Accepted Posters
|
Category 'G'- Functional Genomics' |
| Poster G01 |
| Detecting ssRNA loci with NiBLS |
| Daniel MacLean- The Sainsbury Laboratory |
| No additional authors |
| Short Abstract: We describe an algorithm that determines sRNA loci from HTGS data. The algorithm creates a graph, of the sRNAs based on proximity on the target genome. For each resulting graph the clustering coefficient is used to identify loci. |
| Long Abstract:Click Here |
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| Poster G02 |
| Gene Function Prediction via Morphological Phenotype Discovery in High-Content Screening |
| Chen Lin- Brandeis University |
| Pengyu Hong (Brandeis University, Computer Science); Chris Bakal (The Institute of Cancer Research, Dynamical Cell Systems Team); Norbert Perrimon (Harvard Medical School, Department of Genetics); |
| Short Abstract: We have developed a new methodology for discovering novel cellular morphological phenotypes that can then be used for predicting gene functions. Our method utilizes under-used features (UUFs), which are defined as the features not significant to existing phenotypes. We applied our approach to a genetic HCS of Drosophila BG-2 cells. |
| Long Abstract:Click Here |
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| Poster G03 |
| Selection and Evaluation of Gene-specific Biomarkers in Microarray Experiments |
| Dan Lin- Hasselt University & Katholic Universiteit Leuven |
| Ziv Shkedy (Hasselt University, I-Biostat); Willem Talloen (Janssen Pharmaceutica NV, Biostatistics & Reporting); Luc Bijnens (Janssen Pharmaceutica NV, Biostatistics & Reporting); Geert Molenberghs (Hasselt University & K.U. Leuven, I-Biostat); Hinrich H.W. Goehlmann (Janssen Pharmaceutica NV, Functional Genomics); |
| Short Abstract: We place our attention on the selection and evaluation of biomarkers from microarray experiments. Two sets of biomarkers using gene expressions are considered as of importance, namely, therapeutic and prognostic. For the latter, two types of associations, linear or non-linear between gene expressions and the response of interest are discussed. |
| Long Abstract:Click Here |
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| Poster G04 |
| High Throughput Mutant Screening using Probabilistic Normalization and Identification |
| Shannon Bell- Michigan State University |
| Rob Last (Michigan State University, Biochemistry and Molecular Biology); Shin-Han Shiu (Michigan State University, Plant Biology); |
| Short Abstract: The potential for high throughput phenotypic screening is limited due to lack of methods for dealing with high noise and low replication. We present MIPHENO (Mutant Identification by Probabilistic High throughput-Enabled Normalization), a method for controlling group wise error and enabling cross sample comparisons for processing high throughput phenotyping data. |
| Long Abstract:Click Here |
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| Poster G05 |
| FABIA: Factor Analysis for Bicluster Acquisition |
| Martin Heusel- Johannes Kepler Universität Linz |
| Sepp Hochreiter (Johannes Kepler Universität Linz, Institute of Bioinformatics); Ulrich Bodenhofer (Johannes Kepler Universität Linz, Institute of Bioinformatics); Andreas Mayr (Johannes Kepler Universität Linz, Institute of Bioinformatics); Andreas Mitterecker (Johannes Kepler Universität Linz, Institute of Bioinformatics); Adetayo Kasim (Hasselt University, Institute for Biostatistics and Statistical Bioinformatics); Tatsiana Khamiakova (Hasselt University, Institute for Biostatistics and Statistical Bioinformatics); Suzy Van Sanden (Hasselt University, Institute for Biostatistics and Statistical Bioinformatics); Dan Lin (Hasselt University, Institute for Biostatistics and Statistical Bioinformatics); Ziv Shkedy (Hasselt University, Institute for Biostatistics and Statistical Bioinformatics); Willem Talloen (Johnson & Johnson Pharmaceutical Research & Development, a Division of Janssen Pharmaceutica, Functional Genomics Department); Luc Bijnens (Johnson & Johnson Pharmaceutical Research & Development, a Division of Janssen Pharmaceutica, Functional Genomics Department); Hinrich W. H. G. (Johnson & Johnson Pharmaceutical Research & Development, a Division of Janssen Pharmaceutica, Functional Genomics Department); Djork Clevert (Johannes Kepler Universität Linz, Institute of Bioinformatics); |
Short Abstract: Biclustering genes and samples is important to extract knowledge from
gene expression measurements. Current methods are not generative
models allowing model selection and Bayesian framework or are additive
models not explaining mRNA effects and PCR amplification. We
introduce a generative, multiplicative model which assumes realistic
non-Gaussian heavy tail distributions. |
| Long Abstract:Click Here |
|
| Poster G06 |
| On Protein Biology Discoveries Using Domain-Based Methods |
| Gloria Rendon- National Center for Supercomputing Applications |
| No additional authors |
| Short Abstract: Recombination of functional domains is a major mechanism of protein evolution. Here, we report on a variety of biological discovery scenarios that include evolutionary analysis of ion channel families and functional analysis of the human biome with focus on bacterial pathogens and human-pathway mimics, carried out by our group UIUC-Renci. |
| Long Abstract:Click Here |
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| Poster G07 |
| A knowledge-based approach to target protein-protein interfaces for drug discovery |
| Ratna Thangudu- National Center for Biotechnology Information |
| Thomas Madej (National Center for Biotechnology Information , Computational Biology Branch); Anna Panchenko (National Center for Biotechnology Information , Computational Biology Branch); Stephen Bryant (National Center for Biotechnology Information , Computational Biology Branch); |
| Short Abstract: Protein-protein interactions are emerging as important targets for design of novel therapeutics. In this study, we devise a strategy to comprehensively explore the knowledge of homology on a large scale in discovering putative protein-protein interactions overlapping with small molecule binding sites that could be attractive drug targets. |
| Long Abstract:Click Here |
|
| Poster G08 |
| Integrated Interpretation of Genes |
| Gang Feng- Northwestern University |
| Pan Du (Northwestern University, NU Clinical and Translational Sciences Institute); Warren Kibbe (Northwestern University, NU Clinical and Translational Sciences Institute); Simon Lin (Northwestern University, NU Clinical and Translational Sciences Institute); |
| Short Abstract: For identifying potential functions and pathways for given genes, we developed a new Bioconductor package, GeneAnswers. By means of tests of statistical significance, GeneAnswers supports Gene Ontology, Pathway Ontology (KEGG) and Disease Ontology. Furthermore, it also provides key words analysis based on Entrez eUtils |
| Long Abstract:Click Here |
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| Poster G09 |
| CRSH: Classes of Reiprocal Sequence Homologs |
| Samuel Handelman- Ohio State University |
| Nelson Tong (Rutgers University, Molecular Biology and Biochemistry); Jon Luff (Columbia University, Biological Sciences); David Lee (Columbia University, Biological Sciences); Andre Lazar (Columbia University, Biological Sciences); Tyrrell Conway (University of Oklahoma, Advanced Center for Genome Technology); John Hunt (Columbia University, Biological Sciences); |
| Short Abstract: The CRSH are produced using orthology-based methods to group bacterial proteins of likely-similar biochemical function. The functional similarity of the proteins in each CRSH is mainly supported by gene neighborhood in diverse microbial organisms, and the strength of correlation between gene neighborhood and transcriptional co-regulation in E-coli. |
| Long Abstract:Click Here |
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| Poster G10 |
| Predicting functional gene regulatory circuits using probability landscapes calculated from hundreds of transcriptome profiles |
| Nicolas TCHITCHEK- Institut des Hautes Etudes Scientifiques |
| Annick LESNE (Institut des Hautes Etudes Scientifiques, ); Arndt BENECKE (Institut des Hautes Etudes Scientifiques, ); |
| Short Abstract: We analyze gene regulation using functional genomics data modelled in a probabilistic fashion. For each base of the genome we associate a measure representing the probability distribution for a biological feature to be expressed. |
| Long Abstract:Click Here |
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| Poster G11 |
| Gene Set Interpreter: An open framework for analysis and management of gene sets and profiles |
| Dmitri Bichko- Pfizer |
| Ranjit Randhawa (Pfizer, Computational Sciences CoE); Savina Jaeger (Pfizer, Computational Sciences CoE); Vicky Wang (Pfizer, Computational Sciences CoE); Simon Xi (Pfizer, Computational Sciences CoE); |
| Short Abstract: Gene Set Interpreter (GSI) is an application that enables scientists to interpret gene sets in the context of biological pathways, molecular functions, and disease relevance. It provides a flexible data repository and a framework for publication of computational methods. |
| Long Abstract:Click Here |
|
| Poster G12 |
| Proteins, Pathways, and Macromolecular Complexes |
| Judith Blake- The Jackson Laboratory |
| Harold Drabkin (The Jackson Laboratory, Bioinformatics/Computational Biology); Alexei Evsikov (The Jackson Laboratory, Bioinformatics/Computational Biology); Carol Bult (The Jackson Laboratory, Bioinformatics/Computational Biology); Peter D'Eustachio (Cold Spring Harbor Laboratory, Genomics Cecila ); Natale Darren (Georgetown University, Protein Informatics Resource); Cathy Wu (Georgetown University and University of Delaware, Protein Informatics Resource); Cecilia Arighi (Georgetown University, Protein Informatics Resource); |
| Short Abstract: We report on ontological cross-linking of protein pathways within MouseCyc using the Protein Ontology and the Gene Ontology. We aligned GO with MouseCyc; and ensured that PRO represented specific isoforms and protein complexes. We illustrate our approach using ceramide biosynthetic pathway(MouseCyc) and sphingomyelin biosynthetic process(GO). |
| Long Abstract:Click Here |
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| Poster G13 |
| Noise reduction in high-throughput gene perturbation screens |
| Danni Yu- Purdue University |
| John Danku (Purdue University, Horticulture & Landscape Architecture); Ivan Baxter (Donald Danforth Plant Science Center, USDA-ARS Plant Genetics Research Unit); Sungjin Kim (Cornell University, Crop and Soil Sciences); Olena K. Vatamaniuk (Cornell University, Crop and Soil Sciences); David E. Salt (Purdue University, Bindley Bioscience Center, Discovery Park); Olga Vitek (Purdue University, Statistics); |
| Short Abstract: The poster addresses the problem of quantification of biological phenotypes in genome-wide perturbation screens. We propose a two-step normalization and quantification approach based on mixed models. We illustrate the approach using three comprehensive ionomic screens of S. cerevisiae, and demonstrate that it yields more accurate quantification than current alternatives. |
| Long Abstract:Click Here |
|
| Poster G14 |
| Information-based fitness and genetic load of SNPs affecting mRNA splicing |
| Peter Rogan- The University of Western Ontario |
| Eliseos Mucaki (The University of Western Ontario, Biochemistry); |
| Short Abstract: We predict HapMap SNPs affecting mRNA splicing using information theory. Information-related fitness and genetic loads are derived by partitioning SNPs that alter information contents of overlapping mRNA splice sites according to their allele frequencies. Results are compared with genome-wide exon microarray expression and qRT-PCR studies of each genotype. |
| Long Abstract:Click Here |
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| Poster G15 |
| High-throughput analysis of miRNAs regulating the Estrogen Receptor |
| Pekka Kohonen- University of Turku |
| Suvi-Katri Leivonen (VTT Technical Research Centre of Finland and University of Turku, Medical Biotechnology); Rami Mäkelä (VTT Technical Research Centre of Finland and University of Turku, Medical Biotechnology); Päivi Östling (VTT Technical Research Centre of Finland and University of Turku, Medical Biotechnology); Saija Haapa-Paananen (VTT Technical Research Centre of Finland and University of Turku, Medical Biotechnology); Kristine Kleivi (Rikshospitalet-Radiumhospitalet Medical Center, Department of Genetics); Espen Enerly (The Norwegian Radium Hospital , Department of Genetics); Anna Aakula (VTT Technical Research Centre of Finland and University of Turku, Medical Biotechnology); Kirsi Hellström (University of Helsinki, Animal Virus Laboratory); Niko Sahlberg (University of Oslo, the Biotechnology Centre, The Chemical Biology Platform); Vessela Kristensen (University of Oslo, Faculty of Medicine and Rikshospitalet-Radiumhospitalet, Department of Genetics); Anne-Lise Børresen-Dale (Rikshospitalet-Radiumhospitalet, Department of Genetics); Petri Saviranta (VTT Technical Research Centre of Finland, Medical Biotechnology); Merja Perälä (VTT Technical Research Centre of Finland, Medical Biotechnology); Olli Kallioniemi (University of Helsinki and VTT Technical Research Centre of Finland, Institute for Molecular Medicine Finland); |
| Short Abstract: High-throughput protein lysate microarray technology is a novel, powerful technique to determine the impact of miRNAs on key target proteins and associated pathways. Estrogen receptor-alpha (ERalpha) protein levels were monitored after overexpression of 319 pre-miRs in breast cancer cells. We identified and validated 21 miRNAs that downregulated the ERalpha. |
| Long Abstract:Click Here |
|
| Poster G16 |
| Incorporating developmental information in functional networks for Arabidopsis thaliana |
| Ana Pop- Princeton University |
| Curtis Huttenhower (Harvard University, Biostatistics); Olga Troyanskaya (Princeton University, Computer Science); |
| Short Abstract: We provide a compendium of functional relationship networks for Arabidopsis thaliana leveraging data integration based on various interaction datasets. These networks include tissue, biological process, and development stage specific interaction predictions. We validated many of our predicted networks' predictions, demonstrating their importance in the generation of biological hypotheses. |
| Long Abstract:Click Here |
|
| Poster G17 |
| Predicting specific roles for metabolic isozymes in Saccharomyces cerevisiae. |
| Patrick Bradley- Princeton University |
| Amy Caudy (Princeton University, Lewis Sigler Institute for Integrative Genomics); Olga Troyanskaya (Princeton University, Computer Science; Lewis Sigler Institute for Integrative Genomics); Joshua Rabinowitz (Princeton University, Chemistry; Lewis Sigler Institute for Integrative Genomics); |
| Short Abstract: Metabolic isozymes of the same compartment (MISCs) are retained despite apparent redundancy. We develop a method, applied in Saccharomyces cerevisiae, that uses a gene expression compendium to predict more specific roles for MISCs. This method implicates certain MISCs in the respirofermentative transition and oxygen utilization. We test these findings experimentally. |
| Long Abstract:Click Here |
|
| Poster G18 |
| Studies of Sesame Phytoestrogenic Lignans Derivatives as Selective Endothelial Nitric Oxide Synthetase Modulators (SNOSM) in Male Sprague Rats Testis. |
| lukeman joseph shittu- benue state university, college of health sciences |
| Remilekun Keji shittu (Bolomedic Laboratory, microbiology); prof Patrick igbigbi (delta state university, college of health sciences, anatomy); |
| Short Abstract: concern over the decreasing sperm counts of male animals has been expressed over the past few decades due to their exposure to some environmental endocrine disruptors/toxicants. However, sesame phytoestrogenic lignans have been found to enhance and improved testicular sperm parameters due to its estrogenic activities and antioxidant nature. Moreover, NO and eNOS are implicated too. |
| Long Abstract:Click Here |
|
| Poster G19 |
| Generation of Mycobacterium tuberculosis functional networks for the characterization of proteins of unknown function |
| Nicola Mulder- University of Cape Town |
| Gaston Mazandu (Computational Biology Group, IIDMM, University of Cape Town Health Sciences); |
| Short Abstract: A limitation in tuberculosis research is the large proportion of hypothetical proteins in the Mycobacterium tuberculosis genome, many of which may be important for virulence. We developed methods for integrating functional genomics data to generate functional networks between M. tuberculosis proteins, and use these to predict functions for uncharacterized proteins. |
| Long Abstract:Click Here |
|
| Poster G20 |
| An effective statistical evaluation of ChIP-seq dataset similarity |
| Maria Chikina- Princeton University |
| Olga Troyanskaya (Princeton University, Computer Science); |
| Short Abstract: We present a method for defining a similarity metric for two ChIP-seq datasets that is based on exact p-value calculations. Our metric rigorously accounts for heterogeneous dataset properties as well as prior knowledge of the background distribution of peak regions. |
| Long Abstract:Click Here |
|
| Poster G21 |
| Beyond the Bounds of Orthology: Functional Inference from Metagenomic Context |
| Gregory Vey- Wilfrid Laurier University |
| Gabriel Moreno-Hagelsieb (Wilfrid Laurier University, Biology); |
| Short Abstract: Prediction reliability metrics can be used to filter homolog-based predictions in the absence of known orthologous relationships to derive a reliable set of predicted functional interactions. This process is demonstrated using the Sargasso Sea metagenome to construct a functional interaction network (PPV = 0.80) for the Escherichia coli K12 genome. |
| Long Abstract:Click Here |
|
| Poster G22 |
| Identification and classification of ncRNAs in Trypanosoma cruzi through a multistep approach |
| Priscila Grynberg- Federal University of Minas Gerais |
| Priscila Grynberg (Federal University of Minas Gerais, Biochemistry and Immunology Department); Mainá Bitar (Federal University of Rio de Janeiro, IBCCF); Alexandre Paschoal (University of Sao Paulo, Institute of Mathmatics and Statistics); Alan Durham (University of Sao Paulo, Institute of Mathmatics and Statistics); Gloria Franco (Federal University of Minas Gerais, Biochemistry and Immunology Department); |
| Short Abstract: Non-coding RNAs (ncRNAs) prediction has become a vast field of research and several classes of ncRNAs with different regulatory, catalytic and structural functions have been discovered. We propose to predict and classify ncRNAs for the complete genome of Trypanosoma cruzi, using eQRNA for this purpose. |
| Long Abstract:Click Here |
|
| Poster G23 |
| The Epigenome of Pluripotent Cells: Variations of a Common Theme |
| Christoph Bock- Broad Institute of MIT and Harvard |
| No additional authors |
| Short Abstract: Here we evaluate the utility of epigenome and transcriptome profiling for quality assessment of human ES and iPS cells lines. |
| Long Abstract:Click Here |
|
| Poster G24 |
| cis-Decoder: A web-based tool for searching a genome-wide conserved sequence cluster database to identify functionally related cis-regulatory elements |
| Amarendra Yavatkar- National Institute of Neurological Disorders & Stroke (NINDS), National Institutes of Health (NIH) |
| Leonard Tyson (National Institute of Neurological Disorders & Stroke (NINDS), National Institutes of Health (NIH), Bioinformatics Section, Information Technology & Bioinformatics Program, Division of Intramural Research ); Thomas Brody (National Institute of Neurological Disorders & Stroke (NINDS), National Institutes of Health (NIH), Neural Cell-Fate Determinants Section); Yang Fann (National Institute of Neurological Disorders & Stroke (NINDS), National Institutes of Health (NIH), Bioinformatics Section, Information Technology & Bioinformatics Program, Division of Intramural Research ); Ward Odenwald (National Institute of Neurological Disorders & Stroke (NINDS), National Institutes of Health (NIH), Neural Cell-Fate Determinants Section); |
| Short Abstract: cis-Decoder is a suite of web-accessed algorithms that is used to search a genome-wide conserved sequence cluster (CSC) database to identify functionally related cis-regulatory enhancers. cis-Decoder identifies repeat sequences within CSCs and searches for related enhancers based on their shared conserved sequence elements. |
| Long Abstract:Click Here |
|
| Poster G25 |
| Reconstruction of ancestral compositions of multi-domain proteins |
| Roland Krause- MPI for Molecular Genetics |
| John Wiedenheoft (Free University Berlin, Computer Science); Oliver Eulenstein (Iowa State University, Computer Science); Martin Vingron (MPI for Molecular Genetics, Computational Molecular Biology); |
Short Abstract: We model the evolution of multi-domain proteins using a novel network-like structure called plexus to reconstruct ancestral domain combinations. We describe an algorithm to find a suitable plexus with the minimum number of macroevolutionary events given collection of domain histories as
phylogenetic trees, and
demonstrate its effectiveness in practice. |
| Long Abstract:Click Here |
|
| Poster G26 |
| Genome-scale methyltyping by statistical inference for determining methylation states in populations |
| Meromit Singer- UC Berkeley |
| Dario Boffelli (CHORI, .); Joseph Dhahbi (CHORI, .); Alexander Schoenhuth (UC Berkeley, Mathematics); Gary Schroth (Illumina, .); David Martin (CHORI, .); Lior Pachter (UC Berkeley, Mathematics); |
| Short Abstract: We introduce a statistical method, MetMap, which corrects for biases in MethylSeq data, producing genome-scale methylation annotations suitable for comparative and association studies, thus leveraging the cost-efficiency of the MethylSeq protocol. MetMap infers site-specific methylation states and unmethylated islands, and revealed many novel unmethylated islands from human neutrophil samples. |
| Long Abstract:Click Here |
|
| Poster G27 |
| EBI R CLOUD - Cloud Computing for Functional Genomics at the EBI |
| Andrew Tikhonov- EMBL-EBI |
| Misha Kapushesky (EMBL-EBI, Functional Genomics); Yuriy Aulchenko (ERASMUS MC, -); Angela Gonçalves (EMBL-EBI, Functional Genomics); Johan Rung (EMBL-EBI, Functional Genomics); Rodrigo Santamaria (EMBL-EBI, Functional Genomics); Alvis Brazma (EMBL-EBI, Functional Genomics); |
| Short Abstract: The EBI R CLOUD is a new service at the EBI, allowing advanced users of the statistical package R to log on and run distributed computational jobs remotely, making use of the powerful EBI infrastructure. The R CLOUD Workbench is an optimized graphical client to R on the cloud. |
| Long Abstract:Click Here |
|
| Poster G28 |
| The relative contribution of miRNAs in the regulation of di?erentiation |
| Sylvia Tippmann- Friedrich Miescher Institute |
| Michael Stadler (Friedrich Miescher Institute , Bioinformatics); Dirk Schübeler (Friedrich Miescher Institute , Epigenetics); |
| Short Abstract: Gene expression in eukaryotes is regulated on the transcriptional and post-transcriptional level. We address the question on the relative contribution of these two regulatory layers to the steady-state mRNA level. We model transcription rate with epigenetic marks and Pol-II occupancy and quantify post-transcriptional regulation using model residuals. |
| Long Abstract:Click Here |
|
| Poster G29 |
| miRNA prediction with SOLiD small RNA deep-sequencing data of 10 nematode species |
| Rina Ahmed- Max Delbrück Center for Molecular Medicine Berlin |
| Rina Ahmed (Max Delbrück Center for Molecular Medicine Berlin, Berlin Institute for Medical Systems Biology); Zisong Chang (Max Delbrück Center for Molecular Medicine Berlin, Berlin Institute for Medical Systems Biology); Matthias Dodt (Max Delbrück Center for Molecular Medicine Berlin, Berlin Institute for Medical Systems Biology); Claudia Langnick (Max Delbrück Center for Molecular Medicine Berlin, Berlin Institute for Medical Systems Biology); Wei Chen (Max Delbrück Center for Molecular Medicine Berlin, Berlin Institute for Medical Systems Biology); Christoph Dieterich (Max Delbrück Center for Molecular Medicine Berlin, Berlin Institute for Medical Systems Biology); |
| Short Abstract: miRNA are important post-transcriptional gene regulators. We developed a k-mer based miRNA prediction strategy for species without genome data, and applied our approach to SOLiD high-throughput sequencing data of small ncRNAs of 10 different nematodes. We present a miRNA catalogue for the nematodes and study the turnover of miRNA families. |
| Long Abstract:Click Here |
|
| Poster G30 |
| ChIP-seq versus ChIP-chip: a systematic comparison of two technologies |
| Joshua Ho- Brigham and Women's Hospital and Harvard Medical School |
| Eric Bishop (Boston University, Program in Bioinformatics); Peter Kharchenko (Harvard Medical School, Center for Biomedical Informatics); Nicolas Negre (University of Chicago, Institute for Genomics and Systems Biology); Kevin White (University of Chicago, Institute for Genomics and Systems Biology); Peter Park (Harvard Medical School, Center for Biomedical Informatics); |
| Short Abstract: We present the first systematic assessment of technical variation within and between ChIP-seq and ChIP-chip using a modENCODE dataset. We show that the set of peaks identified by the two technologies can be significantly different, but the extend to which they are different varies depending on the analysis algorithms used. |
| Long Abstract:Click Here |
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| Poster G31 |
| The Ontological Discovery Environment: A Web-based Software System for Combinatorial Cross-Species Functional Genomic Data Integration |
| Jeremy Jay- University of Tennessee |
| Michael A Langston (University of Tennessee, Electrical Engineering and Computer Science); Erich J Baker (Baylor University, Computer Science); Elissa J Chessler (The Jackson Laboratory, ); |
| Short Abstract: The Ontological Discovery Environment (http://ontologicaldiscovery.org) is a system to integrate diverse gene centered data collected across species and experimental contexts. Using scalable combinatorial approaches, a suite of tools enable users to direct integrative analyses and share data across groups. A new module enables a neighborhood search technique for gene-seeded query. |
| Long Abstract:Click Here |
|
| Poster G32 |
| Joint ChIP-Seq event discovery using the Genome Positioning System (GPS) |
| Yuchun Guo- Massachusetts Institute of Technology |
| Shaun Mahony (MIT, CSAIL); Georgios Papachristoudis (MIT, CSAIL); Robert Altshuler (MIT, CSAIL); Georg Gerber (Harvard Medical School, Pathology); Tommi Jaakkola (MIT, CSAIL); David Gifford (MIT, CSAIL); |
| Short Abstract: The Genome Positioning System (GPS) uses ChIP-Seq data to detect protein-DNA interaction events at high spatial resolution while retaining the ability to resolve closely spaced events that appear as a single cluster of reads. GPS models observed reads using a complexity penalized mixture model and efficiently predicts event locations with a segmented EM algorithm. |
| Long Abstract:Click Here |
|
| Poster G33 |
| Haplotype specific DNA methylation in mouse inbred strains and its contribution to transcriptional diversity |
| Robert Ivanek- Friedrich Miescher Institute for Biomedical Research |
| Tim Roloff (Friedrich Miescher Institute for Biomedical Research, Core Facility); Leslie Hoerner (Friedrich Miescher Institute for Biomedical Research, Epigenetics); Leonardo Iniguez (Roche, NimbleGen); Andrew Su (Genomics Institute of the Novartis Research Foundation, ); Dirk Schuebeler (Friedrich Miescher Institute for Biomedical Research, Epigenetics); |
| Short Abstract: We studied epigenetic differences of the mouse methylome in different inbred strains. Since these strains have been extensively studied in regards to their expression differences, the observed methylation differences can be linked to genetic and expression diversity. Ultimately, the results could be used as guide to the similar studies in outbred populations, such as human. |
| Long Abstract:Click Here |
|
| Poster G34 |
| A spectral clustering and information integration framework to mine gene sets using heterogeneous data sources |
| Adam Richards- Medical University of South Carolina |
| John Schwacke (Medical University of South Carolina, Department of Biochemistry and Molecular Biology); L. Ashley Cowart (Medical University of South Carolina, Department of Biochemistry and Molecular Biology); Baerbel Rohrer (Medical University of South Carolina, Department of Neurosciences); Xinghua Lu (Medical University of South Carolina, Department of Biochemistry and Molecular Biology); |
| Short Abstract: A common task in bioinformatics is to mine large gene lists for functional subsets using multiple sources of information. How to integrate heterogeneous information in a principled manner remains a challenging task. We report a platform for management, integration and subset searching within the framework of graph-spectrum analysis |
| Long Abstract:Click Here |
|
| Poster G35 |
| An integrative ChIP-sequencing analysis and motif discovery platform for genome-wide TFBS identification |
| LAKSHMI KUTTIPPURATHU- Harvard-MIT health science and technology |
| Michael Hsing (Harvard-MIT Health Science and Technology, Childrens Hospital Informatics Program); Sek Won Kong (Childrens Hospital/Harvard Medical School, Childrens Hospital Informatics Program); |
| Short Abstract: We developed an integrated web resource for analyzing high-throughput ChIP data to identify transcription factor binding sites. It streamlines a peak detection tool, motif discovery algorithms, a novel statistical scoring, and annotations with visualization into a comprehensive platform, which would be useful for biologists to study gene expression regulation. |
| Long Abstract:Click Here |
|
| Poster G36 |
| Whole gene functional variant identification using a conservation based feature model |
| Kjong Lehmann- University of Southern California |
| Ting Chen (University of Southern California, Molecular and Computational Biology); |
| Short Abstract: We present the design of a new model, SInBaD (Sequence-Information-Based-Decision-model), relying on nucleotide sequence conservation to identify functional variants including intron and promoter regions overcoming previous limitations to coding regions. Our results have been validated through cross validation simulations and on experimentally confirmed disease variants. |
| Long Abstract:Click Here |
|
| Poster G37 |
| Genome wide map of meiotic double stranded break hotspots in the mouse |
| Kevin Brick- NIDDK, National Institutes of Health |
| Ivan Gregoretti (NIDDK, National Institutes of Health, Genetics and Biochemistry Branch); Pavel Khil (NIDDK, National Institutes of Health, Genetics and Biochemistry Branch); Fatima Smagulova (Uniformed Services University of Health Sciences, Department of Biochemistry and Molecular Biology); Galina Petukhova (Uniformed Services University of Health Sciences, Department of Biochemistry and Molecular Biology); Dan Camerini-Otero (NIDDK, National Institutes of Health, Genetics and Biochemistry Branch); |
| Short Abstract: Most meiotic recombination occurs in hotspots which constitute ~5% of genomic DNA. Using ChIP-Seq with antibodies against proteins which bind meiotic DNA double-stranded breaks, we have precisely identified hotspots in the mouse genome. H3K4me3 marks, putative histone-methyltransferase binding motifs and well positioned nucleosomes are characteristic of these regions. |
| Long Abstract:Click Here |
|
| Poster G38 |
| Functional analysis of metagenome data using MEGAN4 |
| Suparna Mitra- Algorithms in Bioinformatics, University of Tuebingen |
| Nico Weber (Algorithms in Bioinformatics, University of Tuebingen, ZBIT Center for Bioinformatics); Daniel Huson (Algorithms in Bioinformatics, University of Tuebingen, ZBIT Center for Bioinformatics); |
| Short Abstract: This poster demonstrates functional analysis of metagenome-, metatranscriptome- and metaproteome data using MEGAN4. Genes are mapped onto functional roles that are grouped into subsystems, based on the SEED system. All navigation and analysis tools that were previously provided only for taxonomic analysis can now also be used for functional analysis. |
| Long Abstract:Click Here |
|
| Poster G39 |
| Illuminating Complete Functional Networks: Automation, Computation and the Single Cell |
| Michael Fero- Stanford University |
| Beat Christen (Stanford University School of Medicine, Developmental Biology); Nathan Hillson (Joint BioEnergy Institute, Fuels Synthesis Division); Grant Bowman (Stanford University School of Medicine, Developmental Biology); Sun-Hae Hong (Stanford University School of Medicine, Developmental Biology); Lucy Shapiro (Stanford University School of Medicine, Developmental Biology); Harley McAdams (Stanford University School of Medicine, Developmental Biology); |
| Short Abstract: Advances in high resolution microscopy and quantitative analysis of subcellular protein location and abundance have greatly enriched number and quality of phenotypic measurements accessible to the biologist. Now, with the development of high speed data acquisition and automated analysis, single subcellular level data can be gathered in the context of saturated genetic screens, yielding rich data sets that can be used to associate genotypic disruptions or perturbations with clear subcellular phenotypes. Modern screening techniques allow us to probe gene function on a genome scale, from engineered transposons in prokaryotic systems, to systematic knock-outs in Saccharomyces cerevisiae, to RNA interference (RNAi) knock-down libraries in a variety of organisms By combining rich subcellular phenotypic data with known gene perturbations on a genome scale, we can now identify key functional networks including signaling, transcription and protein localization factors in a single genetic screen. |
| Long Abstract:Click Here |
|
| Poster G40 |
| The Discern Active Site Predictor |
| Sriram Sankararaman- U C Berkeley |
| Kimmen Sjolander (UC Berkeley, Bioengineering); Michael Jordan (UC Berkeley, EECS); Jack Kirsch (UC Berkeley, QB3 Institute); Fei Sha (Univ. Southern California, Computer Science); |
| Short Abstract: Discern is an enzyme active site predictor providing a significant improvement over the state-of-the-art through the use of three important techniques: INTREPID evolutionary conservation scores, inclusion of features at structural neighbors, and statistical sparsification using L1-regularization. An examination of Discern parameters provides insight into the characteristics of enzyme active sites. |
| Long Abstract:Click Here |
|
| Poster G41 |
| The Genetic Landscape of a Cell |
| Anastasia Baryshnikova- University of Toronto |
| Michael Costanzo (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Jeremy Bellay (University of Minnesota, Department of Computer Science and Engineering); Yungil Kim (University of Minnesota, Department of Computer Science and Engineering); Eric D. Spear (Massachusetts Institute of Technology, Department of Biology); Carolyn S. Sevier (Massachusetts Institute of Technology, Department of Biology); Huiming Ding (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Judice L. Y. Koh (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Kiana Toufighi (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Sara Mostafavi (University of Toronto, Banting and Best Department of Medical Research; Department of Computer Science); Jeany Prinz (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Robert P. St. Onge (Stanford University, Department of Biochemistry, Stanford Genome Technology Center); Benjamin VanderSluis (University of Minnesota, Department of Computer Science and Engineering); Taras Makhnevych (University of Toronto, Department of Biochemistry); Franco J. Vizeacoumar (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Solmaz Alizadeh (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Sondra Bahr (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Renee L. Brost (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Yiqun Chen (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Murat Cokol (Harvard Medical School, Department of Biological Chemistry and Molecular Pharmacology); Raamesh Deshpande (University of Minnesota, Department of Computer Science and Engineering); Zhijian Li (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Zhen-Yuan Lin (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Wendy Liang (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Michaela Marback (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Jadine Paw (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Bryan-Joseph San Luis (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Ermira Shuteriqi (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Amy Hin Yan Tong (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Nydia van Dyk (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Iain M. Wallace (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research; Department of Pharmacy); Joseph A. Whitney (University of Toronto, Banting and Best Department of Medical Research; Department of Computer Science); Matthew T. Weirauch (University of California, Santa Cruz, Department of Biomolecular Engineering); Guoqing Zhong (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Hongwei Zhu (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Walid A. Houry (University of Toronto, Department of Biochemistry); Michael Brudno (University of Toronto, Banting and Best Department of Medical Research; Department of Computer Science); Sasan Ragibizadeh (S&P Robotics, Inc., ); Balazs Papp (Biological Research Center, Institute of Biochemistry); Csaba Pal (Biological Research Center, Institute of Biochemistry); Frederick P. Roth (Harvard Medical School, Department of Biological Chemistry and Molecular Pharmacology); Guri Giaever (University of Toronto, Department of Molecular Genetics; Department of Pharmacy); Corey Nislow (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Olga G. Troyanskaya (Princeton University, Department of Computer Science, Lewis-Sigler Institute for Intergrative Genomics, Carl Icahn Laboratory); Howard Bussey (McGill University, Biology Department); Gary D. Bader (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Anne-Claude Gingras (Mount Sinai Hospital, Samuel Lunenfeld Research Institute); Quaid D. Morris (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research; Department of Computer Science); Philip M. Kim (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Chris A. Kaiser (Massachusetts Institute of Technology, Department of Biology); Chad L. Myers (University of Minnesota, Department of Computer Science and Engineering); Brenda J. Andrews (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); Charles Boone (University of Toronto, Banting and Best Department of Medical Research; Department of Molecular Genetics; Terrence Donnelly Centre for Cellular and Biomolecular Research); |
| Short Abstract: Understanding how genes of an organism interact with one another to produce complex phenotypes is a primary challenge in deciphering the functional organization of living cells and the genetic basis of disease. We examined 5.4 million gene-gene pairs in yeast Saccharomyces cerevisiae and generated the largest genetic interaction map to date, containing genetic interaction profiles for ~75% of all yeast genes. Our genetic interaction network reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets. Highly correlated genetic profiles delineate specific pathways and enable the prediction of novel gene function. We also demonstrate that unbiased genome-wide mapping of the genetic interactions provides a key for interpreting chemical-genetic interactions and identifying drug targets. Finally, we show that genetic interactions identify functional cross-connections between all bioprocesses and correlate with several gene attributes, which may be informative about genetic network hubs in other organisms. |
| Long Abstract:Click Here |
|
| Poster G42 |
| Quantitative functional annotation of H. sapiens genes |
| Frederick Roth- Harvard Medical School |
| No additional authors |
Short Abstract: Despite the wealth of human genomic and proteomic evidence, a surprisingly small
fraction of genes have clear, documented associations with speci?c functions, and
new functions continue to be found for 'characterized' genes. In addition to archival
annotation, there is a need to guide ongoing experimentation by summarizing shades
of gray in current knowledge. We assembled an integrated collection of diverse genomic and proteomic evidence for 21341 H. sapiens genes. This resource was used to train inferential models combining 'guilt-by-pro?ling' and 'guilt-by-association' approaches to quantitatively annotate each gene to 4333 Gene Ontology (GO) terms.
Performance was evaluated by cross-validation, prospective validation, and by careful evaluation of biological literature. As part of the modeling process we constructed twelve distinct functional-linkage networks (FLNs), each capturing one of
twelve types of functional relationship between human genes. We demonstrate the utility of human FLNs by identifying candidate genes related to a glioma FLN using a seed network from genome-wide association studies (GWAS). All of our predictions are made available to the community via an online web-accessible searchable
resource (http://func.med.harvard.edu). Thus, we have established a genome-scale quantitative functional annotation resource for human genes. |
| Long Abstract:Click Here |
|
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