Accepted Posters

Category 'Z'- Other'
Poster Z01
Advancing Research with Bioinformatics Training: The Canadian Bioinformatics Workshop Series
Michelle Brazas- Ontario Institute for Cancer Research
Francis Ouellette (Ontario Institute for Cancer Research, Informatics and Biocomputing);
Short Abstract: Bioinformatics skills play a fundamental role in health research today, particularly with the introduction of new high-throughput technologies such as library screens and next-generation sequencers. The challenge is in acquiring the advanced level bioinformatics skills required of these new technologies and data-sets. The Canadian Bioinformatics Workshops is evolving such opportunities.
Long Abstract:Click Here

Poster Z02
Towards automated high-throughput screening of C. elegans on agar
Mayank Kabra- University of California, San Diego
Annie Conery (Massachusetts General Hospital, Molecular Biology and Center for Computational and Integrative Biology); Eyleen O'Rourke (Massachusetts General Hospital, Molecular Biology and Center for Computational and Integrative Biology); Xin Xie (University of California, San Diego, Computer Science and Engineering); Vebjorn Ljosa (Broad Institute, Imaging Platform); Thouis Jones (Broad Institute, Imaging Platform); Frederick Ausubel (Massachusetts General Hospital, Department of Molecular Biology and Center for Computational and Integrative Biology); Gary Ruvkun (Massachusetts General Hospital, Department of Molecular Biology and Center for Computational and Integrative Biology); Anne Carpenter (Broad Institute, Imaging Platform); Yoav Freund (University of California, San DIego, Computer Science and Engineering);
Short Abstract: We present progress towards applying computer vision and machine learning methods to analyze High Throughput Screening experiments that use C. elegans grown on agar. We present a robust segmentation algorithm for separating the worms from the background using brightfield images and results on phenotype separation using Nile-Red fluorescent dye.
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Poster Z03
Occupancy Classification of Position Weight Matrix-inferred Transcription Factor Binding Sites
Hollis Wright- Oregon Health and Science University
Hollis Wright (Oregon Health and Science University, Department of Medical Informatics and Clinical Epidemiology); Aaron Cohen (Oregon Health and Science University, Department of Medical Informatics and Clinical Epidemiology); Kemal Sönmez (Oregon Health and Science University, Department of Medical Informatics and Clinical Epidemiology); Gregory Yochum (Pennsylvania State University College of Medicine, Department of Biochemistry and Molecular Biology); Shannon McWeeney (Oregon Health and Science University, Medical Informatics and Clinical Epidemiology, Biostatistics);
Short Abstract: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. We additionally construct generalizable classifiers based upon combination of multiple TF data sets and classifiers, and evaluate the performance and characteristics of these classifiers.
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Poster Z04
Motion-compensated in vivo microscopy of heart morphogenesis and function in developing embryos
Jungho Ohn- University of California, Santa Barbara
Michael Liebling (University of California, Santa Barbara, Electrical and Computer Engineering);
Short Abstract: We have developed a non-invasive, high-speed, multi-dimensional, and high resolution in vivo microscopy technique that includes an active sample drift compensation also for imaging the beating and developing heart at the cellular level. Our movies of the developing zebrafish embryo heart show endothelial cell divisions in the beating heart.
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Poster Z05
Design of Inorganic Binding Peptides
Kemal Sonmez- Department of Biomedical Computer Science Oregon Health and Science University
Kemal Sonmez (Oregon Health and Science University, Biomedical Computer Science);
Short Abstract: Inorganic materials are incorporated into tissues such as such as bone, teeth, and shells using proteins and enzymes that direct their assembly . Understanding and reverse engineering these processes can lead the way to many advances in medicine, including implants with functionally optimized biointerfaces, and bone and tooth regeneration. The engineering of such rationally designed bio-interfaces for medical applications relies upon development of predictive models of inorganic-peptide interactions. Recently, phage display based bio-combinatorial selection techniques have resulted in the isolation of peptides with short amino acid sequences that specifically bind to a variety of metals and oxides, providing a valuable resource for modeling of peptide-solid interactions relevant for numerous biomedical applications. We present a novel in silico design methodology that uses finite state machines to sample from a SVM kernel-based model of the frequency domain features of experimentally identified peptides to predict novel peptide sequences with specific inorganic binding properties, such as a strong-binder for gold and a weak-binder to quartz. The method is based on learning from n-gram counts of chemical property classes of amino acid residues, and then using weighted finite state transducers to predict sequences rich in features strongly associated with binding activity. The novel contribution of our approach is the SVM-directed probabilistic sampling of a finite state machine to predict peptides with specific binding properties. Finally, a key contribution of this work is that it introduces OpenFST, a powerful computational framework that drives many real world applications, to applications in computational biology.
Long Abstract:Click Here

Poster Z06
Effector prediction in host-pathogen interaction based on a Markov model of a ubiquitous EPIYA motif
Chao Zhang- University of Missouri
Chao Zhang (University of Missouri, Computer Science); Dong Xu (University of Missouri, Computer Science);
Short Abstract: Effector secretion is a common strategy of pathogen in mediating host-pathogen interaction. Eight EPIYA-motif containing effectors have recently been discovered in six pathogens. Once these effectors enter host cells through type III/IV secretion systems (T3SS/T4SS), tyrosine in the EPIYA-motif is phosphorylated, which triggers the effector binding other proteins to manipulate host-cell functions. A hidden Markov model (HMM) of five amino acids was built for the EPIYA-motif based on those eight known effectors. Using this HMM to search the non-redundant protein database containing 9,216,047 sequences, we obtained 107,231 sequences with at least one EPIYA-motif occurrence and 3115 sequences with multiple. Although the EPIYA-motif exists among broad species, it is significantly over-represented in some particular groups of species. For those proteins containing at least four copies of EPIYA-motif, most of them are from intracellular bacteria, extracellular bacteria with T3SS/T4SS or intracellular protozoan parasites. By combining the EPIYA-motif and the adjacent SH2 binding motifs (KK, R4, Tarp and Tir), we built others HMMs with nine amino acids and predicted many potential effectors for pathogens in bacteria and protista by the HMMs. Our study indicates that the EPIYA motif may be a ubiquitous functional site for effectors that play an important pathogenicity role in mediating host-pathogen interactions. We also suggest that some intracellular protozoan parasites could secrete EPIYA-motif containing effectors through secretion systems similar to the T3SS/T4SS in bacteria. Our predictions provide useful hypotheses for further and also have the scientific and clinical implications for prevention and treatment of infectious diseases.
Long Abstract:Click Here

Poster Z07
Mobyle Platform: Bioinformatics applications from command-line to web-based workflows and services
Herve Menager- Institut Pasteur
Vivek Gopalan (Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA, Bioinformatics and Computational Biosciences Branch (BCBB)); Bertrand Neron (Institut Pasteur, Genomes and Genetics - Software and Databases); Sandrine Larroude (Institut Pasteur, Genomes and Genetics - Software and Databases); Pierre Tuffery (Université Paris Diderot / INSERM, MTi / RPBS); Yentram Huyen (Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA, Bioinformatics and Computational Biosciences Branch (BCBB));
Short Abstract: Mobyle platform provides a web-based solution to integrate diverse command-line applications for computational biologists and researchers to use in their analysis and share with their peers. It provides tools to generate homogenous web-interfaces for applications, to integrate related applications as workflow and to easily design web-interfaces for applications.
Long Abstract:Click Here

Poster Z08
Efficiently generating multi-biomarker ROC curves to identify significant multi-biomarkers
Amol Prakash- Thermo Fisher Scientific
No additional authors
Short Abstract: Biomarkers have great potential in predicting chances for diseases, aiding in early diagnosis, and setting standards for the development of new remedies to treat diseases. A good biomarker should be precise and reliable, distinguishable between normal and interested disease, and differentiable between different diseases. The information that can be derived from a biomarker includes disease diagnostics, disease progression, pathway control, and regulatory behavior. Often, such biomarker measurement experiments are performed on a large sample set with a similarly sized control set, followed by identification of significant markers. ROC (Receiver Operating Characteristic) curves are a robust and popular instrument to characterize biomarkers. Unfortunately, verification of these markers in multiplexed assays poses a statistical challenge as traditional ROC curves used to calculate the sensitivity and specificity of a diagnostic or predictive assay are based on single markers. The ability to combine quantitative information from several markers could potentially improve the diagnostic accuracy of existing tests and facilitate the development of new tests. However multi-marker ROC curves are computationally expensive to compute, and therefore have not been used. We suggest a novel algorithm to enable efficient computation of ROC curves of pair-markers and apply it to a large data set to identify significant pair markers. The definition of a ROC curve of pair marker is also presented.
Long Abstract:Click Here

Poster Z09
BioCatalogue: The curated registry of Life Science Web Services
Franck Tanoh- University of Manchester
Jiten Bhagat (University of Manchester, School of computer sciences); Katy Wolstencroft (University of Manchester, School of computer sciences); Steve Pettifer (University of Manchester, School of computer sciences); Carole Goble (University of Manchester, School of computer sciences); Robert Stevens (University of Manchester, School of computer sciences); Thomas Laurent (EBI , EBI); Eric Nzuobontane (EBI , EBI); Rodrigo Lopez (EBI , EBI);
Short Abstract: The BioCatalogue is a registry of biological Web Services with a goal to provide a centralised and curated catalogue of Life Science Web Services.
A place where the community can find, register, monitor or annotate Web Services, and a platform for Web Service providers to publish their services.
Long Abstract:Click Here

Poster Z10
Improved models for in vitro selection of aptamers
Alexandra Teslya- Ryerson University
Gregory Penner (NeoVentures Biotechnology Inc., London, Ontario); Silvana Ilie (Ryerson University, Mathematics);
Short Abstract: Aptamers, single-stranded DNA molecules that bind to particular molecular targets with high affinity and specificity, are isolated for many critical applications in science and medicine. Aptamers isolation process is commonly modeled by SELEXION and our work aims at providing superior mathematical models that lead to improved effectiveness of this process.
Long Abstract:Click Here

Poster Z11
Methodological analysis of QR markers to identify and classify functional parts
Allan Orozco- Spanish National Cancer Research Centre (CNIO)
No additional authors
Short Abstract: The work consists on the design of a generic methodology to identify and classify virtual protein structures through its functional constitutive parts by QR markers (variation of the bar code but constituted by points) and video digital processing (augmented reality)). Finally, this methodology was implemented and programmed on demo software that shows the operational phases.
Long Abstract:Click Here

Poster Z12
A User Study of Attribute Presentation Tools and Their Role in Understanding Biological Networks
Hande Küçük- Eastern Michigan University
Benjamin Keller (Eastern Michigan University, Computer Science); Terry E Weymouth (University of Michigan, School of Informatics, NCIBI); Barbara Mirel (University of Michigan, School of Education);
Short Abstract: We present a user study to determine how attribute presentation affects a user's ability to understand and analyze molecular interaction networks. Users were asked to perform three tasks using three tools within Cytoscape with table-, list-, and chart-based presentations. The results indicate that interactive visual presentations improve user task performance.
Long Abstract:Click Here

Poster Z13
MobyleNet: user-centered large spectrum service integration over distributed sites.
Herve Menager- Institut Pasteur
Sandrine Larroude (Institut Pasteur, Genomes and Genetics - Software and Databases); Julien Maupetit (Université Paris Diderot / INSERM, MTi / RPBS); Bertrand Néron (Institut Pasteur, Genomes and Genetics - Software and Databases); Adrien Saladin (INSERM, MTi); Bernard Caudron (Institut Pasteur, Genomes and Genetics - Software and Databases); Pierre Tuffery (Université Paris Diderot / INSERM, MTi / RPBS);
Short Abstract: The MobyleNet project is a network which aims at federating existing bioinformatics platforms using the Mobyle framework. The goals are to integrate the services located on different nodes, covering complementary aspects of bioinformatics. The use of a single framework aims at favoring the interoperability of the services and quality management.
Long Abstract:Click Here

Poster Z14
Decipher the protein cofactors for small RNA function by comprehensive phylogenetic analysis, protein interactions, expression data, high throughput screens and other data sets.
Yuval Tabach- Harvad/ MGH
No additional authors
Short Abstract: Small RNAs are short (~21-30nt) single-stranded RNA molecules which regulate chromatin structure, chromosome segregation, transcription, RNA processing, RNA stability, and translation. Systematically identifying the protein components of the small RNA world and understanding how they integrate into other molecular pathways represents a major challenge in cell biology. In an effort to more comprehensively identify genes that contribute to small RNA pathways, we experimentally and computationally collected different genome-scale data sets including: protein-protein interaction, phylogenetic profiles, expression databases, RNAi phenotypes, genetic interactions, interolog interactions, mass spectrometry data and RNAi screens to detect genes involved in microRNA and siRNA. Alone, each dataset has limitations and biases that result in limited sensitivity and specificity.
Combining these different resources provided clues on gene functionality and increase our confidence in prediction of new tinyRNA genes while reduce the false prediction rate. We integrated the data to generate a list of genes that are highly implicated as functioning in small RNA pathways. Our results of this integrative approach show high sensitivity and specificity and suggest novel genes from many noisy datasets which are highly likely to be part of the tiny RNA pathways.
Long Abstract:Click Here

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