SIGs & Satellite Meetings

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1. Application of structure to systems biology
2. Structure-based drug discovery including polypharmacology and network pharmacology
3. Structure representation, classification and predication
4. Macromolecular assembles
5. Structural genomics
6. 3D databases and data mining
7. Molecular visualization
8. Relevant methods of structure determination, particularly hybrid methods
9. Structure-based function prediction
10. Evolution studied through structures
11. Docking, analysis, prediction and simulation of biomolecular interactions such as protein-protein, protein-ligand and protein-nucleic-acid
12. Prediction and analysis of protein domains
13. Membrane protein structure analysis and prediction
14. Protein dynamics and disorder
15. The structural basis of immunology

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Molecular biology and all the biomedical sciences are undergoing a revolution as a result of improved sensorics. This has resulted in the ability to study biological processes in unprecedented detail, with exploding fields sharing the "-omics" suffix in their name. These include in particular epigenomics, genomics, transcriptomics, proteomics and metabolomics, devoted respectively to the examination of the entire systems of the epigenome, genes, transcripts, proteins and metabolites present in a given cell or tissue type. The availability of these new data sources for biological exploration is changing the way research in quantitative biology proceeds. In terms of sheer numbers we are presented with a large amount of data about the operation of an individual cell (with high throughput sequencing terabytes of information). However, given the complexity of the underlying  systems, decoding the interactions between all the components within the biological system is still a massive challenge. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. Machine learning also offers the promise of combining disparate data sources to gain a better overall picture of the biological system. The MLSB workshop is focussed on the interface between machine learning and systems biology, topics of interest include learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms.

Important Dates:

Submission deadline (4 pages, Springer style): May 2, 2011
Notification: May 26, 2011
Poster submissions will also be possible in June and July 2011.
For details on the submission procedure, please take a look at
the upcoming workshop website.

Program Chairs:

Stefan Kramer
Technische Universität München
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http://wwwkramer.in.tum.de/kramer/stefan.html

Neil Lawrence
University of Sheffield
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• Bioinformatics Education Papers
• Bioinformatics for Biologists Textbook Chapter Proposals
• Bioinformatics Education Abstracts (submitted by educators)
• Undergraduate Bioinformatics Research Abstracts (submitted by undergraduate or first-year graduate students)

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SIG Meetings

URL: http://www.alternative-splicing.org/as-sig-11/
Date: Friday, July 15 - Saturday, July 16
Start time: 8:30 a.m. - End time: 6:00 p.m.
Room Location: Hall O (Austria Center Vienna)
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Alternative splicing (AS) appears as a highly regulated mechanism that is involved in many biological processes, such as cell type differentiation, sex-determination, or apoptosis. Importantly, defects in either cis-acting regulatory elements or trans-acting factors that mediate AS can lead to aberrant splicing and even human disease.

In addition to AS, other RNA transcription and processing events contribute to significantly expand the transcriptome diversity. These include alternative polyadenylation and the expression of many non-coding RNAs, such as micro-RNAs (miRNAs), small-interfering RNAs (siRNAs), PIWI-interacting RNAs (piRNAs), or long nuclear RNAs. While originally many of these phenomena were studied separately, accumulating evidence now points towards the interplay between transcription, RNA processing, RNA turnover and the effects non-coding RNAs exert on these machineries. This interplay leads to a new, interconnected system view and consequent study of gene expression regulation.

With the advent of high-throughput methodologies researchers have been able to obtain genome-wide information to analyze the make-up and the regulation of the transcriptome within a well-defined biological context. While the depth of new data generation is astonishing and a welcomed development, its complexity also poses interpretation challenges, particularly when considering that the snapshot of the transcriptome that high-throughput methodologies permit us to obtain are the result of many integrated gene expression processes.

In this meeting we aim to provide an opportunity to bring together world experts in the fields of RNA processing, non-coding RNAs, and computation to discuss recent advances highlighting the integrated view of RNA biology with the hope to generate new research ideas to decipher the regulation of RNA processing. Moreover, as these studies rely heavily on clever computational approaches to analyze high-throughput data sets, we expect that the context of the ISMB conference and the participation of computational experts will give rise to fruitful interactions and will provide the basis for building a port for shared resources and tools in this fast developing field.

The AS-SIG look forward to welcoming attendees to a fun filled evening at the Winery Fuhrgassl-Huber in Neustift on Friday, July 15!

Located in the heart of Neustift am Walde between vineyards and the Vienna Woods, this Winery with its cosy parlours and romantic patio has been a popular wine tavern for almost 30 years. In the "Schmiede" we will enjoy the atmosphere of a Viennese wine tavern in a sheltered garden. Winery details here.

Delegates must purchase their ticket for €45+VAT online (available under merchandise) by July 5,2011. Transportation to the winery is included with bus transportation from the Austria Center Vienna. Delegates will return individually or in smaller groups of three to four by cab.

19:00 Depart Austria Center Vienna
19:30 Dinner
21:30 (est)   Departure on own

Fuhrgassl-Huber, 1190 Vienna, Neustift/Walde 68
Telephone: +43 (0) 1 440 14 05.


Organizers:

Klemens Hertel
University of California, Irvine, USA
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Yoseph Barash
University of Toronto, Canada
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Dirk Holste
Institute of Molecular Pathology, Vienna
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Eduardo Eyras
Pompeu Fabra University & ICREA, Spain
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URL: http://biofunctionprediction.org/
Date: Friday, July 15 - Saturday, July 16
Start time: 8:30 a.m. - End time: 6:00 p.m.
Room Location: Hall L (Austria Center Vienna)
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The deluge of genomic information is challenging biologists to annotate this data, from locating genes in the raw data through to predicting the function from protein sequence and structure. Following the successful meetings of 2005-2008 we have decided to a SIG that will deal with a wide scope of gene, protein, and genomic annotations.

Keynote speakers:

• Janet Thornton European Bioinformatics Institute
• Amos Bairoch Swiss Institute of Bioinformatics
• Olga Troyanskaya Princeton University
  
  

About CAFA

CAFA is a community-driven effort. About 35 research groups predicted the function of a set of proteins whose true function is sequestered. At the meeting, we will reveal the true functions and discuss the predictions. The CAFA challenge goals are to foster a discussion between annotators, predictors and experimentalists about methodology as quality of functional predictions, as well as the methodology of assessing those predictions.

Besides the CAFA challenge, the Automated Function Prediction SIG will also include keynote and contributed talks.

Contributed talks are sought in, but not limited to:

• Function prediction using sequence based methods. This would include "classic" methods such as detection of functional motifs and inferring function from sequence similarity.
• Function from genomic information: prediction by genomic location; locus comparison with other organisms; function gain and loss.
• Phylogeny based methods
• Function from molecular interactions
• Function from structure
• Function prediction using combined methods
• "Meta-talks" discussing the limitations and horizons of computational function prediction.
• Collaboration between experimental and computational biologists in function prediction.
• Assessing function prediction programs

Organizers:

Iddo Friedberg
Miami University, United States
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Michal Linial
The Hebrew University of Jerusalem, Israel
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Predrag Radivojac
Indiana University, United States
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The Bio-Ontologies SIG provides a forum for discussion of the latest and most innovative research in the appplication of ontologies and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences. Bio-Ontologies has existed as a SIG at ISMB for 13 years, making it one of the longest running.

We are interested in innovative approaches to organising, presenting and consuming knowledge in life sciences and biomedicine. We invite papers in traditional areas, such as the biological applications of ontologies, reports on newly developed Bio-Ontologies, and the use of ontologies in data sharing standards. In addition, we invite submissions on methods, applications and workflows that "bridge the gaps" in the acquisition, dissemination and consumption of scientific content in biomedical informatics research.


We are inviting three types of submissions.

• Short papers, up to 4 pages.
• Poster abstracts, up to 1 page.
• Flash updates, up to 1 page

Following review, successful papers will be presented at the Bio-Ontologies SIG. Poster abstracts will be provided poster space and time will be allocated during the 2 days for at least one poster session. Flash updates are for short talks (5 min) giving the salient new developments on existing public ontologies. Authors of posters can also indicate a desire to provide a flash update.


Organizers:

Nigam Shah, Stanford University, USA
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Susanna-Assunta Sansone, University of Oxford, Oxford e-Research Center, UK
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Susie M Stephens, Johnson & Johnson Pharmaceutical Research & Development, USA
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Larisa Soldatova, University of Aberswyth, UK
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URL: http://light.ece.ohio.edu/bioreg/2011/
Date: Saturday, July 16
Start time: 8:30 a.m. - End time: 6:00 p.m.
Room Location: Hall N (Austria Center Vienna)
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Regulatory genomics involves the study of the genomic ‘control system,’ which determines how, when and where to activate the ‘blueprint’ encoded in the genome.   Regulatory genomics is the topic of much research activity worldwide.  Since computational methods are important in the study of gene regulation, we propose to continue BioRegSIG, an ISMB special interest group that focuses on bioinformatics for regulatory genomics.  An important goal of the SIG is to foster a collaborative community wherein scientists convene to solve difficult research problems in all areas of computational regulatory genomics.

At BioRegSIG-2010, researchers considered the problem of “pattern discovery for prediction/identification of functional elements in sequenced genomes.”  BioRegSIG-2011 will be broadened to include other topics that are important in regulatory genomics research. Specifically, we envision sessions and plenary talks that address the following topics:

• epigenetic analysis (emerging technologies, large scale data handling, applications)
• pattern discovery for prediction/identification of functional elements in sequenced genomes  (algorithms, statistical models, benchmarks and case studies)
• analysis of high-throughput sequencing data in all its applications, including RNA-seq for expression measurement; ChIP-seq for chromatin state; and ChIP-seq for mapping of TFs (including particular cell-type specific TFs)
• miRNAs (including how to predict ncRNA genes and miRNA target sites)
• developing models of TF specificity from various types of high-throughput data
• regulation of alternative splicing
• the role of chromatin structure and histone modifications in the regulation of alternative splicing and in gene regulation (including how to integrate different types of information into TFBS prediction and analysis)
• evolution and genetics of regulation (eQTL analyis)
• elucidating gene regulatory networks and circuits
  

SIG CHAIRS

• Finn Drablos, Norwegian University of Science and Technology, Norway
• Sophie Schbath, Institut National de la Recherché Agronomique, France
• Lonnie Welch, Ohio University, USA

SIG ORGANIZING COMMITTEE

• Herbert Auer, Institute for Research in Biomedicine, Spain
• Tim Bailey, University of Queensland, Australia
• Laura Elnitski, National Human Genome Research Institute, USA
• Roderic Guigo, Centre for Genomic Regulation, Spain
• Kathleen Marchal, Katholieke Universiteit Leuven, Belgium
• Gerhard Mittler, Max Planck Institute, Germany
• Matias Piipari, Wellcome Trust Sanger Institute, UK
• Daniel J. Quest, Oak Ridge National Laboratory, USA
• Isidore Rigoutsos, Thomas Jefferson University, USA
• Saurabh Sinha, University of Illinois, USA
• Gary Stormo, Washington University, USA
• Weixiong Zhang, Washington University, USA
  

Organizers (contact):

Finn Drablos
Norwegian University of Science and Technology, Norway
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Sophie Schbath
Institut National de la Recherché Agronomique, France
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Lonnie Welch
Ohio University, USA
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URL: http://www.open-bio.org/wiki/BOSC_2011
Date: Friday, July 15 - Saturday, July 16
Start time: 8:30 a.m. - End time: 6:00 p.m.
Room Location: Hall F2 (Austria Center Vienna)
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The Bioinformatics Open Source Conference (BOSC) is sponsored by the Open Bioinformatics Foundation (O|B|F), a non-profit group dedicated to promoting the practice and philosophy of Open Source software development within the biological research community. Many open source bioinformatics packages are widely used by the research community across a wide range of application areas. They form a cornerstone in enabling research in the genomic and post-genomic era. Open source bioinformatics software has facilitated rapid innovation, dissemination, and wide adoption of new computational methods, reusable software components, and standards.  The sessions planned for BOSC 2011 include:

• Approaches to parallel processing -- This session will cover data-parallel approaches to analyzing the massive data sets from next-generation sequencing and mass spec proteomics, or reports on the parallelization of bioinformatics algorithms in general.
• Cloud-based approaches to improving software and data accessibility -- The emergence of cloud computing has made highly scalable cluster computing available to computational biologists. Services such as Amazon Elastic Compute Cloud combined with publicly available datasets promise to lower the overhead to participate in large scale data analyses. Talks will focus on how the community can build up resources and datasets for cloud infrastructure, as well as the sharing of insights, and the contribution of implemented workflows.
• The Semantic Web in open source bioinformatics -- Software that employs semantic web technology: Existing databases such as UniProt are now being made fully available in RDF format, and new repositories such as Bio2RDF are being built upon RDF and are allowing RDF-based query language use (e.g., SPARQL). This session is devoted to reports on software that works with such new data stores, or aids in their development, as well as descriptions of software that pertains to ontology building or maintenance. We also solicit talks about projects that employ information management architectures/frameworks such as UIMA, an open source project in Apache Incubator status, that facilitate integration of semantic analysis and search components.
• Genome content management -- Processes and technologies that support the creating, managing and reporting of genomic data. This session is appropriate for discussion of systems that involve components such as (but not limited to) Ensembl and GMOD/Chado data stores, Taverna and Galaxy analysis workflows, and BioMart and InterMine warehouses.
• Data visualization-- Approaches to presenting large biomedical datasets with visual aids that make the data easier to understand and analyze.
• Tools for next-generation sequencing -- Practical approaches to analyzing next generation sequencing data, focusing on flexible tools that combine existing software and algorithms.
• Meeting the challenges of inter-institutional collaboration (panel discussion) -- Many open source projects involve collaborators from organizations all over the world. Participants in this panel discussion will comment on how their projects have dealt with the challenges that arise from these multi-institution collaborations.
  

Please visit our web site at http://www.open-bio.org/wiki/BOSC_2011 for more details about BOSC 2011.

Organizers:

Nomi Harris
Lawrence Berkeley National Laboratory, USA
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Peter Rice
European Bioinformatics Institute, UK
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URL: hitseq.org
Date: Friday, July 15 - Saturday, July 16

Start time: 8:30 a.m. - End time: 6:00 p.m.

Room Location: Hall F1 (Austria Center Vienna)
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High Throughput Sequencing is revolutionizing the way biologists generate and analyze biological data. While in the past sequencing was used mainly to characterize individual genomes or transcripts, low-cost high-throughput sequencing can now address a broad range of genetic analysis applications including: comparative genomics, high-throughput polymorphism detection, analysis of coding and non-coding RNAs, identifying mutant genes in disease pathways, and profiling metagenomes. Some of the earlier “high-throughput” technologies, such as Illumina, and ABI SOLiD have compromised read length to increase throughput. Forthcoming, 3rd Generation sequencing technologies are expected to have significantly longer reads, albeit at the cost of high error rates, while maintaining, or increasing the overall throughput.

While the promise of high throughput sequencing (HTS) technologies has become a reality, and computational methods for assembly, alignment, and variation detection using HTS reads are becoming available, there is still significant room to improve. Programs and algorithms developed for Sanger-style reads must be scaled, or completely reinvented to match the characteristics of the HTS data.

These Special Interest Group sessions will be devoted to discussing latest advances in computational techniques for HTS datasets, and will provide a forum for in depth presentations of the methods and discussion among the scientists working in this field.

I. Detecting Genome variation

While the problem of mapping reads to a pre-existing “reference” genome is becoming better understood, better algorithms for discovery of important variants from the mapped reads are still necessary. This session will explore methodology to infer various polymorphisms (SNVs, large insertions/deletions, copy number variations) with HTS data. As the HapMap and now the 1000 Genomes projects have increased our understanding of SNPs, structural variation (including insertions, deletions, translocation, inversions, and CNVs) have come to the forefront as one of the main sources of variation within a species. HTS technologies offer the potential of high resolution detection of structural variants, in particular in cancer genomes, but they will also create a deluge of data, requiring the use of more powerful algorithms. The methods for detecting structural variants using HTS data are a promising research area for the genome analysis community.

II. Transcriptome Analysis

Another exciting application of HTS technologies is RNA sequencing. Here the target sequence is not the genome, but the set of RNA molecules transcribed from the genome. RNA sequencing is currently used for several applications, including RNA expression, de-novo transcriptome sequencing for non-model organisms, and novel (coding and non-coding) transcript discovery, among others. For RNA and micro-RNA expression profiling, HTS has significant advantages compared to microarray methods in that it is better able to identify quantities of very common and very rare transcripts. RNA sequencing also makes possible the discovery of novel transcripts, which do not match any probe on a microarray – an important feature for discovery of novel transcripts, and identification of alternative splicing. Transcriptome profiling is also increasingly used for sequencing the RNA of non-model organisms for phylogenetic and population studies, and methods for transcriptome assembly and co-assembly need to be improved. With sequencing, detection of expressed SNVs and their use to detected allele-specific expression and cis-regulatory elements is also feasible. This, again, is a novel area of algorithmic exploration.

III. Epigenomics and chromatin regulation

HTS Sequencing is also commonly used for discovery of transcription factor binding sites (and nucleosome positioning) using CHiP-seq. In addition, many techniques are appearing to explore the epigenome, including methylation sensitive restriction enzymes, MeD-IP, and bisulfate resequencing. The latter makes the problem of aligning reads much more complex. Thus, novel algorithms will be required to handle these experiments.

IV. Metagenomics.

Another common application of HTS is metagenomics. This ranges from sequencing of environmental samples, to census of microbial flora in disease affected individuals vs. control. We welcome presentations on these topics, as well as any other important methods related to HTS technologies.

V. 3rd Generation Sequencing Technologies

One of the key developments in the HTS field is the emergence of 3rd generation, single molecule sequencing technologies. These promise to enhance the utility of whole genome DNA sequencing and the associated genetic analysis applications by providing longer reads, quicker turnaround times, and valuable kinetic information that enables direct detection of base modifications as part of a sequencing run. The diversity of applications and flexibility of single molecule sequencing technologies will demand novel approaches to modeling sequencing data in order to fully leverage the information (e.g., kinetic information associated with each read) obtained from such platforms. As was the case with other HTS platforms, programs and algorithms developed for Sanger and NGS data will need to be modified or reinvented to match the characteristics of single molecule sequencing data. Managing and organizing the data in ways that facilitate interpretation and data mining applications will also be a challenge.

3. Draft Schedule

Day 1:

9 am – 12 pm Session I, Variation Discovery: Keynote address 1, 7-8 talks

1:30pm - 6 pm Session II, Transcriptomics: ~10 talks

Day 2:

9 am – 12 pm Session III, Epigenetics & Metagenomics: ~10 talks

1:30pm - 4:30 pm Poster Session

5pm – 6pm Keynote address 2

Organizers:

Inanc Birol
British Columbia Cancer Agency, Canada
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Michael Brudno
University of Toronto, Canada
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Francisco M. De La Vega
San Mateo, United States
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Gunnar Rätsch
Max Planck Society, Germany
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Jens Stoye
Bielefeld University, Germany
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URL: http://nrnb.org/netbiowiki
Date: Friday, July 15
Start time: 8:30 a.m. - End time: 6:00 p.m.
Room Location: J241 (Austria Center Vienna)
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Biological networks provide a context for integrating and analyzing massive amounts of diverse kinds of measurement data, such as gene expression data from microarray experiments, protein abundance data from mass spectrometry, and genetic data from association studies. Network theory provides powerful analysis techniques that can be used to develop insights into large amounts of data. Our use of networks in biology has changed from purely representational and didactic purposes to more analytic and hypothesis formulation purposes. This shift has resulted, in part, from the confluence of advances in computation, informatics, and high throughput techniques in systems biology.

For interactions amongst thousands of genes, proteins, and metabolites, these networks can be quite large and complex. Network analysis tools can manage this complexity, for example by identifying clusters of activity – regions of high interconnectivity that are also characterized by high gene expression levels. The most highly connected molecules or hubs of such regions may be control points for the network; modulating their activity may influence the activity of many other molecules. With knowledge of the structure and behavior of biological networks, biologists can identify intervention points for drugs and therapeutics, limit adverse side-effects of treatments, and infer predisposition to disease.

We are soliciting abstracts that cover new developments in network biology. The SIG will focus on two major areas: (1) the development of network-related tools and resources, and (2) the application of network analysis and visualization in the study of biology, synthetic biology and medicine. The SIG will provide a unique interface between tool developers and users in the field of network biology. Through these complementary lenses, the SIG will bring into focus the current state of the field, its future promise and how to get there.

Organizers:

Alexander Pico
David J Gladstone Institutues, USA
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Scooter Morris
University of California, San Francisco, USA
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Allan Kuchinsky
Agilent Technologies, USA
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Annette Adler
Agilent Technologies, USA
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Gary Bader
University of Toronto
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Mario Albrecht
Max Planck Institut
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Diego di Bernardo
Telethon Institute of Genetics and Medicine
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Ian Donaldson
University of Oslo
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Hana El-Samad
University of California at San Francisco
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URL: http://snps.uib.es/snp-sig/
Date: Friday, July 15
Start time: 8:30 a.m. - End time: 6:00 p.m.
Room Location: Hall N (Austria Center Vienna)
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The primary goal of the SNP-SIG is to outline and discuss the recent advances in the methodology for the annotation and analysis of genomic variation data.

SNPs are generally very interesting in the context of their phenotypic manifestations. The discrepancy between the significant availability of SNP data and the current lack of its interpretation requires the development of methods for the annotation/prediction of the SNP impact. Direct-to-consumer (DTC) companies, such as 23andMe and Navigenics, offer DNA tests to provide insight on the genetic traits. In the near future the analysis of genetic variation will be a key factor for the understanding of the information encoded in the genome

The SNP-SIG provides a forum necessary for the organization of a research network facilitating the exchange of ideas and the establishment of new collaborations bringing together varying expertise. It will thus support the unprecedented collaborative effort to manage the complexity of the analysis and evaluation of genetic variation.

Keynote Presenters:
Atul J Butte, Stanford University (USA)
Mauno Vihinen, University of Tampere (Finland)

We are interested in attracting submissions describing original work in all the fields of genomic variation research including, but not limited to:

• Databases, data mining algorithms and visualization tools for SNP analysis
• Methods for predicting structural/functional impacts of SNPs
• Personal Genomics, GWAS studies and SNP prioritization
• Population genomics and phylogenetic analysis

Organizer(s):

Yana Bromberg
Rutgers University, United States
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Emidio Capriotti
Stanford University, United States
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