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

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

Authors: Yana Bromberg and Emidio Capriotti

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

Prediction methods are increasingly used in biosciences to forecast diverse features and characteristics. Binary two-state classifiers are the most common applications. They are usually based on machine learni...

Authors: Mauno Vihinen

Prioritization of pathogenic mutations in the protein kinase superfamily

Most of the many mutations described in human protein kinases are tolerated without significant disruption of the corresponding structures or molecular functions, while some of them have been associated to a v...

Authors: Jose MG Izarzugaza, Angela del Pozo, Miguel Vazquez and Alfonso Valencia

Predict impact of single amino acid change upon protein structure

Amino acid point mutations (nsSNPs) may change protein structure and function. However, no method directly predicts the impact of mutations on structure. Here, we compare pairs of pentamers (five consecutive r...

Authors: Christian Schaefer and Burkhard Rost

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

Non-synonymous coding SNPs (nsSNPs) that are associated to disease can also be related with alterations in protein stability. Computational methods are available to predict the effect of single amino acid subs...

Authors: Ezequiel Juritz, Maria Silvina Fornasari, Pier Luigi Martelli, Piero Fariselli, Rita Casadio and Gustavo Parisi

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

The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into ...

Authors: Justin Ritz, Joshua S Martin and Alain Laederach

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

The computational analysis of regulatory SNPs (rSNPs) is an essential step in the elucidation of the structure and function of regulatory networks at the cellular level. In this work we focus in particular on ...

Authors: Alberto Riva

Predicting cancer-associated germline variations in proteins

Various computational methods are presently available to classify whether a protein variation is disease-associated or not. However data derived from recent technological advancements make it feasible to exten...

Authors: Pier Luigi Martelli, Piero Fariselli, Eva Balzani and Rita Casadio

Domain landscapes of somatic mutations in cancer

Large-scale tumor sequencing projects are now underway to identify genetic mutations that drive tumor initiation and development. Most studies take a gene-based approach to identifying driver mutations, highli...

Authors: Nathan L Nehrt, Thomas A Peterson, DoHwan Park and Maricel G Kann

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

Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in variou...

Authors: Nona Naderi and René Witte

Disease-related mutations predicted to impact protein function

Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study pred...

Authors: Christian Schaefer, Yana Bromberg, Dominik Achten and Burkhard Rost