Dataset Information

DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels.

ABSTRACT: Micro-indels (insertions or deletions shorter than 21 bps) constitute the second most frequent class of human gene mutation after single nucleotide variants. Despite the relative abundance of non-frameshifting indels, their damaging effect on protein structure and function has gone largely unstudied. We have developed a support vector machine-based method named DDIG-in (Detecting disease-causing genetic variations due to indels) to prioritize non-frameshifting indels by comparing disease-associated mutations with putatively neutral mutations from the 1,000 Genomes Project. The final model gives good discrimination for indels and is robust against annotation errors. A webserver implementing DDIG-in is available at http://sparks-lab.org/ddig.

SUBMITTER: Zhao H

PROVIDER: S-EPMC4053752 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels.

Zhao Huiying H Yang Yuedong Y Lin Hai H Zhang Xinjun X Mort Matthew M Cooper David N DN Liu Yunlong Y Zhou Yaoqi Y

Genome biology 20130313 3

Micro-indels (insertions or deletions shorter than 21 bps) constitute the second most frequent class of human gene mutation after single nucleotide variants. Despite the relative abundance of non-frameshifting indels, their damaging effect on protein structure and function has gone largely unstudied. We have developed a support vector machine-based method named DDIG-in (Detecting disease-causing genetic variations due to indels) to prioritize non-frameshifting indels by comparing disease-associa ...[more]

PMID: 23497682

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Project description:BackgroundNon-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 substitutions (SASs) on protein stability based on a single folded structure. However, the native state of a protein is not unique and it is better represented by the ensemble of its conformers in dynamic equilibrium. The maintenance of the ensemble is essential for protein function. In this work we investigated how protein conformational diversity can affect the discrimination of neutral and disease related SASs based on protein stability estimations. For this purpose, we used 119 proteins with 803 associated SASs, 60% of which are disease related. Each protein was associated with its corresponding set of available conformers as found in the Protein Conformational Database (PCDB). Our dataset contains proteins with different extensions of conformational diversity summing up a total number of 1023 conformers.ResultsThe existence of different conformers for a given protein introduces great variability in the estimation of the protein stability (ΔΔG) after a single amino acid substitution (SAS) as computed with FoldX. Indeed, in 35% of our protein set at least one SAS can be described as stabilizing, destabilizing or neutral when a cutoff value of ±2 kcal/mol is adopted for discriminating neutral from perturbing SASs. However, when the ΔΔG variability among conformers is taken into account, the correlation among the perturbation of protein stability and the corresponding disease or neutral phenotype increases as compared with the same analysis on single protein structures. At the conformer level, we also found that the different conformers correlate in a different way to the corresponding phenotype.ConclusionsOur results suggest that the consideration of conformational diversity can improve the discrimination of neutral and disease related protein SASs based on the evaluation of the corresponding Gibbs free energy change.

Project description:The neutral theory of biodiversity constitutes a reference null hypothesis for the interpretation of ecosystem dynamics and produces relatively simple analytical descriptions of basic system properties, which can be easily compared to observations. On the contrary, investigations in non-neutral dynamics have in the past been limited by the complexity arising from heterogeneous demographic behaviours and by the relative paucity of detailed observations of the spatial distribution of species diversity (beta-diversity): These circumstances prevented the development and testing of explicit non-neutral mathematical descriptions linking competitive strategies and observable ecosystem properties. Here we introduce an exact non-neutral model of vegetation dynamics, based on cloning and seed dispersal, which yields closed-form characterizations of beta-diversity. The predictions of the non-neutral model are validated using new high-resolution remote-sensing observations of salt-marsh vegetation in the Venice Lagoon (Italy). Model expressions of beta-diversity show a remarkable agreement with observed distributions within the wide observational range of scales explored (5 x 10(-1) m divided by 10(3) m). We also consider a neutral version of the model and find its predictions to be in agreement with the more limited characterization of beta-diversity typical of the neutral theory (based on the likelihood that two sites be conspecific or heterospecific, irrespective of the species). However, such an agreement proves to be misleading as the recruitment rates by propagules and by seed dispersal assumed by the neutral model do not reflect known species characteristics and correspond to averages of those obtained under the more general non-neutral hypothesis. We conclude that non-neutral beta-diversity characterizations are required to describe ecosystem dynamics in the presence of species-dependent properties and to successfully relate the observed patterns to the underlying processes.

Dataset Information

DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels.

Publications

DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels.

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