Literature DB >> 22759647

SNP-SIG Meeting 2011: identification and annotation of SNPs in the context of structure, function, and disease.

Yana Bromberg1, Emidio Capriotti.   

Abstract

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Year:  2012        PMID: 22759647      PMCID: PMC3395891          DOI: 10.1186/1471-2164-13-S4-S1

Source DB:  PubMed          Journal:  BMC Genomics        ISSN: 1471-2164            Impact factor:   3.969


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Overview

Advances in high-throughput sequencing, genotyping, and characterization of haplotype diversity are consistently generating vast amounts of genomic data. Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation. In the recent years the number of known SNPs has been increasing exponentially; the last release of the NCBI’s dbSNP database contained more than 50 million human SNPs. SNPs are interesting as both markers of evolutionary history and in the context of their phenotypic manifestations (e.g. characteristic traits and diseases). For some diseases, e.g. sickle-cell anemia, the causative SNPs are well documented. In most other cases, the detection of disease-causing variants is still a problem. The genome-wide association studies (GWAS) provide insight into SNP-disease relationships. However, GWAS analysis is both experimentally and computationally expensive and fails to properly consider the rare variants, i.e. individual-specific SNPs that have yet to be documented on a population scale. This discrepancy between the deluge of SNP data and the lack of its interpretation spurs the development of the SNP impact annotation/prediction algorithms. In the near future, the study of genetic variation in disease and treatment options will be key for the development of the field of personalized medicine. In 2010, the first edition of the Critical Assessment of Genomic Interpretation (CAGI; Berkeley, California) was organized to evaluate the ability of available computational methods to predict the phenotypic impacts of genomic variation. Annotation of SNPs was also a hot topic in many other meetings, such as AIMM at ECCB 2010 (Ghent, Belgium), the HGVS 2010 meeting (Washington, DC) and PSB 2011 (Big Island of Hawaii, USA). In line with the increasing interest in the genetic variation analysis and annotation, on July 15th, 2011 we organized the first SNP Special Interesting Group (SNP-SIG) meeting at ISMB/ECCB’2011 in Vienna, Austria (http://snps.uib.es/snp-sig/2011). This meeting attempted to summarize the field’s research advances in the directions of “Annotation and prediction of structural/functional impacts of coding SNPs” and “SNPs and Personal Genomics: GWAS, populations and phylogenetic analysis”. Over 70 scientists actively working in the field and strongly interested in its development have officially registered for the SIG. On the date of the meeting, an even larger number of ISMB participants have gathered to discuss their work, the state of the art, and future perspectives. In all, 17 presentation proposals and 13 posters were submitted to the SIG and eight works where selected for an oral presentation at the meeting. Distinguished scientists were invited to share their visions of the field past, present, and future: Steven Brenner (University of California at Berkeley), Atul Butte (Stanford University), John Moult (University of Maryland, College Park), Burkhard Rost (Techinal University of Munich) and Mauno Vihinen (Lund University). A round table discussion on the most timely and important problems of SNP annotation was held, directed by Christopher Baker (University of New Brunswick), Maricel Kann (University of Maryland, Baltimore), Sean Mooney (Buck Institute), Pauline Ng (Genome Institute of Singapore) and Mauno Vihinen (Lund University). We have invited all SIG presenters to submit full research papers for publication in this special issue of BMC Genomics. We adopted a peer review process to select ten exceptional works. The articles cover different aspects of the field, presenting databases and tools for the annotation of SNPs as well as novel scientific advances achieved based on these resources. The described methods use different types of information derived from sequence, evolution, function and structure to analyze large sets of variations. They address SNP-associated (1) specific protein function [1] or (2) structure/stability [2,3] changes or focus on (3) non-coding SNPs [4,5] or (4) specific disease classes [6,7]. Presented work gives new life to the information buried in literary free text [8] and outlines the potential of using SNP functional impacts to predict disease involvement [9]. We also include a method developer tutorial/framework that will be very helpful for all future work in the field [10].

Next meeting

We are now working on the organization of the next edition of the SNP-SIG meeting to be held in the context of the ISMB 2012, Long Beach, California. Further information about the SNP-SIG 2012 is available on our web site (http://snps.uib.es/snp-sig). Submissions of posters and presentation proposals are welcome.
  10 in total

1.  Evaluating our ability to predict the structural disruption of RNA by SNPs.

Authors:  Justin Ritz; Joshua S Martin; Alain Laederach
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

2.  Predicting cancer-associated germline variations in proteins.

Authors:  Pier Luigi Martelli; Piero Fariselli; Eva Balzani; Rita Casadio
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

3.  Automated extraction and semantic analysis of mutation impacts from the biomedical literature.

Authors:  Nona Naderi; René Witte
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

4.  Predict impact of single amino acid change upon protein structure.

Authors:  Christian Schaefer; Burkhard Rost
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

5.  Large-scale computational identification of regulatory SNPs with rSNP-MAPPER.

Authors:  Alberto Riva
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

6.  How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis.

Authors:  Mauno Vihinen
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

7.  On the effect of protein conformation diversity in discriminating among neutral and disease related single amino acid substitutions.

Authors:  Ezequiel Juritz; Maria Silvina Fornasari; Pier Luigi Martelli; Piero Fariselli; Rita Casadio; Gustavo Parisi
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

8.  Domain landscapes of somatic mutations in cancer.

Authors:  Nathan L Nehrt; Thomas A Peterson; DoHwan Park; Maricel G Kann
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

9.  Prioritization of pathogenic mutations in the protein kinase superfamily.

Authors:  Jose M G Izarzugaza; Angela del Pozo; Miguel Vazquez; Alfonso Valencia
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

10.  Disease-related mutations predicted to impact protein function.

Authors:  Christian Schaefer; Yana Bromberg; Dominik Achten; Burkhard Rost
Journal:  BMC Genomics       Date:  2012-06-18       Impact factor: 3.969

  10 in total
  5 in total

1.  SNP-SIG 2013: from coding to non-coding--new approaches for genomic variant interpretation.

Authors:  Yana Bromberg; Emidio Capriotti
Journal:  BMC Genomics       Date:  2014-05-20       Impact factor: 3.969

2.  VarI-SIG 2014--From SNPs to variants: interpreting different types of genetic variants.

Authors:  Yana Bromberg; Emidio Capriotti
Journal:  BMC Genomics       Date:  2015-06-18       Impact factor: 3.969

3.  VarI-SIG 2015: methods for personalized medicine - the role of variant interpretation in research and diagnostics.

Authors:  Yana Bromberg; Emidio Capriotti; Hannah Carter
Journal:  BMC Genomics       Date:  2016-06-23       Impact factor: 3.969

4.  Thoughts from SNP-SIG 2012: future challenges in the annotation of genetic variations.

Authors:  Yana Bromberg; Emidio Capriotti
Journal:  BMC Genomics       Date:  2013-05-28       Impact factor: 3.969

Review 5.  Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics.

Authors:  Alfonso Valencia; Manuel Hidalgo
Journal:  Genome Med       Date:  2012-07-30       Impact factor: 11.117

  5 in total

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