Volume 14 Supplement 3
SNP-SIG 2012: Identification and annotation of SNPs in the context of structure, function, and disease
Research and reviews
Edited by Yana Bromberg and Emidio Capriotti
This supplement has not been sponsored. The source of funding used to cover open access publication charges is declared by the authors in each article.
SNP-SIG 2012: Identification and annotation of SNPs in the context of structure, function, and disease. Go to conference site.
Long Beach, CA, USA14 May 2012
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Citation: BMC Genomics 2013 14(Suppl 3):S1
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Collective judgment predicts disease-associated single nucleotide variants
In recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated ...
Citation: BMC Genomics 2013 14(Suppl 3):S2 -
Identifying Mendelian disease genes with the Variant Effect Scoring Tool
Whole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of spec...
Citation: BMC Genomics 2013 14(Suppl 3):S3 -
The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations
Understanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic....
Citation: BMC Genomics 2013 14(Suppl 3):S4 -
A protein domain-centric approach for the comparative analysis of human and yeast phenotypically relevant mutations
The body of disease mutations with known phenotypic relevance continues to increase and is expected to do so even faster with the advent of new experimental techniques such as whole-genome sequencing coupled w...
Citation: BMC Genomics 2013 14(Suppl 3):S5 -
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation
SNPs&GO is a method for the prediction of deleterious Single Amino acid Polymorphisms (SAPs) using protein functional annotation. In this work, we present the web server implementation of SNPs&GO (WS-SNPs&GO)....
Citation: BMC Genomics 2013 14(Suppl 3):S6 -
Assessment of computational methods for predicting the effects of missense mutations in human cancers
Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most...
Citation: BMC Genomics 2013 14(Suppl 3):S7 -
Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers
Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial divers...
Citation: BMC Genomics 2013 14(Suppl 3):S8 -
Multiscale modeling of the causal functional roles of nsSNPs in a genome-wide association study: application to hypoxia
It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establi...
Citation: BMC Genomics 2013 14(Suppl 3):S9 -
GWIS - model-free, fast and exhaustive search for epistatic interactions in case-control GWAS
It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently ...
Citation: BMC Genomics 2013 14(Suppl 3):S10 -
Pathway analysis of genome-wide data improves warfarin dose prediction
Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be diff...
Citation: BMC Genomics 2013 14(Suppl 3):S11
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