Highlights Track Presentations

All Highlights and Proceedings Track presentations are presented by scientific area part of the combined Paper Presentation schedule.


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


Highlights Track: Applied Bioinformatics
Presenting author: Carlo Vittorio Cannistraci, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Sunday, July 21 Room: Hall 7

Additional authors:
Timothy Ravasi, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Enrico Ammirati, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Italy

Area Session Chair: Predrag Radivojac

Presentation Overview:
Inflammation is likely involved in ST-elevation acute myocardial infarction (STEMI), and patients with STEMI can present with high levels of circulating interleukin-6 (IL6) at the onset of symptoms. We used machine learning techniques to identify characteristic inflammatory cytokine patterns in the blood of emergency-room patients with STEMI, and observed two functional modules characterizing the reciprocal behaviours of the cytokines in patients with high IL6 levels. Next, exploiting reverse engineering techniques, we inferred which cytokines were crucial inside the respective modules. Combining them together with IL6 in a unique formula yielded a risk-index – a kind of composed-biomarker – that outperformed any single cytokine and classical prognostic factors in the prediction of cardiac dysfunction at discharge and death at six months.
Our methodology was considered a translational research innovation for the definition of composed-inflammatory-markers in cardiology, while our findings have potential implications for risk-oriented patient stratification and design of immune-modulating therapies.
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Presenting author: Richard Lathrop, University of California, Irvine, United States
Sunday, July 21 Room: Hall 14.2

Additional authors:
Christopher Wassman, Google Inc., United States
Roberta Baronio, University of California, Irvine, United States
Özlem Demir, University of California, San Diego, United States
Brad Wallentine, University of California, Irvine, United States
Chiung-Kuang Chen, University of California, Irvine, United States
Linda Hall, University of California, Irvine, United States
Faezeh Salehi, University of California, Irvine, United States
Da-Wei Lin, University of California, Irvine, United States
Benjamin Chung, University of California, Irvine, United States
Wesley Hatfield, University of California, Irvine, United States
Richard Chamberlin, University of California, Irvine, United States
Hartmut Luecke, University of California, Irvine, United States
Peter Kaiser, University of California, Irvine, United States
Rommie Amaro, University of California, San Diego, United States

Area Session Chair: Russell Schwartz

Presentation Overview:
The tumour suppressor p53 is the most frequently mutated gene in human cancer. Reactivation of mutant p53 by small molecules is an exciting potential cancer therapy. Although several compounds restore wild-type function to mutant p53, their binding sites and mechanisms of action are elusive. Here computational methods identify a transiently open binding pocket between loop L1 and sheet S3 of the p53 core domain. Mutation of residue Cys124, located at the centre of the pocket, abolishes p53 reactivation of mutant R175H by PRIMA-1, a known reactivation compound. Ensemble-based virtual screening against this newly revealed pocket selects stictic acid as a potential p53 reactivation compound. In human osteosarcoma cells, stictic acid exhibits dose-dependent reactivation of p21 expression for mutant R175H more strongly than does PRIMA-1. These results indicate the L1/S3 pocket as a target for pharmaceutical reactivation of p53 mutants.
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Presenting author: Chen-Hsiang Yeang, Academia Sinica, Taiwan
Sunday, July 21 Room: Hall 7

Additional authors:
Robert Beckman, University of California, San Francisco, United States
Gunter Schemmann, World Water and Solar Technologies, United States

Area Session Chair: Predrag Radivojac

Presentation Overview:
Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity within a single individual’s cancer. We developed a mathematical model of personalized cancer therapy incorporating genetic evolutionary dynamics and single-cell heterogeneity, and examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable “patients” demonstrate that augmented nonstandard personalized medicine strategies may lead to superior outcomes compared with the current personalized medicine approach. Current personalized medicine matches generally focuses on the average, static, and current properties of the sample. In contrast, nonstandard strategies also consider minor subclones, dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.
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Presenting author: Livnat Jerby Arnon, Tel Aviv University, Israel
Sunday, July 21 Room: Hall 14.2

Additional authors:
Lior Wolf, Tel Aviv University, Israel
Carsten Denkert, Charité Hospital, Germany
Gideon Y Stein, Beilinson Hospital, Rabin Medical Center, Israel
Mika Hilvo, VTT Technical Research Centre of Finland, Finland
Matej Oresic, VTT Technical Research Centre of Finland, Finland
Tamar Geiger, Tel Aviv University, Israel
Eytan Ruppin, Tel Aviv University, Israel

Area Session Chair: Russell Schwartz

Presentation Overview:
The importance of metabolic reprogramming in cancer is being increasingly recognized. However, whole metabolic flux measurements in cancer are still scarce. Hence, we developed a novel Metabolic Phenotypic Analysis (MPA) method that profiles the metabolic phenotype of a tumor based on its gene or protein expression. We applied MPA to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of cell lines and clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming other metabolic modeling methods. MPA revealed that the tumor proliferation decreases as it evolves metastatic capability. We experimentally validated this "go or grow" dichotomy in-vitro, and linked the proliferation decrease to oxidative stress. Finally, we found fundamental metabolic differences between estrogen receptor (ER)+ and ER- tumors. These findings provide new insights into core metabolic aberrations in breast cancer.
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Presenting author: Hector Corrada Bravo, University of Maryland, United States
Sunday, July 21 Room: Hall 7

Area Session Chair: Ivo Hofacker

Presentation Overview:
Gene expression anti-profiles are a new computational approach for developing cancer genomic signatures that specifically take advantage of gene expression heterogeneity. This presentation will describe the biological basis for this method derived from experimental findings suggesting that stochastic across-sample hyper-variability in the expression of specific genes is a stable and general property of cancer. Application of this methodology in screening patients for colon cancer based on expression measurements obtained from peripheral blood samples will be presented. We will also present results from development of a universal cancer anti-profile that accurately distinguishes cancer from normal regardless of tissue type. This method uses single-chip normalization and quality assessment methods so no further retraining of signatures would be required before their application in clinical settings. These results suggest that anti-profiles may be used to develop inexpensive and non-invasive universal cancer screening tests.
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Presenting author: Yves Lussier, The University of Illinois, United States
Monday, July 22 Room: Hall 14.2

Additional authors:
Xinan Yang, The University of Chicago, United States
Kelly Regan, Ohio State University, United States
Yong Huang, The University of Chicago, United States
Jianrong Li, The University of Illinois at Chicago, United States
Ezra Cohen, The University of Chicago, United States
Tanguy Zeiwert, The University of Chicago, United States

Area Session Chair: Serafim Batzoglou

Presentation Overview:
Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic interpretation of expression arrays remains an unmet challenge. We developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional-Analysis-of-Individual-Microarray-Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. In oral squamous cell carcinoma samples, the overlap of “Oncogenic Mechanisms of OSCC” (deregulated FAIME-derived scores of pathways and biological functions) accurately discriminate clinical samples in two additional datasets (n=35;91, F-accuracy=100%;97%, p<0.001), and predicts patients’ survival in two studies (p=0.0018;p=0.032). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes(e.g. survival-time).
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Presenting author: Andrew Smith, University of Southern California, United States
Monday, July 22 Room: Hall 4/5

Area Session Chair: Reinhard Schneider

Presentation Overview:
Predicting the molecular complexity of a genomic sequencing library has emerged as a critical but difficult problem in modern applications of genome sequencing. Available methods to determine either how deeply to sequence, or predict the benefits of additional sequencing, are almost completely lacking. We introduce an empirical Bayesian method to implicitly model any source of bias and accurately characterize the molecular complexity of a DNA sample or library in almost any sequencing application.
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Presenting author: Stefan Kramer, Johannes Gutenberg University Mainz, Germany
Tuesday, July 23 Room: Hall 7

Additional authors:
Andreas Karwath, Johannes Gutenberg University Mainz, Germany
Madeleine Seeland, TU München, Germany
Martin Gütlein, University of Freiburg, Germany

Area Session Chair: Thomas Lengauer

Presentation Overview:
It is generally agreed that a better understanding of chemical space and its bioactive compounds requires a better set of tools for the visualization and the mining of structures and associated activities. In the talk, I will present some progress towards this goal. In the first part, I will present the visualization tool CheS-Mapper (Chemical Space Mapping and Visualization in 3D), which arranges sets of chemical structures in 3D space, such that spatially close structures share more common properties than remote ones. In the second part of the talk, I will present new methods for predicting the bioactivities of compounds. These methods build upon a recently developed clustering scheme that clusters chemical structures by common "scaffolds", i.e., the existence of one large substructure shared by all cluster elements. With the help of such a structural clustering, prediction performance can be improved substantially, in particular on heterogeneous sets of structures.
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Presenting author: Philippe Sanseau, GlaxoSmithKline, United Kingdom
Tuesday, July 23 Room: ICC Lounge 81

Additional authors:
Mark Hurle, GlaxoSmithKline, United States
Brent Richards, McGill University, Canada
Lon Cardon, GlaxoSmithKline, United States
Pankaj Agarwal, GlaxoSmithKline, United States

Area Session Chair: Donna Slonim

Presentation Overview:
Systematic drug repositioning is perhaps one the best ways for computational biology to show clear translational value in the pharmaceutical and biotech industry. Bionformatics methods that use genome-wide association studies (GWAS), side effects and connectivity map data are proving to have value. We built a computational pipeline to examine the relationship between the drug disease indications of drugs and genetics findings such as GWAS traits. When the drug indication was different from the GWAS disease trait we hypothesized that the drug could potentially be repositioned. We identified almost 100 GWAS genes with at least one associated drug that suggest potential drug repositioning opportunities. Further investigations provided additional evidence for some of these opportunities. We will also show some recent developments in connectivity map and side effect methods to reposition rapidly drugs and ultimately benefit the patients.
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Highlights Track: Bioimaging & Data Visualization
Presenting author: Manuel Corpas, The Genome Analysis Centre, United Kingdom
Sunday, July 21 Room: Hall 7

Additional authors:
John Gómez, EBI, United Kingdom
Leyla García, EBI, United Kingdom
Gustavo Salazar, University of Cape Town, South Africa
Jose Villaveces, Max Planck Institute, Germany
Swanand Gore, EBI, United Kingdom
Alexander García, Florida State University, United States
Maria Martín, EBI, United Kingdom
Guillaume Launay, Lyon1 University, France
Rafael Alcántara, EBI, United Kingdom
Noemi Del Toro Ayllón, EBI, United Kingdom
Marine Dumousseau, EBI, United Kingdom
Sandra Orchard, EBI, United Kingdom
Sameer Velankar, EBI, United Kingdom
Henning Hermjakob , EBI, United Kingdom
Chenggong Zong, UCLA, United States
Peipei Ping, UCLA, United States
Rafael Jiménez, EBI, United Kingdom

Area Session Chair: Ivo Hofacker

Presentation Overview:
This presentation first sets the scene for the problem: dynamic web visualization of bioinformatics, which depends heavily on JavaScript, has no coordination of efforts to date. Available applications in JavaScript are difficult to discover, develop, test, maintain, use, customize, extend or combine. BioJS provides a common specification to document, develop and register JavaScript graphical components in bioinformatics. Next, I will briefly talk about how components are developed to comply with our purposely-defined implementation guidelines. The rest of the talk is mostly taken by a practical demonstration of representative functionalities already available in the BioJS registry. Examples include a) the Sequence component to visualize proteins in fasta format in a variety of ways, b) the GeneExpressionSummary that links genes to phenotypes, c) the ChEBICompound and d) the InteractionTable. To conclude, I briefly show the portal for the project, how to contribute to this effort and who is involved.
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Presenting author: Nils Gehlenborg, Harvard Medical School, United States
Tuesday, July 23 Room: Hall 7

Additional authors:
Alexander Lex, Harvard University, United States
Marc Streit, Johannes Kepler University Linz, Austria
Hans-Joerg Schulz, University of Rostock, Germany
Christian Partl, Graz University of Technology, Austria
Dieter Schmalstieg, Graz University of Technology, Austria
Peter Park, Harvard Medical School, United States

Area Session Chair: Thomas Lengauer

Presentation Overview:
This talk will introduce the promises and challenges of identifying and characterizing tumor subtypes in cancer genomics data sets from patient cohorts with hundreds of patients and how our visual exploration system Caleydo StratomeX (http://stratomex.caleydo.org) supports these processes. Heterogeneous data sets including multiple genomic (mRNA, miRNA, RPPA, copy number, gene mutations) and clinical data types can be loaded into the software to efficiently generate and confirm hypotheses about tumor subtypes and their functional and clinical effects.

In order to help analysts to identify promising candidate subtypes, StratomeX has been extended with computational methods to rank stratifications and identify stratifications that provide corroborating evidence for candidate subtypes. This previously unpublished feature as well as a new interactive website with large heterogeneous data sets from The Cancer Genome Atlas (TCGA) will be presented, too.

The talk will demonstrate the utility of StratomeX through a comprehensive case study from TCGA.
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Presenting author: Nikolaus Schultz, Memorial Sloan-Kettering Cancer Center, United States
Tuesday, July 23 Room: Hall 7

Additional authors:
Jianjiong Gao, Memorial Sloan-Kettering Cancer Center, United States
B. Arman Aksoy, Memorial Sloan-Kettering Cancer Center, United States
Benjamin Gross, Memorial Sloan-Kettering Cancer Center, United States
Gideon Dresdner, Memorial Sloan-Kettering Cancer Center, United States
S. Onur Sumer, Memorial Sloan-Kettering Cancer Center, United States
Ethan Cerami, Memorial Sloan-Kettering Cancer Center, United States
Anders Jacobsen, Memorial Sloan-Kettering Cancer Center, United States
Ugur Dogrusoz, Bilkent University, Turkey
Erik Larsson, University of Gothenburg, Sweden
Chris Sander, Memorial Sloan-Kettering Cancer Center, United States

Area Session Chair: Thomas Lengauer

Presentation Overview:
The cBio Portal for Cancer Genomics (cbioportal.org) provides an integrated and easy to use web resource for exploring, visualizing and analyzing multidimensional cancer genomics data. The portal reduces massive molecular profiling data from cancer tissues and cell lines to a readily understandable form as genetic, epigenetic, gene expression and proteomic events. The combination of a convenient query interface and customized data storage enables researchers to interactively explore genetic alterations across samples, genes and pathways and to link these to clinical outcomes, when available. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, and patient-centric queries. With its simple, yet powerful and flexible, interface and software programmatic access, the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries.
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Highlights Track: Databases & Ontologies
Presenting author: Junwen Wang, The University of Hong Kong, China
Tuesday, July 23 Room: Hall 4/5

Additional authors:
Feng Xu, The University of Hong Kong, China
Mulin Li, The University of Hong Kong, China
Weixin Wang, The University of Hong Kong, China
Pak Sham, The University of Hong Kong, China
Panwen Wang, The University of Hong Kong, China

Area Session Chair: Reinhard Schneider

Presentation Overview:
In this talk, I will first introduce a fast and accurate genetic variants detection (FaSD) program we recently developed for NGS data [1]. We assessed this program and compared its performance with several state-of-the-art programs on normal and cancer NGS data. We found that FaSD is a fast and highly accurate SNP detection method, particularly when the sequence depth is low.

Next, I will also introduce a GWASdb database we manually curated to catalog the GVs discovered by GWAS and WGS [2]. In addition, we developed a GWASrap tool that can re-prioritize genetic variants by combining the GWAS statistical value and variant prioritization score based on the additive effect principle [3]. Our evaluations demonstrated that this prioritization method is very effective in selecting disease susceptibility regions.

In summary, our algorithm, database and tools will greatly facilitate NGS studies and benefit scientific community in general.
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Highlights Track: Disease Models & Epidemiology
Presenting author: Wenzhong Xiao, Massachusetts General Hospital/Harvard Medical School and Stanford University, United States
Sunday, July 21 Room: Hall 7

Area Session Chair: Predrag Radivojac

Presentation Overview:
A cornerstone of modern biomedical research is the use of mouse models to explore basic disease mechanisms, evaluate new therapeutic approaches, and make decisions to carry new drug candidates forward into clinical trials. However, few of these human trials have shown success. Here we systematically compared the genomic response from publically available datasets of patients of different acute inflammatory diseases and corresponding murine models, and show that, although inflammation from different etiologies result in highly similar genomic responses in humans, the responses in mouse models correlate poorly with the human disease and also one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts. In addition to improvements in the current animal model systems, our study supports higher priority for translational research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
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Presenting author: Timothy Tickle, Harvard School of Public Health, United States
Tuesday, July 23 Room: Hall 7

Additional authors:
Xochitl Morgan, Harvard School of Public Health, United States
Harry Sokol, University of Paris, France
Dirk Gevers, Broad Institute, United States
Kathryn Devaney, Massachusetts General Hospital, United States
Doyle Ward, Broad Institute, United States
Joshua Reyes, Harvard School of Public Health, United States
Samir Shah, Brown University, United States
Neal LeLeiko, Brown University, United States
Scott Snapper, Children's Hospital and Brigham and Women's Hospital, United States
Athos Bousvaros, Children's Hospital and Brigham and Women's Hospital, United States
Joshua Korzenik, Children's Hospital and Brigham and Women's Hospital, United States
Bruce Sands, Mount Sinai School of Medicine, United States
Ramnik Xavier, Massachusetts General Hospital, United States
Curtis Huttenhower, Harvard School of Public Health, United States

Area Session Chair: Alfonso Valencia

Presentation Overview:
The inflammatory bowel diseases have been consistently linked to dysbiosis in the gut microbiota. This microbial dysfunction has not been fully characterized, however, due to the lack of methods assessing community functional activity and statistically associating it with disease. In this study, "virtual" metagenomes were inferred using 16S rRNA gene sequencing of 231 biopsies and stool samples. This incorporated analysis of 1,119 microbial genomes and was validated by shotgun metagenomics . A multivariate approach linking microbiome shifts to disease, treatment, or environment recovered dysbioses in ~2% of microbial clades, including depletion of Clades IV and XIVa Clostridia and enrichment of Enterobacteriaceae. However, microbial functional activity was more consistently disrupted in disease, with 12% of pathways associated with IBD. These included decreases in short-chain fatty acid production, oxidative stress, and shifts from amino acid biosynthesis towards transport. These results provide initial methods for assessing biomolecular functions corresponding to changes in microbial community ecology.
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Highlights Track: Evolution & Comparative Genomics
Presenting author: Milana Frenkel-Morgenstern, Spanish National Cancer Research Centre (CNIO), Spain
Monday, July 22 Room: Hall 7

Additional authors:
Alfonso Valencia, Spanish National Cancer Research Centre (CNIO), Spain

Area Session Chair: Alex Bateman

Presentation Overview:
Chimeric RNAs of two or more genes are distinct from conventional alternatively spliced isoforms, because they result from the trans-splicing of pre-mRNAs or gene fusion following translocations. Only a limited number of chimeric transcripts and their associated proteins have been characterized, mostly result from chromosomal translocations and are associated with cancers. Therefore, it is important to extend these observations so as to catalog the chimeric transcripts expressed in different types of cancers, and to study the potential functions of their corresponding chimeric proteins, including the alterations they produce in protein-protein interaction networks. Indeed, we found already evidence that chimeric transcripts are translated into functional chimeric proteins and they can change cellular localization of parental proteins and can be identified in cancer patients using the specific and unique peptides. Finally, we collected the chimeric transcripts of human, mouse and fly in the ChiTaRS database to study the evolutionary conservation of chimeras.
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Presenting author: David Juan, Spanish National Cancer Research Centre, Spain
Monday, July 22 Room: ICC Lounge 81

Additional authors:
Florencio Pazos, Spanish National Centre for Biotechnology, Spain
Alfonso Valencia, Spanish National Cancer Research Centre, Spain

Area Session Chair: Burkhard Rost

Presentation Overview:
Co‐evolution is an essential component of evolution that contributes to maintain the structure of ecological and molecular networks while allowing species, proteins and genes to change and adapt over time. A wide range of co‐evolution‐inspired computational methods has been designed for: protein modeling, detection of binding sites, deciphering protein mechanisms of action, prediction of protein–protein interaction partners and reconstruction of protein complexes and interaction networks. Interestingly, recent important breakthroughs in the field have resulted in a remarkable improved capacity to predict interactions between proteins, and contacts between different protein residues. While co‐evolution‐based approaches have been developed independently over the last several decades, we propose that unification under a common framework would be a major step forward in the understanding of the molecular basis of co‐evolution.
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Presenting author: Michal Linial, The Hebrew University of Jerusalem, Israel
Monday, July 22 Room: ICC Lounge 81

Additional authors:
Isaak Tirosh, The Hebrew University of Jerusalem, Israel
Manor Askenazi, The Hebrew University of Jerusalem, Israel
Itai Linial, The Hebrew University of Jerusalem, Israel

Area Session Chair: Burkhard Rost

Presentation Overview:
The publication of Tirosh et al (2012) deals with a neglected niche in functional genomics. The main finding is the identification of short active sequences that failed detection via classical alignment-based approaches. This research lies in the interface of computational biology and automatic functional annotation scheme.
Cnidaria is a rich phylum that includes thousands of marine species. In this study, we focused on Nematostella vectensis and Hydra magnipapillata genomes. We present a method for ranking toxin-like candidates. Toxin-like functions were revealed using ClanTox. Among 83,000 proteins from Cnidaria, we found 170 candidates that fulfill the properties of toxin-like-proteins. Remarkably, only 11% of the predicted toxin-like proteins were previously classified as toxins. Our prediction methodology inferred functions for protease inhibitors, membrane pore formation, ion channel blockers and metal binding proteins. We conclude that the evolutionary expansion of toxin-like proteins in Cnidaria contributes to their fitness in the complex environment of the aquatic ecosystem.
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Presenting author: Yuval Tabach, Massachusetts General Hospital/ Harvard Medical School, United States
Monday, July 22 Room: ICC Lounge 81

Area Session Chair: Burkhard Rost

Presentation Overview:
Small RNAs such as microRNAs and small interfering RNAs (siRNAs) require protein cofactors to promote their biogenesis and mediate their silencing functions. Even though small RNA pathways are widely distributed among animal, plant, fungal, and protist phyla, these pathways diverge or are lost in particular taxonomic clades. We used phylogenetic conservation patterns to identify new small RNA cofactor genes. We compared 86 divergent eukaryotic genome sequences to discern the sets of genes that show similar phylogenetic profiles with known small RNA cofactor genes. The top predictions from this phylogenetic screen were tested for defects in RNA interference and a large fraction of the candidate genes showed defects as strong as validated small RNA cofactor genes, revealing new components in the pathway. RNA splicing components were the most enriched class of new small RNA cofactors identified, suggesting a deep connection between the mechanism of RNA splicing and small RNA-mediated gene silencing.
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Highlights Track: Gene Regulation & Transcriptomics
Presenting author: Christopher Ng, Massachusetts Institute of Technology, United States
Sunday, July 21 Room: Hall 14.2

Additional authors:
Ferah Yildirim, Massachusetts Institute of Technology, United States
Yoon Yap, Massachusetts Institute of Technology, United States
Simona Dalin, Massachusetts Institute of Technology, United States
Bryan Matthews, Massachusetts Institute of Technology, United States
Patricio Velez, Massachusetts Institute of Technology, United States
Adam Labadorf, Massachusetts Institute of Technology, United States
Ernest Fraenkel, Massachusetts Institute of Technology, United States
David Housman, Massachusetts Institute of Technology, United States

Area Session Chair: Russell Schwartz

Presentation Overview:
With technological advances, it is becoming increasingly clear that DNA methylation has a role in wide range of biological processes, including neuronal activity, learning, and memory. In this paper, we explored the hypothesis that DNA methylation is altered in Huntington’s disease and used reduced representation bisulfite sequencing (RRBS) to map sites of DNA methylation in cells carrying either wild-type or mutant huntingtin (HTT). We found that a large fraction of the genes that change in expression in the presence of mutant HTT demonstrate significant changes in DNA methylation. Regions with low CpG content, which have previously been shown to undergo methylation changes in response to neuronal activity, were disproportionately affected. Using motif analysis, we identified transcriptional regulators associated with DNA methylation changes, and we confirmed these hypotheses using genome-wide chromatin immunoprecipitation sequencing (ChIP-Seq). Our findings suggest new mechanisms for the effects of polyglutamine-expanded HTT on DNA methylation and transcriptional dysregulation.
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Presenting author: Caroline Friedel, Ludwig-Maximilians-Universität München, Germany
Sunday, July 21 Room: Hall 7

Additional authors:
Lukas Windhager, Ludwig-Maximilians-Universität München, Germany
Thomas Bonfert, Ludwig-Maximilians-Universität München, Germany
Kaspar Burger, Helmholtz-Zentrum München, Germany
Zsolt Ruzsics, Ludwig-Maximilians-Universität München, Germany
Stefan Krebs, Ludwig-Maximilians-Universität München, Germany
Stefanie Kaufmann, Ludwig-Maximilians-Universität München, Germany
Georg Malterer, Ludwig-Maximilians-Universität München, Germany
Anne L’Hernault, University of Cambridge, United Kingdom
Markus Schilhabel, Christian-Albrechts-Universität Kiel, Germany
Stefan Schreiber, Christian-Albrechts-Universität Kiel, Germany
Philip Rosenstiel, Christian-Albrechts-Universität Kiel, Germany
Ralf Zimmer, Ludwig-Maximilians-Universität München, Germany
Dirk Eick, Helmholtz-Zentrum München, Germany
Lars Dölken, University of Cambridge, United Kingdom

Area Session Chair: Ivo Hofacker

Presentation Overview:
Metabolic tagging of newly transcribed RNA by 4-thiouridine (4sU) can reveal the relative contributions of RNA synthesis and decay rates. Recently, we showed that ultra-short 4sU-tagging combined with RNA-seq determines global RNA processing kinetics at nucleotide resolution. This allowed identification of classes of rapidly and slowly spliced/degraded introns characterized by a distinct association with intron length, gene length and splice site strength. For one class of introns, we also observed long lasting retention in the primary transcript, but efficient secondary splicing/degradation at later time points. Finally, we showed that processing of most small nucleolar (sno)RNA-containing introns is remarkably inefficient with the majority of introns being spliced and degraded rather than processed into mature snoRNAs. In summary, our study yielded unparalleled insights into the kinetics of RNA processing and provides the tools to study molecular mechanisms of RNA processing and their contribution to gene expression regulation at the nucleotide level.
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Presenting author: Peter Glaus, University of Manchester, United Kingdom
Sunday, July 21 Room: Hall 14.2

Additional authors:
Antti Honkela, University of Helsinki, Finland
Magnus Rattray, University of Manchester, United Kingdom

Area Session Chair: Cenk Sahinalp

Presentation Overview:
Analysing RNA-seq data poses multiple challenges due to base mismatches, non-uniform read distribution, reads shared by multiple splice variants and other factors which make the expression analysis especially difficult. The BitSeq method uses a Bayesian approach to model the read generation and sequencing processes and infers expression estimates of individual transcripts. Transcript expression levels can be used to obtain more accurate gene expression estimates, in comparison to popular count based methods, or for identifying differentially expressed transcripts or genes. Our differential expression model combines the uncertainty of the expression estimates with variances estimated from biologically replicated experiments to identify significantly differentially expressed transcripts with improved precision.
We present advantages of using BitSeq in RNA-seq datasets dealing with multi-mapping reads and non-uniform read distribution. Experiments with real and synthetic datasets show that BitSeq produces state-of-the-art results in both expression estimation and differential expression analysis.
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Presenting author: Hendrik Tiedemann, Helmholtz Center Munich, Germany
Tuesday, July 23 Room: Hall 14.2

Additional authors:
Elida Schneltzer, Helmholtz Center Munich, Germany
Gerhard Przemeck, Helmholtz Center Munich, Germany
Martin Hrabě De Angelis, Helmholtz Center Munich, Germany

Area Session Chair: Lonnie Welch

Presentation Overview:
The Delta-Notch signal transduction pathway is involved in numerous processes in embryogenesis and adult organisms.
After binding of the Delta or Jagged ligand to the Notch receptors on the membrane of neighboring cells the cleaved-off
intracellular domain of Notch activates genes of the Hey/Hes trancription factor family, which show ultradian expression
in somitogenesis and some neural progenitor cells. While in somitogenesis D/N-signaling enforces the synchronization of
ultradian oscillators and is important for boundary formation, in neurogenesis it acts by lateral inhibition to give some
cells a different developmental fate than their neighbors. Similar processes destine some cells in intestinal crypts,
the developing airways of the lung, and the epithelial ducts of the developing pancreas to different fates.
With our gene- and cell-based computer model we simulated boundary formation in somitogenesis and islet progenitor
cell formation in pancreas and examined which parameters steer the systems toward lateral inhibition or synchronization,
respectively.
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Presenting author: Aviad Tsherniak, Broad Institute of MIT and Harvard, United States
Tuesday, July 23 Room: Hall 14.2

Additional authors:
Diane Shao, Broad Institute, United States
William Hahn, Broad Institute, United States
Jill Mesirov, Broad Institute, United States

Area Session Chair: Lonnie Welch

Presentation Overview:
Genome-scale RNAi libraries enable the systematic interrogation of gene function. However, the interpretation of RNAi screens is complicated by the observation that RNAi reagents designed to suppress the mRNA transcripts of the same gene often produce a spectrum of phenotypic outcomes due to differential on-target gene suppression or perturbation of off-target transcripts. Here we present ATARiS, a computational method that takes advantage of patterns in RNAi data across multiple samples in order to enrich for RNAi reagents whose phenotypic effects relate to suppression of their intended targets. By summarizing only such reagent effects for each gene, ATARiS produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample. This method is robust for datasets that contain as few as ten samples and can be used to analyze screens of any number of targeted genes. ATARiS is available at http://broadinstitute.org/ataris
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Presenting author: Petr Nazarov, Centre de Recherche Public de la Sante, Luxembourg
Tuesday, July 23 Room: Hall 14.2

Additional authors:
Susanne Reinsbach, University of Luxembourg, Luxembourg
Arnaud Muller, Centre de Recherche Public de la Sante, Luxembourg
Nathalie Nicot, Centre de Recherche Public de la Sante, Luxembourg
Demetra Philippidou, University of Luxembourg, Luxembourg
Laurent Vallar, Centre de Recherche Public de la Sante, Luxembourg
Stephanie Kreis, University of Luxembourg, Luxembourg

Area Session Chair: Ralf Zimmer

Presentation Overview:
MicroRNAs (miRNAs), small non-coding RNAs that negatively regulate gene expression at the post-transcriptional level, are involved in fine-tuning fundamental cellular processes and are believed to confer robustness to biological responses. Using microarray data we investigated simultaneously the transcriptional changes of miRNA and mRNA expression levels over time after activation of the Jak/STAT pathway by IFN-γ stimulation of melanoma cells. We observed delayed responses of miRNAs (after 24-48 h) with respect to mRNAs (12-24 h) and identified biological functions involved at each step of the cellular response. Inference of the upstream regulators allowed for identification of transcriptional regulators involved in cellular reactions to IFN-γ stimulation. Linking expression profiles of transcriptional regulators and miRNAs with their annotated functions, we demonstrate the dynamic interplay of miRNAs and upstream regulators with biological functions. Finally, our data revealed network motifs in the form of feed-forward loops involving transcriptional regulators, mRNAs and miRNAs.
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Highlights Track: Mass Spectrometry & Proteomics
Presenting author: Mathieu Clément-Ziza, Biotec, Technische Universitaet Dresden, Germany
Monday, July 22 Room: Hall 14.2

Additional authors:
Paola Picotti, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
Henry Lam, The Hong Kong University of Science and Technology, Hong Kong
David Campbell, Institute for Systems Biology, United States
Alexander Schmidt, University of Basel, Switzerland
Eric Deutsch, Institute for Systems Biology, United States
Hannes Röst, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
Zhi Sun, Institute for Systems Biology, Seattle, United States
Olivier Rinner, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
Lukas Reiter, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
Qin Shen, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
Jacob Michaelson, Technische Universitaet Dresden, Germany
Andreas Frei, Institute of Molecular Systems Biology, ETH Zurich, Switzerland
Simon Alberti, Max Planck Institute of Molecular Cell Biology and Genetics, Germany
Ulrike Kusebauch, Institute for Systems Biology, Seattle, United States
Bernd Wollscheid, nstitute of Molecular Systems Biology, ETH Zurich, Switzerland
Robert Moritz, Institute for Systems Biology, Seattle, United States
Andreas Beyer, BIOTEC, Technische Universitaet Dresden, Germany
Ruedi Aebersold, Institute of Molecular Systems Biology, ETH Zurich, Switzerland

Area Session Chair: Sean O'Donoghue

Presentation Overview:
sing a combination of new proteomics methods and novel computational algorithms we investigated the impact of natural genetic variation on protein concentrations. To accomplish this task we generated an almost complete reference map of the yeast proteome for shotgun and targeted proteomics. We used this map in a series of shotgun- and targeted proteomics experiments in a panel of 78 budding yeast strains in order to identify protein-QTL, i.e. genomic regions associated with protein abundance changes. These experiments were informed by computational network analysis. Using a powerful new machine-learning approach we could identify a surprisingly large fraction of protein-QTL being in epistasis with each other.
The network-based analysis facilitated the identification of protein modules, whose members are affected by several independent genetic variants in a coordinated way. This suggests that selective pressure favors the acquisition of sets of polymorphisms that adapt protein abundances at the pathway level.
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Presenting author: Michael Liam Tress, Centro Nacional de Investigaciones Oncologicas (CNIO), Spain
Tuesday, July 23 Room: ICC Lounge 81

Additional authors:
Iakes Ezkurdia, Centro Nacional de Investigaciones Oncologicas (CNIO), Spain
Angela del Pozo, Centro Nacional de Investigaciones Oncologicas (CNIO), Spain
Jose Manuel Rodriguez, Centro Nacional de Investigaciones Oncologicas (CNIO), Spain
Alfonso Valencia, Centro Nacional de Investigaciones Oncologicas (CNIO), Spain
Jennifer Harrow, Wellcome Trust Sanger Centre, United Kingdom
Adam Frankish, Wellcome Trust Sanger Centre, United Kingdom
Keith Ashman, Centro Nacional de Investigaciones Oncologicas (CNIO), Spain

Area Session Chair: Janet Kelso

Presentation Overview:
As part of a comprehensive analysis of experimental spectra from two large publicly available mass spectrometry databases we provide a detailed overview of the population of alternatively spliced protein isoforms detectable by peptide identification methods. We found that 150 genes expressed multiple alternative protein isoforms. This constitutes the largest set of reliably confirmed alternatively spliced proteins yet discovered.

Alternative isoforms generated from interchangeable homologous exons and from short indels were significantly enriched, both in human experiments and parallel analyses of mouse and Drosophila proteomics experiments. Our results show that a surprisingly high proportion (25%) of the detected alternative isoforms are only subtly different from their constitutive counterparts.
The evidence of a strong bias towards subtle differences in coding sequence and likely conserved cellular function and structure is remarkable and strongly suggests that the translation of alternative transcripts may be subject to selective constraints.
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Highlights Track: Population Genomics
Presenting author: Yufeng Wu, University of Connecticut, United States
Monday, July 22 Room: Hall 4/5

Area Session Chair: Russell Schwartz

Presentation Overview:
Incomplete lineage sorting is a genealogical phenomenon that is caused by the inherent stochasticity of population genealogical processes. With incomplete lineage sorting, gene tree topologies may be different from the species tree topologies and thus may potentially cause difficulty in inferring species phylogeny or population evolutionary history. An established topic in incomplete lineage sorting is computing the probability (called gene tree probability) of a gene tree topology for a given species tree based on coalescent theory. However, previously there exists no practical algorithm for computing the gene tree probability for large trees. Gene tree probability is. In this talk, I will present an algorithm for computing the gene tree probability. This algorithm is much faster than an existing algorithm and can be applied to larger trees. Thus, this new algorithm may be useful in large-scale phylogenetics study.
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Highlights Track: Protein Interactions & Molecular Networks
Presenting author: Jacques Colinge, CeMM, Austria
Sunday, July 21 Room: Hall 4/5

Area Session Chair: Olga Vitek

Presentation Overview:
It is well known that viral proteins interfere with the innate immune system of the infected host to block detection or prevent response. Is it all what viruses do to human cells? Do they share common strategies? In a pan viral study mapping by mass spectrometry the protein interactions of 70 viral proteins from 30 viruses known to modulate the innate immune system, we tried to answer these questions. In particular, we found that viruses reprogram a broad range of biological functions through interactions with multifunctional general regulators. We proposed that size-limited virus genomes dictate such strategies, which we could support by comparing the functional and human interactome impact of diverse viral proteins showing non redundancy among a single genome and convergent evolution within virus families. In recent work, we are focusing on a smaller number of viruses whose host interactions have been mapped for almost all their proteins.
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    Cancelled
Presenting author: Rohith Srivas, University of California, San Diego, United States
Sunday, July 21 Room: Hall 4/5

Additional authors:
Aude Guenole, Leiden University Medical Center, Netherlands
Kees Vreeken, Leiden University Medical Center, Netherlands
Ze Zhong Wang, University of California, San Diego, United States
Shuyi Wang, University of California, San Francisco, United States
Nevan Krogan, University of California, San Francisco, United States
Trey Ideker, University of California, San Diego, United States
Haico van Attikum, Leiden University Medical Center, Netherlands

Area Session Chair: Olga Vitek

Presentation Overview:
To protect the genome, cells have evolved a diverse set of pathways designed to sense, signal, and repair multiple types of DNA damage. To assess the degree of coordination and crosstalk among these pathways, we systematically mapped changes in the cell’s genetic network across a panel of differentDNA-damaging agents, resulting in ~1,800,000 differential measurements. Each agent was associated with a distinct interaction pattern, which, unlike single-mutant phenotypes or gene expression data, has high statistical power to pinpoint the specific repair mechanisms at work. The agent specific networks revealed roles for the histone acetyltranferase Rtt109 in the mutagenic bypass ofDNA lesions and the neddylation machinery in cell cycle regulation and genome stability, while the network induced by multiple agents implicatesIrc21, an uncharacterized protein, in checkpoint control and DNA repair. Our multiconditional genetic interaction map provides a unique resource that identifies agent-specific and general DNA damage
response pathways.
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Presenting author: Inna Kuperstein, Institut Cuire, France
Sunday, July 21 Room: Hall 4/5

Additional authors:
Andrei Zinovyev, Institut Curie, France
Emmanuel Barillot, Institut Curie, France
Wolf-Dietrich Heyer, University of California, Davis, United States

Area Session Chair: Olga Vitek

Presentation Overview:
Synthetic lethality (SL) is a framework to decipher molecular pathways and to develop new treatment strategies. The canonical explanation of SL considers two genes functioning in parallel, mutually compensatory pathways, the between-pathway SL. We classify all known types of synthetic lethal interactions and propose a novel mechanism of SL in a single pathway. The new within-reversible-pathway SL (wrpSL) involves pathway with reversible steps and kinetic trapping of a toxic intermediate or of an essential resource. Mathematical modeling recapitulates the possibility of kinetic trapping leading to lethality and reveals the potential contributions of synthetic dosage and positive masking interactions in a single pathway. Experimental data with Homologous Recombination DNA repair pathway validate the concept. Analysis of yeast gene interactions and pathways suggests broad applicability of this novel concept in many biological processes. These observations extend the interpretation of synthetic lethality and contribute to pathways reconstruction and therapeutic approach improvement.
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Presenting author: Alexey Stukalov, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Austria
Monday, July 22 Room: ICC Lounge 81

Area Session Chair: Hagit Shatkay

Presentation Overview:
Guided by current knowledge on the modular structure of protein complexes, we propose BI-MAP, a novel statistical approach to analyze targeted medium-scale affinity purification-mass spectrometry (AP-MS) datasets. It allows confidently identifying protein modules, i.e. groups of proteins in strong interaction that are shared by multiple complexes. We show that BI-MAP can be applied from small and very detailed maps to large, sparse, and much noisier datasets. In the latter case, the analysis of the inferred posterior distribution helps identifying robust components that frequently recur in the most probable data models. Detailed performance analysis shows that BI-MAP clearly outperforms alternative algorithms addressing the same problem. A new graphical grammar representing the inferred modules and their interactions provides a convenient visual representation of the very complex underlying data that facilitates data interpretation by biologists. BI-MAP is open source with exports to R, Cytoscape and GraphML.
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Presenting author: Paula Petrone, Hoffmann-La Roche, Switzerland
Monday, July 22 Room: Hall 14.2

Additional authors:
Ben Simms, Novartis NIBR, United States
Anne Mai Wassermann, Novartis NIBR, United States
Eugen Lounkine, Novartis NIBR, United States
Peter Kutchukian, Novartis NIBR, United States
Paul Selzer, Novartis NIBR, United States
Florian Nigsch, Novartis NIBR, United States
Jeremy Jenkins, Novartis NIBR, United States
Allen Cornett, Novartis NIBR, United States
Zhan Deng, Novartis NIBR, United States
John W Davies, Novartis NIBR, United States

Area Session Chair: Serafim Batzoglou

Presentation Overview:
Typically, virtual screening of compound libraries is based on the assumption that structurally similar compounds are likely to share similar properties and bind to the same group of proteins. This model often fails due to the rugged nature of the activity landscape. Furthermore, similarity in chemical space cannot explain the activity of compounds against a specific pathway or groups of pathways. Compounds that incur similar phenotypes and yet are structurally diverse are therefore often overlooked in automated searches. Our alternative perspective on virtual screening and library design is based solely on the interactions of compounds with the proteome. Ligands may be quantitatively grouped by the biological closeness of their targets by means of their biological fingerprints. We study similarity and diversity in biological space as necessary ingredients for compounds in screening libraries. We demonstrate here how compound-target interaction networks can be steered to find novel and biologically relevant chemical matter.
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Presenting author: Janusz Dutkowski, University of California, San Diego, United States
Tuesday, July 23 Room: Hall 4/5

Additional authors:
Michael Kramer, University of California San Diego, United States
Michal Surma, 3. Max Planck Institute, Germany
Rama Balakrishnan, Stanford University, United States
J. Michael Cherry, Stanford University, United States
Nevan Krogan, University of California, San Francisco, United States
Trey Ideker, University of California San Diego, United States

Area Session Chair: Reinhard Schneider

Presentation Overview:
Ontologies are of key importance to many domains of biological research. The Gene Ontology (GO), in particular, has proven instrumental in unifying knowledge about biological processes, cellular components, and molecular functions through a hierarchy of concepts and their interrelationships. However, given only partial biological knowledge and inconsistency in how this knowledge is curated, it has been difficult to construct, extend and validate GO in an unbiased manner. To address this problem we have recently developed a new computational system that infers ontological representations automatically from large-scale maps of gene and protein interactions. The result is a network-extracted ontology (NeXO), which contains 4,123 biological concepts and 5,766 hierarchical concept relations, capturing the majority of known cellular components and identifying approximately 600 new components and relationships. As we show, many new components can be validated using a combination of experimental and bioinformatic approaches, and used directly to update the Gene Ontology structure.
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Presenting author: Gabriele Sales, Università di Padova, Italy
Tuesday, July 23 Room: Hall 7

Additional authors:
Paolo Martini, Università di Padova, Italy
Enrica Calura, Università di Padova, Italy
Chiara Romualdi, Università di Padova, Italy

Area Session Chair: Alfonso Valencia

Presentation Overview:
Gene expression analysis is increasingly relying on information about pathway topology to enhance result interpretation. This connection between pathway annotation and analysis remains limited. Pathway representation formats have grown richer, but at the same time they gained a great deal of complexity that offers no direct advantage to data modelling. As a result, most analysis methods completely discard the information about topology and instead focus on simple gene lists.
Our recent efforts have been directed to fill this gap between annotation and analysis. We developed a totally new computational platform that exploits both the richness of the latest pathway data formats (such as BioPax 3) and the sensitivity of the topological analyses.
Our software is able to convert topological information into gene networks. From this, it can dissect the complexity of a pathway identifying the portions associated with a biological process, providing easy visualization, access and interpretation of expression data.
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Highlights Track: Protein Structure & Function
Presenting author: Avner Schlessinger, Mount Sinai School of Medicine, United States
Sunday, July 21 Room: Hall 14.2

Additional authors:
Ethan Geier, University of California, San Francisco, United States
Hao Fan, University of California, San Francisco, United States
Jonathan Gable, University of California, San Francisco, United States
John Irwin, University of California, San Francisco, United States
Kathleen Giacomini, University of California, San Francisco, United States
Andrej Sali, University of California, San Francisco, United States

Area Session Chair: Russell Schwartz

Presentation Overview:
We describe a structure-based discovery approach to identify small molecule ligands for pharmacologically important membrane proteins. Here, we focus on LAT-1, a transporter of amino acids, thyroid hormones, and prescription drugs that is highly expressed in the blood-brain-barrier (BBB) and various types of cancer. LAT-1 is important for cancer development as well as for mediating drug and nutrient delivery across the BBB, making it a key drug target. We identify four LAT-1 ligands, including one chemically novel substrate, by comparative modeling, virtual screening, and experimental testing. These results may rationalize the enhanced brain permeability of two drug-like molecules, including the anti-cancer agent acivicin. Two of our hits inhibited proliferation of a cancer cell-line by distinct molecular mechanisms, providing useful chemical tools to characterize the role of LAT-1 in cancer metabolism. Finally, our integrated approach is generally applicable to characterization of other protein families and their interactions with small molecule ligands.
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Presenting author: Elisa Cilia, Université Libre de Bruxelles, Belgium
Tuesday, July 23 Room: ICC Lounge 81

Additional authors:
Tom Lenaerts, Université Libre de Bruxelles, Belgium
Geerten Vuister, University Of Leicester, United Kingdom

Area Session Chair: Janet Kelso

Presentation Overview:
Experimental NMR relaxation studies have shown that peptide binding induces dynamical changes at the side-chain level throughout the second PDZ domain of PTP1e, identifying as such the residues involved in long-range communication. Even though different computational approaches have identified qualitatively similar subsets of these residues, no quantitative analysis of the accuracy of these predictions was thus far determined.
We show that our own approach based on Monte-Carlo sampling and information theoretical analysis gives significantly more accurate results than the methods that aimed to tackle the same question earlier. Moreover, a network is inferred that captures clearly the residues involved in the process. We show furthermore that these predictions are consistent within both the human and mouse variants of this domain.
Together, these results improve the understanding of intra-protein communication and allostery in PDZ domains, underlining at the same time the necessity of producing similar data sets for further validation purposes.
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Presenting author: Predrag Radivojac, Indiana University, United States
Tuesday, July 23 Room: ICC Lounge 81

Area Session Chair: Janet Kelso

Presentation Overview:
The presentation will first provide motivation for and challenges of predicting protein function. This will include both biological significance and also precise computational problem formulation. We will then present details (at an appropriate level for a highlight presentation) of the CAFA experiment as described in the paper, discuss current state-of-the art in protein function prediction, and lay out possible avenues for improvements and accuracy assessment of computational function prediction. Finally, we intend to briefly discuss the next CAFA challenge whose start will coincide with the ISMB 2013 conference.
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Highlights Track: Sequence Analysis
Presenting author: Xuejian Xiong, Hospital for Sick Children, Canada
Sunday, July 21 Room: Hall 7

Additional authors:
John Parkinson, Hospital For Sick Children, Canada
Daniel Frank, University of Colorado, United States
Charles Robertson, University of Colorado, United States
Stacy Hung, Hospital for Sick Children, Canada
Janet Markle, Hospital for Sick Children, Canada
Jayne Danska, Hospital for Sick Children, Canada
Philippe Poussier, Sunnybrook Health Sciences Centre Research Institute, Canada
Angelo Canty, McMaster University, Canada
Kathy McCoy, University of Bern, Switzerland
Andrew MacPherson, University of Bern, Switzerland

Area Session Chair: Predrag Radivojac

Presentation Overview:
The emerging science of metagenomics is transforming our understanding of the relationships of microbes with their environments. Moving beyond cataloguing the organisms and genes present, metatranscriptomics offers the exciting prospect of providing a more mechanistic understanding of these relationships. Exploiting metatranscriptomic data from microbiomes of increasing complexity, generated using the Illumina platform, we are developing novel software pipelines to process and interpret these datasets. Key to these analyses is adopting a protein-protein interaction and other systems datasets as frameworks onto which metatranscriptomic data may be integrated and interpreted. In this presentation I will outline some of the significant challenges we have encountered in analysing metatranscriptomic data generated by next generation sequencing platforms and discuss how these challenges are may be addressed.
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Presenting author: Steven Brenner, University of California, Berkeley, United States
Sunday, July 21 Room: Hall 7

Additional authors:
Jacob Mallott, UCSF, United States
Antonia Kwan, UCSF, United States
Joseph Church, USC, United States
Diana Gonzalez, UCSF, United States
Fred Lorey, Public Health Institute, United States
Ling Tang, UCSF, United States
Rajgopal Srinivisan, Tata Conservancy Service, in
Sadhna Rana, Tata Conservancy Service, in
Uma Sunderam, Tata Conservancy Service, in

Area Session Chair: Ivo Hofacker

Presentation Overview:
Severe combined immunodeficiency (SCID) is characterized by failure of T lymphocyte development. Newborn screening to identify SCID is now performed in several states. In addition to infants with typical SCID, screening identifies infants with T lymphocytopenia who appear healthy and in whom a SCID diagnosis cannot be confirmed. Deep sequencing was employed to find causes of T lymphocytopenia in such infants. Whole exome sequencing and analysis were performed in infants and their parents. Upon finding deleterious mutations in the ataxia telangiectasia mutated (ATM) gene, we confirmed the diagnosis of ataxia telangiectasia (AT) in two infants. AT is usually not diagnosed until much later in life, after symptoms are manifest. Although there is no current cure for the progressive neurological impairment of AT, early detection permits avoidance of infectious complications, while providing information for families regarding reproductive recurrence risks and increased cancer risks in patients and carriers.
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Presenting author: Michael Baym, Harvard Medical School, United States
Monday, July 22 Room: Hall 14.2

Additional authors:
Po-Ru Loh, MIT, United States
Bonnie Berger, MIT, United States

Area Session Chair: Serafim Batzoglou

Presentation Overview:
The past two decades have seen an exponential increase in sequencing capabilities, outstripping advances in computing power. Extracting new insights from the datasets currently being generated will require not only faster computers; it will require smarter algorithms. However, most genomes currently sequenced are highly similar to ones already collected; thus the amount of novel sequence information is growing much more slowly. We show that this redundancy can be exploited by compressing the data so as to allow direct computation on the compressed data. This approach reduces the computational task of operating on many similar genomes to slightly more than that of operating on just one. Moreover, its relative advantage over existing algorithms grows with the accumulation of future genomic data. We demonstrate this compressive architecture by implementing versions of both BLAST and BLAT, and emphasize how compressive genomics, more generally, will enable biologists to keep pace with current data.
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Presenting author: Rajeev Azad, University of North Texas, United States
Monday, July 22 Room: Hall 4/5

Area Session Chair: Reinhard Schneider

Presentation Overview:

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Presenting author: Misook Ha, Samsung Advanced Institute of Technology, Korea, Rep
Monday, July 22 Room: Hall 14.2

Additional authors:
Soondo Hong, Samsung Display Corporation, Korea, Rep
Wen-Hsing Li, University of Chicago, United States

Area Session Chair: Sean O'Donoghue

Presentation Overview:
Histone modifications play an important role in chromatin structure and gene regulation. To understand the relationship between genome sequence and chromatin structure we studied DNA sequences at histone modification sites in various human cell types. We found sequence specificity for histone modifications. Using the sequence specificities of H3 and H3K4me3 nucleosomes, we developed a model that computes the probability of H3K4me3 occupation at each base-pair from the genome sequence context. A comparison of our predictions with in vivo data suggests a high performance of our method. The predicted H3K4me3 sequence signature preferentially occurs at binding sites of transcription regulators involved in chromatin modification activities, including histone acetylases and enhancer- and insulator-associated factors. Clearly, the human genome sequence contains signatures for chromatin modifications essential for gene regulation and development. Our method may be applied to find new regulatory elements functioning by chromatin modifications and disease-causing impaired chromatin structures.
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Presenting author: Martin Weigt, Universite Pierre and Marie Curie, France
Tuesday, July 23 Room: ICC Lounge 81

Area Session Chair: Janet Kelso

Presentation Overview:
Biological research has been revolutionized by high-throughput experiments. Unprecedented amounts of large-scale data have to be complemented by computational methods unveiling the information hidden in raw data, to increase our understanding of complex biological processes.

As an example, proteins show a remarkable degree of structural and
functional conservation in the course of evolution, despite large sequence divergence. We have developed a
statistical-inference approach, Direct Coupling Analysis, to link sequence variability to protein structure. Using sequence alone, we infer directly co-evolving residue pairs, to detect native residue-residue contacts. This information is used to guide tertiary and quaternary structure prediction. As a specific case study, I will discuss the auto-phosphorylation complex of histidine kinases, which
are involved in the majority of signal transduction systems in the bacteria. Only a multidisciplinary approach integrating statistical genomics, biophysical protein simulation, and mutagenesis experiments, allows us to predict and verify the, previously unknown, active kinase structure.
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Presenting author: Denisa Duma, University of California Riverside, United States
Tuesday, July 23 Room: Hall 4/5

Additional authors:
Stefano Lonardi, University of California Riverside, United States
Matthew Alpert, University of California Riverside, United States
Gianfranco Ciardo, University of California Riverside, United States
Timothy J. Close, University of California Riverside, United States
Steve Wanamaker, University of California Riverside, United States
Yaqin Ma, University of California Riverside, United States
Ming-Cheng Luo, University of California Davis, United States
Yonghui Wu, University of California Riverside, United States
Francesca Cordero, University of Torino, Italy
Marco Beccuti, University of Torino, Italy
Serdar Bozdag, Marquette University, United States
Prasanna R. Bhat, University of California Riverside, United States
Burair Alsaihati, University of California Riverside, United States
Josh Resnik, University of California Riverside, United States

Area Session Chair: Debra Goldberg

Presentation Overview:
The problem of obtaining the full genomic sequence of an organism has been solved either via a global brute-force approach (WGS) or by a divide-and-conquer strategy (clone-by-clone). While the advent of NGS instruments, made the WGS approach the preferred choice, the clone-by-clone strategy is still relevant especially for large complex genomes for which clone libraries and physical maps are available. In this paper, we demonstrate the feasibility of the clone-by-clone approach on the gene-space of a large, very repetitive plant genome. The novelty of our approach consists in exploiting the the high throughput of NGS instruments by pooling together hundreds of clones using a special type of combinatorial pooling design and a companion decoding algorithm.Our method allows accurate determination of the source clone(s) of each sequenced read. I will present extensive simulations and experimental results on the genomes of rice and barley, as well as new developments on decoding algorithms using Compressive Sensing ideas.
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Highlights Track: other
Presenting author: Jennifer Cham, European Bioinformatics Institute, United Kingdom
Tuesday, July 23 Room: Hall 7

Additional authors:
Katrina Pavelin, European Bioinformatics Institute, United Kingdom
Paula de Matos, European Bioinformatics Institute, United Kingdom
Cath Brooksbank, European Bioinformatics Institute, United Kingdom
Graham Cameron, European Bioinformatics Institute, United Kingdom
Hong Cao, European Bioinformatics Institute, United Kingdom
Rafael Alcantara, European Bioinformatics Institute, United Kingdom
Francis Rowland, European Bioinformatics Institute, United Kingdom
Brendan Vaughan, European Bioinformatics Institute, United Kingdom
Silvano Squizzato , European Bioinformatics Institute, United Kingdom
Youngmi Park, European Bioinformatics Institute, United Kingdom
Rodrigo Lopez, European Bioinformatics Institute, United Kingdom
Christoph Steinbeck, European Bioinformatics Institute, United Kingdom

Area Session Chair: Thomas Lengauer

Presentation Overview:
It is recognised that bioinformatics resources often suffer from usability problems: for example, they can be too complex for the infrequent user to navigate, and they can “lack sophistication” compared to other websites that people use in their daily lives. In this presentation, Dr. Jenny Cham, User-Experience Analyst at the European Bioinformatics Institute, UK, will describe specific case studies to show how user-centred design (UCD) principles can be applied to bioinformatics services.

As well as improved usability, the benefits of UCD can include more effective decision-making for design ideas and technologies during development; enhanced team-working and communication; cost effectiveness; and ultimately a bioinformatics service that more closely meets the needs of its target research community.
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