[Elsnet-list] CFP- ECCB 2008 Workshop: Annotation,
interpretation and management of Mutations (AIMM)- call for papers
Christopher J. O. Baker
cbaker at i2r.a-star.edu.sg
Tue Jun 3 17:26:52 CEST 2008
ECCB 2008 Workshop: Annotation, interpretation and management of Mutations (AIMM)- call for papers
Genetic variability (SNPs, mutations) plays a key role in the analysis of genetic mechanisms and complex diseases. The effort in the gathering and understanding of the information regarding human variability is large, e.g. data collection from the 1000 genomes project. In step with such initiatives this workshop will focus on solutions in text mining, data warehousing and machine learning that allow better integration of mutation relevant information into a bioinformatics infrastructure. Altogether, the meeting participants will discuss the methods for the prediction of phenotypic effects induced by mutations, support to clinical decision processes involving mutations and the means that allow access and management of mutations with annotations from different data resources. Synergistic use of these technologies should facilitate inference of knowledge from sequence to structure to function and to phenotypes. The workshop brings together members of different disciplines to improve know-how and technology transfer as well as better hypothesis generation for yet un-annotated mutations.
Leibniz-Institut f. Molekulare Pharmakologie, Berlin, DE
In Collaboration with Tom Blundell, Cambridge, UK
Kevin B. Cohen,
The Hunter Lab, Center for Computational Pharmacology
University of Colorado Health Sciences Center, Colorado, US
This workshop reaches out to the following participants: data architects working on data modeling and knowledge representation, data warehouse curators seeking to address a backlog of un-curated mutations from the literature, designers of mining solutions and services for unstructured text, machine learning specialists developing classifiers for predictive analyses, structural biologists involved in protein engineering and physicians involved in genome scale population studies. The workshop will consist of keynote talks reporting on the management of SNP data and giving better insights on the importance of non-synonymous SNPs on diseases.
We invite both long (4000 words / 12 pages) and short papers (2000 words / 6 pages) on the topics listed below. Manuscript preparation and formatting instructions are available at the following website http://www.ebi.ac.uk/Rebholz-srv/aimm-instructions.html <http://www.ebi.ac.uk/Rebholz-srv/aimm-instructions.html>
We encourage proposals for workshop presentations that describe:
* Infrastructures and metadata to support archival and study of perturbations (mutations) and variations (SNPs) within biological systems.
* Integration of mutation-related data into systems level analysis of a biological sciences (disease, biomarkers, clinical, metabolomics).
* Novel techniques (information extraction, machine learning and others solutions) for the extraction of mutations and generation of annotations from the scientific literature.
* Tools and analyses predicting the impacts of mutations
* Hypothesis generation and reuse: building the derived insights of mutational studies into biological models
Submissions can be made through the EasyChair submission page: http://www.easychair.org/conferences?conf=aimm2008 <http://www.easychair.org/conferences?conf=aimm2008>
All papers will be published in an online workshop proceedings with CEUR
Long papers may be invited to submit extended versions to a journal special issue.
Christopher J. O. Baker , PhD
Data Mining Department
Institute for Infocomm Research
21 Heng Mui Keng Terrace, Singapore 119613
Tel: +65 6874 3495
Email: cbaker at i2r.a-star.edu.sg
Dietrich Rebholz-Schuhmann, MD, PhD
Research Group Leader
European Bioinformatics Institute
Wellcome Trust Genome Campus
Hinxton, Cambridge, CB101SD,
Tel: +44 (0)1223 492 594
Email: Rebholz at ebi.ac.uk
Paper Submission: July 7th
Acceptance: August 4th
Final Manuscripts: August 18th
Workshop: Monday, September 22, 2008
Venue: ECCB2008 @ Cagliari, Sardinia - Italy
Mutation Databases and Metadata: Design, Content, Accuracy.
Over 400 mutation databases can be found with a 'google' search. Many are no longer maintained and cover very specific data sets. These repositories have been designed to support a wide range of features from SNPs, point mutations, insertions, deletions, and observed phenotypes. Furthermore they incorporate a wide range of modified protein features and metrics in the accompanying annotations to the mutation descriptions. In the main these databases are manually curated however mutation annotations are frequently inaccurate e.g. in the PDB, inaccurate to the degree of 40 % of all PDB records.
Extraction of mutations and annotations from literature:
AI techniques such as text mining and natural language processing have been used to enable . A number of systems have been developed generally showing that extraction mutations from texts can be achieved with high levels of precision and recall. These systems remain prototype scale their adoption to measure the accuracy, recreation and update of existing mutation databases as we as their incorporation into semi manual annotation pipelines is the next milestone. In addition there is continuing discussion over the appropriate metrics for individual tasks within these systems which requires community involvement. This emergent technology now needs standardization.
Predicting the impacts of Mutations:
The ability to predict the impact of a mutation or the consequence of a sequence variant is central to the diagnosis of genetic diseases. Non-synonymous mutations may impact translational regulation, mRNA stability, mRNA splicing and rates of translation. Proteins affected by nsSNPs may have altered; catalytic sites, stability, ability to aggregate, and or posttranslational modifications. Moving from SNP to sequence to structure and function has been addressed with varying degrees of accuracy with sequence and structure based (molecular mechanism, empirical energy function or machine learning) methods. The need to apply such techniques at a genome scale requires that robust approaches are identified, benchmarked with standard metrics in order to assign valid significance to ns mutations. Reuse of existing mutation databases for checking quality of predictions is pivotal.
Mutation Data Integration and Reuse: Panel Discussion
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