Genomics - Understanding human diversity

Duke University, Durham, North Carolina, United States
Nature (Impact Factor: 42.35). 11/2005; 437(7063):1241-2. DOI: 10.1038/4371241a
Source: PubMed

ABSTRACT The first edition of a massive catalogue of human genetic variation is now complete. The long-term task is to translate these data into an understanding of the effects of that variation on human health.

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    ABSTRACT: Motivation: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem of missing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a two-step algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes.
    Bioinformatics 04/2014; 30(16). DOI:10.1093/bioinformatics/btu297 · 4.62 Impact Factor
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    ABSTRACT: In this paper, we study methods for improving the efficiency and privacy of compressed DNA sequence comparison computations, under various querying scenarios. For instance, one scenario involves a querier, Bob, who wants to test if his DNA string, $Q$, is close to a DNA string, $Y$, owned by a data owner, Alice, but Bob does not want to reveal $Q$ to Alice and Alice is willing to reveal $Y$ to Bob \emph{only if} it is close to $Q$. We describe a privacy-enhanced method for comparing two compressed DNA sequences, which can be used to achieve the goals of such a scenario. Our method involves a reduction to set differencing, and we describe a privacy-enhanced protocol for set differencing that achieves absolute privacy for Bob (in the information theoretic sense), and a quantifiable degree of privacy protection for Alice. One of the important features of our protocols, which makes them ideally suited to privacy-enhanced DNA sequence comparison problems, is that the communication complexity of our solutions is proportional to a threshold that bounds the cardinality of the set differences that are of interest, rather than the cardinality of the sets involved (which correlates to the length of the DNA sequences). Moreover, in our protocols, the querier, Bob, can easily compute the set difference only if its cardinality is close to or below a specified threshold.
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    ABSTRACT: The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL . An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.
    Journal of integrative bioinformatics 01/2010; 7(3). DOI:10.2390/biecoll-jib-2010-138