavioral Neuroscience; Portland Alcohol Research Center (PARC)); Shannon McWeeney (Oregon Health & Science University, Knight Cancer Institute; Oregon Clinical and Translational Research Institute (OCTRI); Portland Alcohol Research Center (PARC));
Short Abstract: We analyze eQTL concordance levels of two QTL mapping methods on three mouse crosses: eight-strain collaborative cross, four-strain cross and F2 cross. Results show that increased genetic complexity decreases the level of concordance, likely a reflection of the differing assumptions each method makes about population and haplotype structure.
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Poster S04
Improved prediction of in vivo transcription factor binding sites by using DNA structure
Bart Hooghe- VIB & Ghent University
Stefan Broos (VIB & Ghent University, Department of Molecular and Biomedical Research); Frans van Roy (VIB & Ghent University, Department of Molecular and Biomedical Research); Pieter De Bleser (VIB & Ghent University, Department of Molecular and Biomedical Research);
Short Abstract: We describe an approach that identifies in vivo TFBSs more accurately than the currently used and newly introduced in silico methods. This greater accuracy was achieved by focusing on the subtle differences between in vitro and in vivo TFBSs, and by using DNA sequence-dependent shape characteristics.
Long Abstract:Click Here

Poster S05
Gene Regulatory Network Reconstruction based on Gene Expression and Transcription Factor Activities
Yao Fu- Iowa State University
Yao Fu (Iowa State University, Bioinformatics and Computational Biology); Julie Dickerson (Iowa State University, Bioinformatics and Computational Biology);
Short Abstract: During my work, a gene regulatory
reconstruction algorithm, context likelihood relatedness with gene
expression and transcription factor activities (CLR-GT), is developed to
integrate TFA information and gene expression profiles into a gene
regulatory network reconstruction model.
Long Abstract:Click Here

Poster S06
ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules.
Kathleen Marchal- K.U.Leuven
Hong Sun (PhD student, Microbial and Molecular Systems); Valerie Storms (PhD student, Microbial and Molecular Systems); Qiang Fu (PhD student, Microbial and Molecular Systems); Karen Lemmens (PhD, Microbial and Molecular Systems); Tijl Debie (PhD, Department of Engineering Mathematics);
Short Abstract: As a result their usage is limited to finding cis-acting regulatory modules (CRMs) in small datasets. We exploited the computational efficiency of an itemset mining approach and combined it with a well-designed statistical scoring scheme. This allowed us to prioritize biologically valid CRMs in a large set of coregulated genes.
Long Abstract:Click Here

Poster S07
Roles of Signal Transducer and Activator of Transcription in Tuning Epigenetic Modification and Transcription during Helper T Cell Differentiation
Golnaz Vahedi- National Institutes of Health
Lai Wei (NIH, NIAMS); Hongwei Sun (NIH, NIAMS); Hayato Takahashi (NIAMS, NIAMS); Gustavo Gutierrez-Cruz (NIH, NIAMS); John J. O’Shea (NIH, NIAMS); Yuka Kanno (NIH, NIAMS);
Short Abstract: Signal transducers and activators of transcription (STATs) are key factors in the speci?cation of T helper cells. Using chromatin immunoprecipitation and massive parallel sequencing, we showed how STATs play critical roles in maintaining chromatin con?guration and the degree of transcription for a subset of genes through various epigenetic patterns.
Long Abstract:Click Here

Poster S08
Epigenetic Relationship between Histone Behaviors and Transcription Initiation
Tadasu Nozaki- Keio University
Ryu Ogawa (Keio University, Graduate School of Media and Governance); Nozomu Yachie (Keio University, Graduate School of Media and Governance); Anton Kratz (Keio University, Graduate School of Media and Governance); Rintaro Saito (Keio University, Faculty of Environment and Information Studies); Masaru Tomita (Keio University, Faculty of Environment and Information Studies);
Short Abstract: We conducted an integrative analysis of multiple human genome-scale datasets, and investigated global relationships between promoter architecture and epigenetic factors including positioning of nucleosomes, their stability and modifications. Our results illuminate how gene regulatory networks are controlled by genome-wide epigenetic events which depend on promoter architecture.
Long Abstract:Click Here

Poster S09
Bioinformatic tools complement molecular biology techniques in rapid discovery of spore-specific transcription factor binding sites in the phytopathogen Phytophthora infestans.
Sourav Roy- University of California, Riverside
Short Abstract: Traditional molecular techniques for identifying transcription factor binding sites (TFBSs) are time consuming. Integrating bioinformatics and molecular techniques can accelerate the process of accurate identification of TFBS in promoters of genes expressed in spore-specific stages of an eukaryotic microbe - Phytophthora infestans. This method can be applied to any organism.
Long Abstract:Click Here

Poster S10
Decoding the cis-regulatory logic of stress-regulated genes in Arabidopsis thaliana
Shin-Han Shiu- Michigan State University
Cheng Zou (Michigan State University, Plant Biology); Josh Mackaluso (Michigan State University, Plant Biology); Kelian Sun (Michigan State University, Plant Biology); Rong Jin (Michigan State University, Comp Sci & Eng); Michael Thomashow (Michigan State University, Plant Res Lab);
Short Abstract: The programmed gene expression under stress requires exquisite regulatory mechanisms. We have identified >1,000 putative CREs (pCREs) and >300 combinatorial rules governing stress-regulated gene expression in the model plant A. thaliana using machine learning methods. Our findings provide important insight into the cis-regulatory logic of plant stress responses.
Long Abstract:Click Here

Poster S11
Systematic Discovery of cis-regulatory Elements in the Mouse Genome
Feng Yue- UCSD
Yin Shen (UCSD, Ludwig Institute for Cancer Research); Lee Edsall (UCSD, Ludwig Institute for Cancer Research); David McCleary (UCSD, Ludwig Institute for Cancer Research); Bing Ren (UCSD, Ludwig Institute for Cancer Research);
Short Abstract: Temporal and tissue-specific gene expression in mammals depends on the cis-regulatory elements in the genome, including promoters, enhancers, and insulators. In this study, we identified cis-regulatory elements in mouse bone marrow, cerebellum, cortex, heart, kidney, liver, lung and embryonic stem cells.
Long Abstract:Click Here

Poster S12
Genome-wide computational prediction of core promoter elements in Arabidopsis.
Sunita Kumari- Cold Spring Harbor Lab
No additional authors
Short Abstract: Our computational pipeline systematically predicted genome-wide core promoter motifs in Arabidopsis. We predicted 20% TATA-box, 14.5% CCAAT, 12% INR, 14.4% BRE, 8.1%GC-box, and 64.5% YPatch motif containing genes in Arabidopsis. Further, TATA and TATA-less genes' analysis showed TATA genes are involved in stress conditions; TATA-less genes in housekeeping activities.
Long Abstract:Click Here

Poster S13
CBS: a database of Conserved regulatory Binding Sites in the genome of multiple Drosophilas
Enrique Blanco- Universitat de Barcelona (UB)
Montserrat Corominas (Universitat de Barcelona (UB), Genetica);
Short Abstract: We present the Conserved regulatory Binding Sites (CBS) resource that contains a comprehensive collection of regulatory predictions conserved in the genome of 12 Drosophila species. The CBS web interface allows the researcher to easily query the catalog of predictions. Our database is furnished with additional tools for data-mining the annotations.
Long Abstract:Click Here

Poster S14
RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins
Hilal Kazan- University of Toronto
Debashish Ray (University of Toronto, Banting and Best Department of Medical Research); Esther Chan (University of Toronto, Department of Molecular Genetics); Timothy Hughes (University of Toronto, Banting and Best Department of Medical Research, Department of Molecular Genetics, Donnelley Centre for Cellular and Biomolecular Research); Quaid Morris (University of Toronto, Department of Computer Science,Banting and Best Department of Medical Research, Department of Molecular Genetics, Donnelley Centre for Cellular and Biomolecular Research);
Short Abstract: Metazoan genomes encode hundreds of RNA-binding proteins (RBPs). Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences and we with greater accuracy than existing approaches.
Long Abstract:Click Here

Poster S15
Improved Prediction of Transcription Binding Sites from Chromatin Modification Dat
Paul Horton- AIST, Computational Biology Research Center
Kengo Sato (University of Tokyo, Graduate School of Frontier Sciences); Thomas Whitington (University of Queensland, the Institute for Molecular Bioscience); Timothy Bailey (University of Queensland, the Institute for Molecular Bioscience);
Short Abstract: We apply random forests to predicting human transcription factor binding sites by combining multiple forms of chromatin modification with the DNA binding strength predicted by a PWM. Our results demonstrate that TFBS prediction accuracy can be significantly improved by using machine learning classifiers with histone modification and other auxillary features
Long Abstract:Click Here

Poster S16
Predict gene expression with genetic and epigenetic features
Xiaowo Wang- MOE Key Laboratory of Bioinformatics and Bioinformatics Div, TNLIST / Department of Automation, Tsinghua University
No additional authors
Short Abstract: We integrated specific genome-wide histone modification profiles and transcription factor binding data together to predict the absolute value of gene expression. We found that TF and Histone Modificaton data share some common but also complementary information, integrating them together can significantly improve the prediction accuracy.
Long Abstract:Click Here

Poster S17
Ching-Wai Tan- Postdoctoral Fellow / UCSD
Julia Ponomarenko (Senior Scientist, San Diego Supercomputer Center);
Short Abstract: We present an automatic classification of transcription factors based on properties of their DNA-binding domains and interfaces learned from 3D structures of homologous proteins and protein-DNA complexes. Comparison with SCOP revealed the families that were clustered together and those that can be broken into subfamilies as their interfaces differ.
Long Abstract:Click Here

Poster S18
Toward a Unified Framework for Motif Discovery Methods
Lonnie Welch- Ohio University
Sophie Schbath (French National Institute for Agricultural Research, Mathematique, Informatique et Genome); Finn Drablos (Norwegian University of Science and Technology, Department of Cancer Research and Molecular Medicine);
Short Abstract: OpenMotif, a unified framework and code repository for motif discovery, provides statistical models, word enumeration algorithms, and module discovery methods. The framework is available (under GNU General Public License) at OpenMotif has been applied to several test cases, including genomic data of E. coli, A. thaliana, and H. sapiens.
Long Abstract:Click Here

Poster S19
Mapping of cell-type specific transcription factor binding from chromatin accessibility assays and genome annotation
Roger Pique-Regi- University of Chicago
Jacob Degner (University of Chicago, Human Genetics); Athma Pai (University of Chicago, Human Genetics); Daniel Gaffney (University of Chicago, Human Genetics); Yoav Gilad (University of Chicago, Human Genetics); Jonathan Pritchard (University of Chicago, Human Genetics);
Short Abstract: We developed a probabilistic framework that integrates experimental data such as histone modifications, DNase-seq, or FAIRE with genomic annotation to predict which motif locations are actively bound by a transcription factor (TF). Compared to ChIP-seq this method is highly accurate with the advantage of targeting all TFs in one experiment.
Long Abstract:Click Here

Poster S20
Towards Decoding The Regulatory Code of The Human Forebrain
Hani Girgis- National Institutes of Health
Axel Visel (Lawrence Berkeley National Laboratory, Genomics Division); John Rubenstein (University of California at San Francisco, Department of Psychiatry); Ivan Ovcharenko (National Institutes of Health, National Center for Biotechnology Information);
Short Abstract: Tissue specificity of gene expression is controlled by non-coding sequences known as cis-regulatory modules (CRMs). We designed five classifiers to recognize CRMs specific to the human Telencephalon and its four domains. The five classifiers achieved high sensitivity and specificity. These results argue for the presence of the cis-regulatory codes specific to different forebrain domains.
Long Abstract:Click Here

Poster S21
Identification of Novel Transcriptional Interactions for Mesoderm Inducer in Xenopus Like1(MIXL1)
Aaron Raymond- MD Anderson Cancer Center
Lalitha Nagarajan (MD Anderson Cancer Center, Genetics);
Short Abstract: MIXL1, a paired-type homeobox transcription factor vital for embryonic and adult hematopoiesis, has been implicated in hematopoietic tumorgenesis. However, the mechanisms of MIXL1-mediated proliferation are unknown. Using microarray analysis and ChIP-Seq analysis, we have identified cRel as a mediator in MIXL1-induced proliferation, and CTCF as an interacting protein.
Long Abstract:Click Here

Poster S22
PolyA site strength is important for gene expression and tissue-specific alternative polyadenylation
Wencheng Li- University of Medicine and Dentistry of New Jersey
Bin Tian (University of Medicine and Dentistry of New Jersey, New Jersey Medical School);
Short Abstract: Using RNA-seq data from human tissues, we found that that strong polyA sites are associated with genes expressed at high levels whereas weak polyA sites are commonly used to regulate 3'UTRs.
Long Abstract:Click Here

Poster S23
Down-regulation by microRNAs depends on target mRNA abundance
Aaron Arvey- MSKCC
Aaron Arvey (MSKCC, Computational Biology); Christina Leslie (MSKCC, Computational Biology); Debora Marks (Harvard Medical School, Systems Biology);
Short Abstract: Post-transcriptional regulation by microRNAs and siRNAs will depend on systems-level properties, as well as characteristics of individual binding sites in target mRNA molecules. Simple chemical kinetics predicts that the level of microRNA regulation will depend upon concentration of mRNA transcripts with target sites in the cell; that is, target abundance acts as a rate-limiting step in degrading target transcripts. To test this we analyze 143 microRNA and siRNA transfection experiments and show that down-regulation by miRNAs and siRNAs depends on total target mRNA abundance. Comparing pairs of miRNAs with high and low target abundance, we show that similar sites can result in very different amount of regulation as a result of differential target abundance. Our conclusion is that more global properties, such as mRNA target abundance, need to be considered in addition to local determinants. Furthermore, the paradigm of microRNA and siRNA targeting should shift away from the simple discretization of "target" or "not a target" and towards a more quantitative framework. This has consequences for microRNA target prediction, siRNA design and small RNA therapeutics.
Long Abstract:Click Here

Poster S24
Positional Variations among Heterogeneous Nucleosome Maps Give Dynamic Information on Chromatin
Yoshiaki Tanaka- University of Tokyo
Yoshiaki Tanaka (University of Tokyo, Medical Genome Sciences); Itsuki Yoshimura (University of Tokyo, Medicine); Kenta Nakai (University of Tokyo, Medical Genome Sciences);
Short Abstract: The local positional variance of nucleosomes in a set of heterogeneous maps can be an indicator of local nucleosome dynamics.
Long Abstract:Click Here

Poster S25
A systems approach shows competition and saturation drives microRNA and siRNA target gene regulation – its more than target site efficacy
Debora Marks- harvard medical school
aaron arvey (MSKCC, cBio); Aly Khan (MSKCC, cBio); Erik Larsson (MSKCC, cBio); Christina Leslie (MSKCC, cBio); Martin Miller (MSKCC, cBio);
Short Abstract: Saturation of protein machinery in RNAi pathways may affect both siRNA and microRNA targeting. By analyzing hundreds of experiment, we show that genes which are targets of endogenous micoRNAs are unexpectedly and unintentionally upregulated after microRNA/siRNA perturbations. We go on to discover over 20 novel significant motifs in 3’UTRs which work cooperatively with microRNAs to alter gene expression. Competition between different mRNAs for the microRNAs or siRNAs should, in theory affect the targeting quantitatively, according to a basic kinetic model. Taking hundreds of experiments we show that target abundance is a strong determinant in microRNA regulation.Our results show that mRNA target abundance and the competition for miRNAs and siRNAs has global consequences. This provides strong support for a re-assessment of what determines micro/siRNA targeting. Specifically, our results will shed insight into several critical problems to the community, including microRNA target prediction, siRNA screen design and small RNA therapeutics.
Long Abstract:Click Here

Accepted Posters

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