20th Annual International Conference on
Intelligent Systems for Molecular Biology
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Highlights Track Presentations

New for 2012!

All Highlights and Proceedings Track presentations are presented by scientific area part of the combined Paper Presentation schedule.
A full schedule of Paper Presentations can be found here.

Applied Bioinformatics
Bioimaging & Data Visualization
Databases and Ontologies
Disease Models and Epidemiology
Evolution and Comparative Genomics
Gene Regulation and Transcriptomics
Mass Spectrometry and Proteomics
Population Genomics
Protein Interactions and Molecular Networks
Protein Structure and Function
Sequence Analysis
Other


Highlights Track: Applied Bioinformatics
Presenting author: Lun Yang , GlaxoSmithKline, United States
Tuesday, July 17: 2:30 p.m. - 2:55 p.m.Room: 104A

Additional authors:
Lin He, Shanghai Jiao Tong U, China
Kejian Wang, Shanghai Jiao Tong U, China
Heng Luo, Shanghai Jiao Tong U, China

Area Session Chair: Terry Gaasterland

Presentation Overview:
Chemical-Protein Interactome is a computational methodology with a focus on characterizing differential drug efficacy and side effects through the combined analysis of genetic polymorphisms and their impact on chemical-protein interactions and gene expression perturbations. The methodology opens opportunities for developing patient-specific medication in terms of decreasing adverse drug reactions and broadening new uses for old drugs.
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Presenting author: Felix Kruger , European Bioinformatics Institute, United Kingdom
Tuesday, July 17: 3:00 p.m. - 3:25 p.m.Room: 104A

Additional authors:
John P Overington, European Bioinformatics Institute, United Kingdom

Area Session Chair: Terry Gaasterland

Presentation Overview:
We integrated small molecule bioactivity data and homology information and compared small molecule binding between pairs of human paralogs and also between curated pairs of human to rat orthologs. To account for noise in the data set, we further compared measurements of the same ligand and target in different assays. We found that differences in small molecule binding between human paralogs are greater than the assay sample error. In contrast, differences between human to rat orthologs are no greater than the sample error. We then analyzed, for pairs of human paralogs, the relationship between sequence identity and differences in small molecule binding. For a subset of the data, differences in small molecule binding are greater for pairs with more divergent sequences. We conclude that small molecule binding between human to rat orthologs is largely conserved while selectivity of small molecule binding was observed between pairs of human paralogs.
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Presenting author: Johannes Soeding , Ludwig-Maximilians-Univeristaet Muenchen, Germany
Monday, July 16: 10:45 a.m. - 11:10 a.m.Room: Grand Ballroom

Additional authors:
Michael Remmert, Ludwig-Maximilians-Univeristaet Muenchen, Germany
Andreas Biegert, genedata.com, Germany
Andreas Hauser, Ludwig-Maximilians-Univeristaet Muenchen, Germany

Area Session Chair: David Gifford

Presentation Overview:
Sequence-based protein function and structure prediction depends critically on sequence-search sensitivity and accuracy of the resulting sequence alignments. I will present HHblits (HMM-HMM–based lightning-fast iterative sequence search), an open-source, general-purpose search tool, which represents both query and database sequences by profile-hidden hidden Markov models (HMMs). Compared to the PSI-BLAST, HHblits is faster owing to its discretized-profile prefilter, has 50–100% higher sensitivity and generates more accurate alignments. It thus has the potential to improve many downstream analysis and prediction methods. I will first explain how HHblits achieves its sensitivity and speed and then show benchmarks and biological applications. If possible, I will finish by a short software demo.
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Presenting author: Marc Hulsman , TU Delft, Netherlands
Tuesday, July 17: 3:30 p.m. - 3:55 p.m.Room: 104A

Additional authors:
Hemant Unadkat, University of Twente, Netherlands
Kamiel Cornelissen, University of Twente, Netherlands
Bernke Papenburg, University of Twente, Netherlands
Roman Truckenmüller, University of Twente, Netherlands
Gerhard Post, University of Twente, Netherlands
Marc Uetz, University of Twente, Netherlands
Marcel Reinders, Delft University of Technology, Netherlands
Dimitrios Stamatialis, University of Twente, Netherlands
Clemens van Blitterswijk, University of Twente, Netherlands
Jan de Boer, University of Twente, Netherlands

Area Session Chair: Terry Gaasterland

Presentation Overview:
In the development of cellular tissues, not only growth factors play a role, but also the structural environment. With material surfaces shown to affect stem cell fate, new avenues are opening for improving the biological performance of (implant) surfaces used in the human body. In the present study, we developed a chip, allowing us to chart cell – surface topography interactions in high-throughput. Human mesenchymal stromal cells (hMSCs) were grown on the chips, and using high-content imaging, each individual cell response was measured. The results reveal formerly unknown surface topographies that are able to induce MSC proliferation or osteogenic differentiation. Moreover, using machine learning techniques, we correlate parameters of the surface designs to cellular responses, yielding new insight into surface design criteria, and enabling us to predict the performance of untested surfaces.
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Presenting author: Maria Chikina , Mount Sinai Medical School, United States
Tuesday, July 17: 10:45 a.m. - 11:10 a.m.Room: 104B

Additional authors:
Olga G. Troyanskaya, Princeton University, United States

Area Session Chair: Terry Gaasterland

Presentation Overview:
ChIPseq technology has become the state-of-the-art whole-genome technique for analyzing protein-DNA interactions, making it necessary to have rigorous methods for quantifying similarity between datasets, and defining interactions among chromatin features. This presents a statistical problem for which several solutions have been proposed.
While other methods for obtaining significance of similarity must make somewhat arbitrary choices of distance metrics, parametric distributions, or procedures for simulating the null hypothesis, we present a simple and intuitive approach for calculating exact p-values that is essentially assumption-free. Our approach is robust to non-biological variations and involves an asymmetric comparison, allowing one to tease out hierarchical relationships among chromatin proteins.
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Highlights Track: Bioimaging & Data Visualization
Presenting author: Robert Murphy , Carnegie Mellon University, United States
Monday, July 16: 2:30 p.m. - 2:55 p.m.Room: 104C

Additional authors:
Tao Peng, Microsoft Research, United States

Area Session Chair: Hagit Shatkay

Presentation Overview:
Computational modeling of cell behavior requires information on the spatiotemporal distribution of proteins. We previously developed the first system for automatically constructing generative models of subcellular location directly from microscope images (Zhao & Murphy, Cytometry 71A, 978-990, 2007). Those models were for 2D images, and the Peng & Murphy 2011 paper made the crucial extension to 3D. The Murphy 2011 paper described using these models for active learning of the effects of many perturbagens on many proteins. Subsequent work has integrated these approaches with generative models of microtubules into a cohesive, open source system, CellOrganizer (http://CellOrganizer.org). The system can output images as an idealized cell or as a convolved image as might have been acquired with a specific microscope. The former is suitable for use in cell simulations, while the latter is useful for testing analysis software with images for which the ground truth is known.
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Highlights Track: Databases and Ontologies
Presenting author: Susanna-Assunta Sansone , University of Oxford, United Kingdom
Monday, July 16: 3:00 p.m. - 3:25 p.m.Room: 104C

Additional authors:
Philippe Rocca-Serra, University of Oxford, United Kingdom
Eamonn Maguire, University of Oxford, United Kingdom
Dawn Field, NERC, United Kingdom
Chris Taylor, EMBL, United Kingdom
Oliver Hofmann, Harvard School of Public Health, United States
Hong Fang, ICF International Company, United States
Steffen Neumann, Leibniz Institute of Plant Biochemistry, Germany
Weida Tong, FDA, United States
Linda Amaral-Zettler, Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, International Census of Marine Microbes, United States
Kimberly Begley, Ontario Institute for Cancer Research, Canada
Tim Booth, NERC, United Kingdom
Lydie Bougueleret, SIB, Switzerland
Gully Burns, Information Sciences Institute, United States
Brad Chapman, Harvard School of Public Health, United States
Tim Clark, Harvard Medical School, United States
Lee-Ann Coleman, The British Library, United Kingdom
Jay Copeland, Harvard Medical School, United States
Sudeshna Das, Harvard Medical School, United States
Antoine de Daruvar, Université de Bordeaux, France
Paula de Matos, EMBL, United Kingdom
Ian Dix, AstraZeneca, United Kingdom
Scott Edmunds, GigaScience, China
Chris T. Evelo, The Netherlands Bioinformatics Centre, Netherlands
Mark J. Forster, Syngenta, United Kingdom
Pascale Gaudet, SIB, Switzerland
Jack Gilbert, Argonne National Laboratory, United States
Carole Goble, University of Manchester, United Kingdom
Julian L. Griffin, University of Cambridge, United Kingdom
Daniel Jacob, Université de Bordeaux, CBiB , France
Jos Kleinjans, Netherlands Toxicogenomics Centre, Netherlands
Lee Harland, ConnectedDiscovery Ltd, United Kingdom
Kenneth Haug, EMBL, United Kingdom
Henning Hermjakob, EMBL, United Kingdom
Shannan J. Ho Sui, Harvard School of Public Health, United States
Alain Laederach, University of North Carolina, United States
Shaoguang Liang, GigaScience, China
Stephen Marshall, The Novartis Institutes for BioMedical Research, United Kingdom
Annette McGrath, CSIRO, Australia
Emily M. Merrill, Massachusetts General Hospital, United States
Dorothy Reilly, The Novartis Institutes for BioMedical Research, United States
Magali Roux, University of Pierre and Marie Curie CNRS UMS 7606, France
Caroline E. Shamu, Harvard Medical School, United States
Catherine A. Shang, Bioplatforms Australia Ltd, Australia
Christoph Steinbeck Christoph, EMBL, United Kingdom
Anne Trefethen, University of Oxford, United Kingdom
Bryn Williams-Jones, ConnectedDiscovery Ltd, United Kingdom
Ioannis Xenarios, SIB, Switzerland
Katherine Wolstencroft, University of Manchester, United Kingdom

Area Session Chair: Hagit Shatkay

Presentation Overview:
The ISA commons (www.isacommons.org) is a growing exemplar ecosystem of data curation and sharing solutions built on a common metadata tracking framework, providing tools and resources to create and manage large, heterogeneous data sets in a coherent manner, and allowing users of (parts of) data sets to ‘connect the metadata dots’. We invite new communities interested in breaching the boundary of their own biodomain to join the growing ISA network to empower ever more scientists to take data management, biocuration and sharing into their own hands, using community standards while remaining blissfully unaware of the underlying complexities of the implementation of those standards.
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Highlights Track: Disease Models and Epidemiology
Presenting author: Pablo Tamayo , Broad Institute, United States
Tuesday, July 17 : 2:30 p.m. - 2:55 p.m.Room: Grand Ballroom

Additional authors:
Yoon-Jae Cho, Stanford University, United States
Aviad Tscherniak Tsherniak, Broad INstitute, United States
Marcel Kool, Amsterdam Medical Center, Netherlands
Scott Pomeroy, Children's Hospital, United States
Jill Mesirov, Broad Institute, United States

Area Session Chair: Serafim Batzoglou

Presentation Overview:
Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis.
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Presenting author: Manolis Kellis , MIT, United States
Tuesday, July 17 : 3:00 p.m. - 3:25 p.m.Room: Grand Ballroom

Additional authors:
Luke Ward, MIT, United States
29-mammals Consortium, Broad Institute, United States

Area Session Chair: Serafim Batzoglou

Presentation Overview:
The large number of single-nucleotide polymorphisms (SNP) from genome-wide association studies (GWAS) that implicate non-coding regions in human disease poses the important challenge of interpreting their molecular mechanisms of action, needed for drug targets and therapeutics. Comparison of many related genomes has emerged as a powerful lens for genome interpretation, which complements large-scale experimental datasets of gene and genome activity by providing information on selective pressures for functional nucleotides. We have used the comparative analysis of 29 eutherian genomes to provide a high-resolution map of selective constraint in the human genome, revealing 3 million novel elements, and used distinct evolutionary signatures and chromatin information to suggest their candidate functions. We have further automated their use for interpreting disease-associated regions, by exploiting the population-specific linkage disequilibrium (LD) structure from the 1000 Genomes Project, to facilitate development of mechanistic hypotheses of the impact of non-coding variants on clinical phenotypes and normal variation.
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Highlights Track: Evolution and Comparative Genomics
Presenting author: Erik Sonnhammer , Stockholm University, Sweden
Tuesday, July 17 : 3:00 p.m. - 3:25 p.m.Room: 104B

Additional authors:
Kristoffer Forslund, SBC, Stockholm University, Sweden

Area Session Chair: Jaques Reifman

Presentation Overview:
According to the “ortholog conjecture”, orthologous proteins are expected to retain function more often than other homologs. Several proxies for functional conservation have been used, such as GO annotations and tissue expression. We here test the ortholog conjecture using conservation of domain architecture as an alternative proxy for protein function.

We studied domain architecture conservation in orthologs and paralogs between human and 40 other species. The analysis shows that orthologs exhibit greater domain architecture conservation than paralogs, even when differences in average sequence divergence are compensated for, for homologs that have diverged beyond a certain threshold.

Our results support the hypothesis that function conservation between orthologs demands higher domain architecture conservation than other types of homologs, relative to primary sequence conservation. This supports the notion that orthologs are functionally more similar than other types of homologs at the same evolutionary distance.
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Presenting author: Michal Linial , The Hebrew University of Jerusalem, Israel
Tuesday, July 17 : 3:30 p.m. - 3:55 p.m.Room: 104B

Additional authors:
Nadav Rappoport, The Hebrew University of Jerusalem, Israel

Area Session Chair: Jaques Reifman

Presentation Overview:
Ample of studies focuses on the exchange of genetic material between viruses and cellular hosts. The common view claims that along the evolutionary history (bacteria to humans), viruses have shaped the host genomes. We will present evidence that, in addition to codon usage adaptation (Bahir et al. MSB 5:311), shaping viral proteomes is executed by ‘stealing and refinement’ of genetic material from the host. Tracing such events is challenging as the origin of the sequences is masked by viruses’ high mutation rate. We will present evidence for “stolen” genetic material from metazoan hosts to their viruses. For about 75% of the cross-taxa families, viral proteins are significantly shorter than their counterpart host proteins. We expose instances for active trimming of domain tails, and removal of internal domains by viruses. The inventory of viral stolen proteins provides insights on the overlooked intimacy of viruses and their multicellular hosts.
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Presenting author: Erez Levanon , Bar-Ilan University, Israel
Tuesday, July 17 : 2:30 p.m. - 2:55 p.m.Room: 104B

Additional authors:
Shai Carmi, Columbia University., United States
George Church, Harvard Medical School , United States

Area Session Chair: Jaques Reifman

Presentation Overview:
Genomic innovation is thought to be mediated by slow accumulation of uncorrelated mutations. Here, we show that mammalians utilized an antiviral mechanism to accelerate their genome evolution by large-scale, parallel editing of their retrotransposons. We found thousands of clusters of G-to-A mismatches between pairs of retrotransposon sequences, indicating massive editing of retrotransposons prior to their integration. Such clusters are the hallmark of the activity of APOBEC3, a potent antiretroviral protein family with cytidine deamination function. We found DNA editing to span many mammalian genomes and retrotransposon families, as well as human-specific elements. Since DNA editing simultaneously generates a large number of mutations, each affected element can begin its evolutionary trajectory from a unique starting point, thereby increasing the probability of developing a novel function.
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Highlights Track: Gene Regulation and Transcriptomics
Presenting author: Mukund Thattai , National Centre for Biological Sciences, India
Sunday, July 15: 10:45 a.m. - 11:10 a.m.Room: 104B

Additional authors:
Navneet Rai, National Centre for Biological Sciences, India
Rajat Anand, National Centre for Biological Sciences, India
Krishna Ramkumar, Indian Institute of Technology Bombay, India
Varun Sreenivasan, St. Xavier’s College, India
Sugat Dabholkar, National Centre for Biological Sciences, India
Kareenhalli Venkatesh, Indian Institute of Technology Bombay, India

Area Session Chair: Paul Horton

Presentation Overview:
Bacterial cells communicate with one another by exchanging chemical signals, which can be used to coordinate actions across a cell population. Such coordination, regulated by so-called quorum-sensing systems, works on the following principle: every cell secretes a specific signal; the more cells there are, the more signal is generated; when the population density crosses a critical threshold, cells respond by driving transcription at a specific promoter. In our experiments, we find that quorum-sensing feedback systems can generate a diverse array of response types; this diversity arises through the complex interaction of microscopic parameters with feedback topology. I will show how, treating the promoter as a black-box characterized only by its input/output response or ‘promoter logic’, we are able to qualitatively and quantitatively predict the entire range of experimentally observed responses: smooth activation; hysteretic behavior; and even synchronized oscillations. Promoter logic is thus a necessary and sufficient representation of microscopic biochemistry.
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Presenting author: Pavel Sumazin , Columbia, United States
Sunday, July 15: 11:15 a.m. - 11:40 a.m.Room: 104B

Additional authors:
Andrea Califano, Columbia, United States

Area Session Chair: Paul Horton

Presentation Overview:
By analyzing gene expression data in gliobastoma in combination with matched microRNA profiles, we have uncovered a post-transcriptional regulation layer of surprising magnitude, comprising hundreds of thousands of microRNA-mediated interactions. These include thousands of genes whose transcripts act as microRNA ‘sponges’ and hundreds of genes that act through alternative, non-sponge interactions. Biochemical analyses in cell lines confirmed that this network regulates established drivers of glioblastoma tumor initiation and subtype, including P53, PTEN, PDGFRA, RB1, VEGFA, STAT3, and RUNX1, suggesting that these interactions mediate crosstalk between canonical oncogenic pathways. RNA silencing of 13 microRNA-mediated PTEN regulators, whose locus deletions are predictive of PTEN expression variability, was sufficient to downregulate PTEN in a 3' UTR-dependent manner and to increase tumor-cell growth rates. Thus, this microRNA-mediated network provides a mechanistic, experimentally validated rationale for the loss of PTEN expression in a large number of glioma samples with an intact PTEN locus.
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Presenting author: Eduardo Eyras , Universitat Pompeu Fabra, Spain
Sunday, July 15: 2:30 p.m. - 2:55 p.m.Room: 104B

Additional authors:
Mireya Plass, Universitat Pompeu Fabra, Spain
Josep Vilardell, CSIC, Spain

Area Session Chair: Janet Kelso

Presentation Overview:
Splicing is generally regulated by protein factors binding the pre-mRNA. Yeast lacks many of the splicing factors present in metazoans; hence it is thought to have limited regulated splicing. We present experimental evidence that the structure adopted by the pre-mRNA can function as a regulator of 3’ splice site selection in yeast, bringing the selected site close to the branch-site (BS) and occluding the rest.

Based on these observations we built a computational classifier that explains most of the annotated 3’ss in yeast. Our model also predicts the usage of alternative 3’ss at low and/or high temperatures, some of which we validated experimentally. Our results are consistent with the presence of alternative 3’ss selection in yeast that is mediated by the pre-mRNA structure, which can be responsive to external cues, like temperature, and which is possibly related to the control of gene expression.
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Presenting author: Tamir Tuller , Tel-Aviv University, Israel
Sunday, July 15: 3:00 p.m. - 3:25 p.m.Room: 104B

Additional authors:
Hadas Zur, Tel Aviv University, Israel
Nir Gazit , Tel Aviv University, Israel
Marin Kupiec, Tel Aviv University, Israel
Eytan Ruppin , Tel Aviv University, Israel
Michal Ziv-Ukelson, Ben Gurion University, Israel
Isana Veksler-Lublinsky, Ben Gurion University, Israel

Area Session Chair: Janet Kelso

Presentation Overview:
Gene translation is a central process in all living organisms. Thus, attaining a better understanding of this complex process has ramifications to every biomedical discipline. In this talk, I will survey recent results related to this topic.
I will show that features of the transcript, such as its folding strength, the adaptation of its codons to the tRNA pool, and the charge of the amino acids encoded in it, contribute to translation efficiency in a causal and/or non-causal way. Specifically, highly expressed genes have stronger mRNA folding, possibly to prevent aggregation of mRNA molecules. In addition, each of these features contributes to: 1) The spatial distribution of ribosomes along transcripts; 2) Slowing down ribosomes at the beginning of the coding regions, presumably to reduce ribosomal traffic-jams and decrease the translation cost.
I will also demonstrate how these results can be integrated into a comprehensive computational predictive model of translation.
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Presenting author: Rory Stark , Cancer Research UK, United Kingdom
Monday, July 16: 10:45 a.m. - 11:10 a.m.Room: 104B

Additional authors:
Caryn Ross-Innes, Hutchison/MRC, United Kingdom
Teschendorff Andrew, University College London, United Kingdom
Holmes Kelly, Cancer Research UK, United Kingdom
Raza Ali, Cancer Research UK, United Kingdom
Mark Dunning, Cancer Research UK, United Kingdom
Gordon Brown, Cancer Research UK, United Kingdom
Ondrej Gojis, Charles University, cz
Ian Ellis, Nottingham University, United Kingdom
Andrew Green, Nottingham University, United Kingdom
Simak Ali, Imperial College London, United Kingdom
Suet-Feung Chin, Cancer Research UK, United Kingdom
Carlo Palmieri, Imperial College London, United Kingdom
Carlos Caldas, Cancer Research UK, United Kingdom
Jason Carroll, Cancer Research UK, United Kingdom

Area Session Chair: Carl Kingsford

Presentation Overview:
In this paper, which maps ERα binding via ChIP-seq in tumour tissue from twenty ER+ breast cancer patients, we develop a novel technique for quantitative differential analysis of protein/DNA binding events, identifying ERα sites significantly differentially bound between good prognosis patients vs. those with poor prognosis and metastases. Gene signatures that predict clinical outcome in ER+ disease, validated in publically available breast cancer gene expression datasets, are derived from these sites. These signatures are enriched for genes with relevant proximal cis-regulatory events. Statistical characterization of differentially bound ERα sites enables further downstream analysis, including identification of a differentially enriched motif for the transcription factor FoxA1. Focusing our analysis on differential binding in primary tumour material allows us to show distinct combinations of cis-regulatory elements linked with the different clinical outcomes. These techniques are applicable to other cancers (and indeed other diseases) where master transcription factor regulators are known.
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Presenting author: Ka Yee Yeung , University of Washington, United States
Monday, July 16: 11:15 a.m. - 11:40 a.m.Room: 104B

Additional authors:
Kenneth Dombek, University of Washington, United States
Kenneth Lo, University of Washington, United States
John Mittler, University of Washington, United States
Jun Zhu, Sage Bionetworks, United States
Eric Schadt, Pacific Biosciences, United States
Roger Bumgarner, University of Washington, United States
Adrian Raftery, University of Washington, United States

Area Session Chair: Carl Kingsford

Presentation Overview:
The goal of network inference is to generate testable hypotheses of gene-to-gene influences and subsequently design bench experiments to confirm network predictions. In [Yeung et al. 2011], we used both time-series and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generated time-series gene-expression data profiling 95 genotyped yeast segregants subjected to a drug perturbation. We developed a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We showed that our inferred network recovered existing and novel regulatory relationships, and discovered de novo transcription-factor binding sites. We generated independent microarray data on selected deletion mutants to prospectively test network predictions.
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Presenting author: Xiaotu Ma Ma , The University of Texas at Dallas, United States
Monday, July 16: 11:45 a.m. - 12:10 p.m.Room: 104B

Additional authors:
Ashwinikumar Kulkarni, The University of Texas at Dallas, United States
Zhihua Zhang, The University of Texas at Dallas, United States
Zhenyu Xuan, The University of Texas at Dallas, United States
Michael Zhang, The University of Texas at Dallas, United States

Area Session Chair: Carl Kingsford

Presentation Overview:
Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes.
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Presenting author: Ivan Ovcharenko , NIH, United States
Monday, July 16: 12:15 p.m. - 12:40 p.m.Room: 104B

Additional authors:
Leila Taher, NIH, United States
Andrew McCallion, JHU, United States
Marcelo Nobrega, University of Chicago, United States

Area Session Chair: Carl Kingsford

Presentation Overview:
Enhancers often diverge much faster than exonic sequence. The role of gene regulatory changes in adaptation of species is one of the factors leading to the accelerated rate of enhancer sequence divergence, and the plasticity of the underlying enhancer encoding is the other contributor. We developed a computational approach capable of using DNA sequence motifs within enhancers to identify their functional orthologs when their sequence diverged beyond recognition by the classical alignment methods. Experimental validation confirmed the enhancer activity of 88% of our functional ortholog predictions. Moreover, 71% of the tested predicted functional enhancer othrolog pairs directed largely identical patterns of expression in zebrafish embryos, confirming both the sensitivity and accuracy of our method. Our study argues that motif composition is often necessary to retain and sufficient to predict regulatory function in the absence of overt sequence conservation, revealing an entire class of functionally conserved, evolutionarily diverged regulatory elements.
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Presenting author: Jason Ernst , University of California Los Angelels, United States
Monday, July 16: 2:30 p.m. - 2:55 p.m.Room: 104B

Additional authors:
Pouya Kheradpour, Massachusetts Institute of Technology, United States
Tarjei Mikkelsen, Broad Institute, United States
Noam Shoresh, Broad Institute, United States
Lucas Ward, Broad Institute, United States
Charles Epstein, Broad Institute, United States
Xiaolan Zhang, Broad Institute, United States
Li Wang, Broad Institute, United States
Robyn Issner, Broad Institute, United States
Michael Coyne, Broad Institute, United States
Manching Ku, Massachusetts General Hospital, United States
Timothy Durham, Broad Institute, United States
Manolis Kellis, Massachusetts Institute of Technology, United States
Bradley Bernstein, Massachusetts General Hospital, United States

Area Session Chair: Reinhard Schneider

Presentation Overview:
Chromatin profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. The approach is especially well suited to the characterization of non-coding portions of the genome, which critically contribute to cellular phenotypes yet remain largely uncharted. Using maps of nine chromatin marks across nine cell types we systematically characterize regulatory elements, their cell-type specificities and their functional interactions. Focusing on cell-type-specific patterns of promoters and enhancers, we define multicell activity profiles for chromatin state, gene expression, regulatory motif enrichment and regulator expression. We then link enhancers to putative target genes, and predict the cell-type-specific activators and repressors that modulate them. The resulting annotations and regulatory predictions have implications for the interpretation of genome-wide association studies. Top-scoring disease SNPs are frequently positioned within enhancer elements specifically active in relevant cell types. Our study presents a general framework for deciphering cis-regulatory connections and their roles in disease.
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Highlights Track: Mass Spectrometry and Proteomics
Presenting author: Wilson Wen Bin Goh , Imperial College London, United Kingdom
Monday, July 16 : 3:00 p.m. - 3:25 p.m.Room: 104B

Area Session Chair: Reinhard Schneider

Presentation Overview:
Traditional proteomics analysis is plagued by the use of arbitrary thresholds resulting in large loss of information. We propose here a novel method utilizing all detected proteins. Its efficacy is demonstrated in a liver cancer proteomics screen. Utilizing biological and predicted complexes, a Proteomics Signa?ture Profile (PSP) for each patient was derived. Although consistency of individual proteins between patients is low, we found the reported proteins tend to hit clusters in a meaningful and informative manner. By extracting this information in the form of a Proteomics Signature Profile, we confirm that this information is conserved and can be used for (1) clustering of patient samples, (2) identification of significant clusters based on real biological complexes, and (3) overcoming consistency and coverage issues prevalent in proteomics data sets.
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Presenting author: Sebastian Böcker , Friedrich-Schiller-University Jena, Germany
Monday, July 16 : 3:30 p.m. - 3:55 p.m.Room: 104B

Additional authors:
Florian Rasche, Friedrich-Schiller-University Jena, Germany
Kerstin Scheubert, Friedrich-Schiller-University Jena, Germany
Franziska Hufsky, Friedrich-Schiller-University Jena, Germany
Thomas Zichner, European Molecular Biology Laboratory, Germany
Marco Kai, Max Planck Institute for Chemical Ecology, Germany

Area Session Chair: Reinhard Schneider

Presentation Overview:
The structural elucidation of organic compounds in complex biofluids and tissues remains a significant challenge. For mass spectrometry, the manual interpretation of tandem mass spectra is cumbersome and requires expert knowledge, as the fragmentation mechanisms of small molecules are not completely understood. Thus, the automated identification of compounds is generally limited to searching in spectral libraries.
We have developed a fully automated pipeline for the identification of truly unknown compounds. First, it annotates the spectra with fragmentation trees, and then compares these trees via tree aligment. This allows for the retrieval of similar compounds from a reference library, even if it contains spectra from a different instrument type. A decoy database strategy enables FDR calculation. In addition, clustering based on tree similarities agrees well with known compound classes. This allows for a basic identification of unknown metabolites in an high-throughput setup.
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Highlights Track: Population Genomics
Presenting author: Guenter Klambauer , Johannes Kepler University of Linz, Austria
Sunday, July 15: 2:30 p.m. - 2:55 p.m.Room: 104A

Additional authors:
Karin Schwarzbauer, Johannes Kepler University of Linz, Austria
Andreas Mayr, Johannes Kepler University of Linz, Austria
Djork-Arné Clevert, Johannes Kepler University of Linz, Austria
Andreas Mitterecker, Johannes Kepler University of Linz, Austria
Ulrich Bodenhofer, Johannes Kepler University of Linz, Austria
Sepp Hochreiter, Johannes Kepler University of Linz, Austria

Area Session Chair: Eran Halperin

Presentation Overview:
Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Technological or genomic variations in the depth of coverage lead to a high false discovery rate (FDR), even upon correction for GC content. We propose ‘Copy Number estimation by a Mixture Of PoissonS’ (cn.MOPS), a data processing pipeline for CNV detection in NGS data. In contrast to previous approaches, cn.MOPS incorporates modeling of depths of coverage across samples at each genomic position. Therefore, cn.MOPS is not affected by read count variations along chromosomes. Using a Bayesian approach, cn.MOPS decomposes variations in the depth of coverage across samples into integer copy numbers and noise noise by means of its mixture components and Poisson distributions, respectively. The noise estimate allows for reducing the FDR by filtering out detections having high noise, which is the reason for the superior performance.
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Presenting author: Marit Ackermann , Technical University Dresden, Germany
Sunday, July 15 : 3:00 p.m. - 3:25 p.m.Room: 104A

Additional authors:
Andreas Beyer, Technical University Dresden, Germany

Area Session Chair: Eran Halperin

Presentation Overview:
Epistatic interactions between genes are crucial for understanding the molecular mechanisms of complex diseases. While systematic testing of genetic interactions with an impact on physiological fitness is possible in simple model organisms, such screens have not been successful in mammals. Here, we propose a computational screening method that only requires genotype information of family trios for predicting epistasis. Based on a Chi-squared test approach, it detects the under-representation of allele pairs in a given population.
We tested our framework on a set of 2,000 heterozygous mice and found 168 imbalanced allele pairs, which is substantially more than expected by chance. We confirmed many of the interactions using independent data and found that interacting loci are enriched for developmental genes. The number of imbalanced allele pairs that we detected is surprisingly large and was not expected based on published evidence. This framework sets the stage for similar work in human trios.
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Highlights Track: Protein Interactions and Molecular Networks
Presenting author: Ohad Balaga , The Hebrew University of Jerusalem, Israel
Sunday, July 15: 2:30 p.m. - 2:55 p.m.Room: Grand Ballroom

Additional authors:
Guy Naamati, The Hebrew University of Jerusalem, Israel
Yitzhak Friedman, The Hebrew University of Jerusalem, Israel
Michal Linial, The Hebrew University of Jerusalem, Israel

Area Session Chair: Lenore Cowen

Presentation Overview:
In human, over 1000 microRNAs (miRNAs) regulate the expression of about half of the genes. This study addresses the potential of a coordinated action of miRNAs to manipulate hundreds of human pathways. Specifically, we analyzed the effectiveness of disrupting the topology of human pathway graphs through a regulation by miRNAs. We will present the combination of our concept of miRNA ‘working together’ (Friedman et al., Bioinformatics, 2010) with the pathways’ topology considerations. From a set of miRNA candidates, an exhaustive search for all possible doubles and triplets (coined miR-Duo, miR-Trios) that impact the integrity of a pathway is performed. We will discuss the surprising finding that 85% of all pathways are effectively disconnected by a remarkably small number of miRNAs sets. Significantly, the combination of the most effective miR-Trios is unique for each pathway. The impact of the selected miR-Duo/Trios on various diseases will be discussed.
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Presenting author: Ora Schueler-Furman , The Hebrew University, Israel
Sunday, July 15 : 3:00 p.m. - 3:25 p.m.Room: Grand Ballroom

Additional authors:
Nir London, Hebrew University, Israel
Hougland James, Syracuse University, United States
Carol Fierke, University of Michigan, United States
Yousef Abu-Kwaik, University of Louisville, United States
Tasneem Al-Qadan, University of Louisville, United States
Christopher Price, University of Louisville, United States

Area Session Chair: Lenore Cowen

Presentation Overview:
Prenylation is an important post-translational modification in which a lipid prenyl group is covalently attached to a protein, thereby changing its functional role. As an example, ras uses this mechanism to reach the membrane where it is active.

In this talk I will describe our recent work on the structure-based modeling of prenylation substrates based on Rosetta FlexPepDock, our peptide docking protocol. Based on structural models of the c-terminal peptide sequence of a protein bound to the enzyme farnesyltransferase, our protocol FlexPepBind identifies both known and novel farnesylation substrates. In vitro validation of the latter demonstrates the high accuracy of this approach: 26/29 peptides are indeed farnesylated. Application of our protocol to human as well as pathogenic genomes has identified many new and interesting targets.This work provides a link between the structure of a peptide-protein complex to its biological importance.
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Presenting author: Saliha Ece Acuner Ozbabacan , Koc University, Turkey
Sunday, July 15 : 10:45 a.m. - 11:10 a.m.Room: 104A

Additional authors:
Ozlem Keskin, Koc University, Turkey
Ruth Nussinov, NCI-Frederick, United States
Attila Gursoy, Koc University, Turkey

Area Session Chair: Yanay Ofran

Presentation Overview:
The structures of protein–protein complexes in the apoptosis signaling pathway are important as the structural pathway helps in understanding the mechanism of the regulation and information transfer, and in identifying targets for drug design. Here, we aim to predict the structures toward a more informative pathway than currently available. Based on the 3D structures of complexes in the target pathway and a protein–protein interaction modeling tool which allows accurate and proteome-scale applications, we modeled the structures of 29 interactions, 21 of which were previously unknown. Next, 27 interactions which were not listed in the KEGG apoptosis pathway were predicted and subsequently validated by the experimental data in the literature. Additional interactions are also predicted. The multi-partner hub proteins are analyzed and interactions that can and cannot co-exist are identified. Overall, our results enrich the understanding of the pathway with
interactions and provide structural details for the human apoptosis pathway.
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Presenting author: Haiyuan Yu , Cornell University, United States
Sunday, July 15: 11:15 a.m. - 11:40 a.m.Room: 104A

Additional authors:
Xiujuan Wang, Cornell University, United States
Xiaomu Wei, Weill Cornell Medical College, United States
Bram Thijssen, Maastricht University, Netherlands
Jishnu Das, Cornell University, United States
Steven Lipkin, Weill Cornell Medical College, United States

Area Session Chair: Yanay Ofran

Presentation Overview:
To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.
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Presenting author: Jesse Gillis , University of British Columbia, Canada
Sunday, July 15 : 11:45 a.m. - 12:10 p.m.Room: 104A

Area Session Chair: Yanay Ofran

Presentation Overview:
This paper concerns a central issue in the analysis of biological networks, which is how functional information can be discovered or exploited through their use. Our key finding is that almost all the available information on gene function is concentrated in a tiny part of networks. A striking demonstration is that a mouse gene network of 4.5 million edges can be reduced one with just 23 edges, while retaining key features commonly thought to involve widely distributed properties. At a basic level, the “guilt-by-association” approach that is practised by biologists all the time to study genes one-by-one does not scale up to networks, despite numerous claims to the contrary. Attempts to adjust or validate networks based on gene function are highly misleading, and attempts to predict gene function using computational means are based on deeply flawed assumptions. We offer concrete suggestions to help others avoid these pitfalls.
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Presenting author: Carl Kingsford , University of Maryland, College Park, United States
Tuesday, July 17: 10:45 a.m. - 11:10 a.m.Room: 104A

Additional authors:
Saket Navlakha, Carnegie Mellon University, United States
Rob Patro, University of Maryland, College Park, United States
Emre Sefer, University of Maryland, College Park, United States
Justin Malin, University of Maryland, College Park, United States
Guillaume Marçais, University of Maryland, College Park, United States

Area Session Chair: Alex Bateman

Presentation Overview:
I will present our recent work on reconstructing ancient biological networks. We have developed several methods for recovering interactions between molecules that were present in ancestral species, starting with only the present-day networks that we are able to measure. We have shown that, using these algorithms, ancestral interactions can be inferred with high accuracy. I will discuss several applications of these approaches, including predicting missing interactions between present-day viral proteins, identifying functionally related proteins, and modeling how protein complexes have rewired over time in yeast.
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Presenting author: Chad Myers , University of Minnesota, United States
Sunday, July 15: 12:15 p.m. - 12:40 p.m.Room: 104A

Additional authors:
Jeremy Bellay, University of Minnesota, United States
Gowtham Atluri, University of Minnesota, United States
Tina Sing, University of Toronto, Canada
Kiana Toufighi, Centre for Genomic Regulation, Spain
Michael Costanzo, University of Toronto, Canada
Philippe Souza Moraes Ribeiro, University of Minnesota, United States
Gaurav Pandey, Mount Sinai School of Medicine, United States
Joshua Baller, University of Minnesota, United States
Benjaim VanderSluis, University of Minnesota, United States
Magali Michaut, University of Toronto, Canada
Sangjo Han, University of Toronto, Canada
Philip Kim, University of Toronto, Canada
Grant Brown, University of Toronto, Canada
Brenda Andrews, University of Toronto, Canada
Charles Boone, University of Toronto, Canada
Vipin Kumar, University of Minnesota, United States

Area Session Chair: Yanay Ofran

Presentation Overview:
Genetic interactions provide a powerful perspective into biological processes that is fundamentally different from other high-throughput genome-wide studies. We developed a data mining approach based on association rule learning to exhaustively discover all statistically significant block structures within the yeast genetic interaction network, producing a complete modular decomposition of the network. This provides a first opportunity for a global, unbiased assessment of the structure of the genetic interaction network and the relationship between structure and individual gene function. The genetic interaction network is highly structured with over half of interactions appearing in modular structures, and genetic interactions contained within modules exhibit strikingly different functional properties relative to isolated interactions. In addition, gene module membership provides a specific and unbiased assessment of the prevalence of multi-functionality among genes. Our modular decomposition also provided a basis for testing the between-pathway model of negative genetic interactions and within-pathway model of positive genetic interactions.
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Presenting author: Yu Xia , Boston University, United States
Tuesday, July 17: 11:15 a.m. - 11:40 a.m.Room: 104A

Additional authors:
Eric Franzosa, Boston University, United States

Area Session Chair: Alex Bateman

Presentation Overview:
General properties of the largely antagonistic biomolecular interactions between pathogens and their hosts remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host. Recent host-pathogen systems biology efforts have generated global, but low-resolution, maps of host-pathogen protein-protein interaction networks. Here, we integrate three-dimensional homology models of protein complexes with interaction networks among human and viral proteins to construct the first human-virus structural interaction network. Subsequent analyses reveal significant biophysical, functional, and evolutionary differences between host-virus and within-host structural interaction networks. We find that viral proteins tend to bind to existing within-host interfaces. Compared to within-host protein-protein interfaces, host-virus protein-protein interfaces tend to be more transient, targeted by more host proteins, more regulatory in function, faster evolving, and rely less on sequence similarity to achieve interface mimicry. These results highlight the distinct consequences of cooperation versus antagonism in biological networks within and between species.
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Presenting author: T. Murali , Virginia Tech, United States
Tuesday, July 17: 11:45 a.m. - 12:10 p.m.Room: 104A

Additional authors:
Matthew Dyer, Applied Biosystems, United States
David Badger, Virginia Tech, United States
Brett Tyler, Virginia Tech, United States
Michael Katze, University of Washington, United States

Area Session Chair: Alex Bateman

Presentation Overview:
HIV Dependency Factors (HDFs) are human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three genome-wide RNAi experiments identified HDF sets with little overlap. We discuss how we combined these three datasets with a human PPI network to predict new HDFs, using an algorithm called SinkSource and four other algorithms published in the literature. A number of HDFs that we predicted are known to interact with HIV proteins. Many predicted HDF genes showed significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers of AIDS development.

We conclude with recent results on predicting dependency factors for multiple viruses, in an effort to discover human proteins that may serve as broad-spectrum drug targets for infectious diseases.
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Highlights Track: Protein Structure and Function
Presenting author: Lei Xie , The City University of New York, United States
Monday, July 16 : 11:15 a.m. - 11:40 a.m.Room: Grand Ballroom

Additional authors:
Li Xie, University of California, San Diego , United States
Philip Bourne, University of California, San Diego , United States
Thomas Evangelidis, Biomedical Research Foundation Academy of Athens, Greece

Area Session Chair: David Gifford

Presentation Overview:
The conventional approach to drug discovery of “one drug – one target – one disease” is insufficient, especially for complex diseases. This inadequacy is partially addressed by accepting the notion of polypharmacology – one drug is likely to bind to multiple targets with varying affinity. However, to identify proteome-wide multiple targets for a drug is a complex and challenging task. We have developed a structural systems biology approach to quantitatively predict potential off-targets for known drugs. This method is applied to identify human off-targets for Nelfinavir, an antiretroviral drug with anti-cancer behavior. We propose inhibition by Nelfinavir of multiple protein kinases. We suggest that broad-spectrum low affinity binding by a drug or drugs to multiple targets may lead to a collective effect important in treating complex diseases such as cancer.
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Presenting author: Mickey Kosloff , University of Haifa, Israel
Monday, July 16: 11:45 a.m. - 12:10 p.m.Room: Grand Ballroom

Additional authors:
Vadim Arshavsky, Duke University, United States
Amanda Travis, Duke University, United States
Dustin Bosch, University of North Carolina at Chapel Hill, United States
David Siderovski, University of North Carolina at Chapel Hill, United States

Area Session Chair: David Gifford

Presentation Overview:
Cellular signaling requires that particular protein-protein interactions be tailored to each signaling cascade with either broad or narrow specificity. Understanding the structural code for such selectivity is a major goal in signal transduction research, as well as in drug design. Yet, beyond single representative examples, little is known of how specificity is determined among large protein families, including those involved in signal transduction.

The talk will present a “bottom-up” approach to decipher interaction specificity, using G-protein signaling as a model system. This approach integrates experimental and structure-based energy calculations to map specificity determinants at the protein family level. The resulting residue-level maps are then used to redesign proteins with altered activities and specificities, offering new insights into G-protein signaling and paving the way for the rewiring of signaling networks at the cellular level.
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Presenting author: Karin Verspoor , National ICT Australia, Australia
Monday, July 16: 12:15 p.m. - 12:40 p.m.Room: Grand Ballroom

Additional authors:
Michael Wall, Los Alamos National Laboratory, United States
Judith Cohn, Los Alamos National Laboratory, United States
Komandur Ravikumar, Mayo Clinic, United States

Area Session Chair: David Gifford

Presentation Overview:
We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites in about 100,000 publicly available protein structures. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.
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Presenting author: Vered Kunik , Bar Ilan University, Israel
Monday, July 16: 2:30 p.m. - 2:55 p.m.Room: 202 B/C

Additional authors:
Yanay Ofran, Bar Ilan University, Israel
Bjoern Peters, La Jolla Institute for Allergy and Immunology, United States

Area Session Chair: Bonnie Berger

Presentation Overview:
Identification of the residues within an antibody (Ab) that recognize and bind the antigen (Ag), which is at the heart of immunological research, is typically done using computational tools for identifying the so called Complementarity Determining Regions (CDRs). We show that CDRs identification tools miss up to 22% of the residues that actually bind the Ag. We show that essentially all antigen binding residues are located within structural consensus regions between antibodies and that these regions could be identified from sequence. Moreover, we demonstrate that Ag binding residues that fall within Ab structural consensus regions and are not identified by the most commonly used CDR identification methods, have a substantial energetic contribution to Ag binding. Finally, we suggest a computational tool for the identification of Ag binding site from Ab sequence and we show that this tool identifies 94% of the residues that actually bind the Ag.
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Presenting author: Keren Lasker , Stanford University, United States
Monday, July 16: 3:00 p.m. - 3:25 p.m.Room: 202 B/C

Additional authors:
Friedrich Förster, Max-Planck-Institute of Biochemistry, Germany
Stefan Bohn, Max-Planck-Institute of Biochemistry, Germany
Thomas Walzthoeni, University of Zürich, Switzerland
Elizabeth Villa, Max-Planck-Institute of Biochemistry, Germany
Pia Unverdorben, Max-Planck-Institute of Biochemistry, Germany
Florian Beck, Max-Planck-Institute of Biochemistry, Germany
Ruedi Aebersold, University of Zürich, Switzerland
Andrej Sali, University of California San Francisco, United States
Wolfgang Baumeister, Max-Planck-Institute of Biochemistry, Germany

Area Session Chair: Bonnie Berger

Presentation Overview:
In eukaryotes, the ubiquitin–proteasome pathway regulates fundamental cellular processes. The 26S proteasome resides at the downstream end of the pathway and degrades defective proteins. While the structure of its 20S core particle (CP) has been determined by X-ray crystallography, the structure of the 19S regulatory particle (RP), which recruits substrates and translocates them to the CP for degradation, has remained elusive. We have revealed the entire structure of the RP and describe a completed molecular architecture of the 26S proteasome. By integrating data from cryo-electron microscopy, X-ray crystallography, residue-specific chemical cross-linking, and additional proteomics techniques, we were able to produce a more accurate and higher resolution structural model than any of the data sets alone can provide. In addition, we have identified previously unpublished protein- protein interactions. The modular structure of the proteasome provides insights into the sequence of events that occur prior to the degradation of ubiquitylated substrates.
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Highlights Track: Sequence Analysis
Presenting author: Nir Ben-Tal , Tel Aviv University, Israel
Monday, July 16: 2:30 p.m. - 2:55 p.m.Room: 104A

Area Session Chair: Burkhard Rost

Presentation Overview:
The constellation of molecular factors leading to the emergence of the human pandemic H1N1 (pH1N1) influenza A virus in 2009 is unclear. Using a computational approach, we identified molecular determinants that may discriminate this strain from other strains. Amino acid positions discriminating pH1N1 from seasonal human strains were located in or near known antigenic sites on the hemagglutinin (HA) protein, thus camouflaging pH1N1 from immune recognition. We also detected positions in HA differentiating classical swine viruses from pH1N1. These positions were mostly located around the receptor-binding pocket, possibly influencing binding affinity to the human cell. Such alterations may be liable in part for the virus’s efficient infection and adaptation to humans. Significantly, we showed that the substitutions R133AK and R149K, predicted to be pH1N1 characteristics, each altered virus binding to erythrocytes and conferred virulence to A/swine/NC/18161/02 in mice, reinforcing the computational findings reported here.
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Presenting author: Marcel Schulz , Carnegie Mellon University, United States
Monday, July 16: 3:00 p.m. - 3:25 p.m.Room: 104A

Additional authors:
Daniel Zerbino, University of California Santa Cruz, United States
Martin Vingron, Max Planck Institute for Molecular Genetics, Germany
Ewan Birney, European Bioinformatics Institute, United Kingdom

Area Session Chair: Burkhard Rost

Presentation Overview:
Next generation sequencing of RNAs (RNA-Seq) has revolutionized the field of transcriptomics for genetics and medical research. De novo transcriptome assembly has become a feasible alternative for transcriptome analysis of novel model organisms, as de novo genome assembly is still a time-consuming process. De novo transcriptome assembly has other important applications for example gene-fusion detection in cancer or detection of trans-splicing events.

This talk will introduce the Oases de novo transcriptome assembler that exploits the relationship between de Bruijn Graphs and Splicing graphs to accurately model alternative gene isoforms in RNA-Seq data. The dynamic range of expression levels, alternative splicing events and repetitive sequences make de novo transcriptome assembly a challenging task and we will show how to strike the balance to deal with these overlapping problems. Further, the talk will reveal new insights into the importance of RNA-Seq data preprocessing and its’ tremendous effect on assembly performance.
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Presenting author: Gunnar Ratsch , Memorial Sloan-Kettering Cancer Center, United States
Monday, July 16: 3:30 p.m. - 3:55 p.m.Room: 104A

Additional authors:
Xiangchao Gan, University of Oxford, United Kingdom
Oliver Stegle, Max Planck Institute for Intelligent Systems and Max Planck Institute for Developmental Biology, Germany
Jonas Behr, Friedrich Miescher Laboratory, Germany
Philipp Drewe, Friedrich Miescher Laboratory, Germany
Joshua G. Steffen, University of Utah, United States
Richard Clark, University of Utah, United States
Edward J. Osborne, University of Utah, United States
Sebastian Schultheiss, Friedrich Miescher Laboratory, Germany
Vipin T. Sreedharan, Friedrich Miescher Laboratory, Germany
Andre Kahles, Friedrich Miescher Laboratory, Germany
Regina Bohnert, Friedrich Miescher Laboratory, Germany
Geraldine Jean, Friedrich Miescher Laboratory, Germany
Katie L. Hildebrand, Kansas State University, United States
Christopher Toomajian, Kansas State University, United States
Rune Lyngsoe, University of Oxford, United Kingdom
Paul Derwent, European Bioinformatics Institute, United Kingdom
Paul Kersey, European Bioinformatics Institute, United Kingdom
Eric Belfield, University of Oxford, United Kingdom
Nicholas Harberd, University of Oxford, United Kingdom
Eric Kemen, The Sainsbury Laboratory, United Kingdom
Paula X. Kover, University of Bath, United Kingdom

Area Session Chair: Burkhard Rost

Presentation Overview:
Genetic differences between Arabidopsis thaliana accessions underlie the plant's extensive phenotypic variation, and until now these have been interpreted largely in the context of the annotated reference accession Col-0. Here we report the sequencing, assembly and annotation of the genomes of 18 natural A. thaliana accessions, and their transcriptomes. When assessed on the basis of the reference annotation, one-third of protein-coding genes are predicted to be disrupted in at least one accession. However, re-annotation of each genome revealed that alternative gene models often restore coding potential. Gene expression in seedlings differed for nearly half of expressed genes and was frequently associated with cis variants within 5 kilobases, as were intron retention alternative splicing events. Sequence and expression variation is most pronounced in genes that respond to the biotic environment. Our data further promote evolutionary and functional studies in A. thaliana, especially the MAGIC genetic reference population descended from these accessions.
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Highlights Track: other
Presenting author: Davide Bau , National Center for Genomic Analysis, Spain
Monday, July 16: 3:30 p.m. - 3:55 p.m.Room: 104C

Additional authors:
Mark Umbarger, Harvard Medical School, United States
Esteban Toro, School of Medicine, United States
Matthew Wright, Harvard Medical School, United States
Gregory Porreca, Harvard Medical School, United States
Sun-Hae Hong, School of Medicine, United States
Michael Fero, School of Medicine, United States
Lihua Zhu, Program in Gene Function and Expression, United States
Marc Marti-Renom, National Center for Genomic Analysis , Spain
Harley McAdams, School of Medicine, United States
Lucy Shapiro, School of Medicine, United States
Job Dekker, University of Massachusetts Medical School, United States
George Church, Harvard Medical School, United States

Area Session Chair: Hagit Shatkay

Presentation Overview:
We have determined the three-dimensional (3D) architecture of the Caulobacter crescentus genome by combining genome-wide chromatin interaction detection, live-cell imaging, and computational modeling. Using chromosome conformation capture carbon copy (5C), we derive around 13 kb resolution 3D models of the Caulobacter genome. The resulting models illustrate that the genome is ellipsoidal with periodically arranged arms. The parS sites, a pair of short contiguous sequence elements known to be involved in chromosome segregation, are positioned at one pole, where they anchor the chromosome to the cell and contribute to the formation of a compact chromatin conformation. Repositioning these elements resulted in rotations of the chromosome that changed the subcellular positions of most genes. Such rotations did not lead to large-scale changes in gene expression, indicating that genome folding does not strongly affect gene regulation. Collectively, our data suggest that genome folding is globally dictated by the parS sites and chromosome segregation.
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