Genome-wide prediction and analysis of coding variants

描述(由申请人提供)：识别与疾病相关的遗传变异是将序列数据与改善人类健康的新方法联系起来的重要步骤。在目前已知的与人类疾病直接相关的序列变体中，57%是基于相应蛋白质中编码单个非同义氨基酸替代的突变。与疾病相关的另外23%的变异是由于基因的小插入和小缺失(Indels)。因此，人类健康中的一个重要问题是识别编码变异，SNPs和Indels，它们影响蛋白质功能，并可能与疾病相关。为此，我们开发了SIFT算法，这是一种预测氨基酸取代是否会影响蛋白质功能的算法。该算法可从SIFT网站(http://blocks.fhcrc.org/sift/SIFT.html)，)获得，被研究界广泛使用，并经常被用作类似预测算法的基准。SIFT和其他类似工具的流行强调了分析编码变体并确定它们中哪些最有可能产生表型效应的优先顺序的必要性。此外，DNA测序技术的进步正在产生大量的变异，包括SNPs和Indels，它们将需要分析。我们建议通过开发一种算法来扩展SIFT，该算法将预测哪些小编码INDel影响蛋白质功能，从而可能参与疾病。此外，我们建议增强SIFT对编码变体进行大规模分析的能力。这些新功能将被整合到SIFT网络服务器中，以实现全基因组分析。可执行文件和代码也将免费提供给研究界。DNA测序技术的最新进展正在产生大量的遗传变异，需要进行分析。小插入和小缺失(Indels)是常见的变异类型，23%的与疾病相关的变异是由Indels引起的。为了更好地识别疾病变体，我们建议扩展我们现有的算法SIFT，使其能够预测影响蛋白质功能的小编码Indels。此外，我们建议增强SIFT对编码变体进行大规模分析的能力。

DESCRIPTION (provided by applicant): The identification of genetic variants that are associated with disease is an important step in linking sequence data with new approaches to improve human health. Among the sequence variants currently known to be directly linked with human disease, 57% are based on mutations that encode a single nonsynonymous amino acid substitution in the corresponding protein. An additional 23% of variants linked with disease are due to small insertions and deletions (indels) in genes. Therefore, an important problem in human health is the identification of coding variants, SNPs and indels, which affect protein function and might be associated with disease. To this end, we developed SIFT, an algorithm that predicts if an amino acid substitution affects protein function. This algorithm, available at the SIFT website (http://blocks.fhcrc.org/sift/SIFT.html), is widely used by the research community and is often used as a benchmark for similar prediction algorithms. The popularity of SIFT and other similar tools emphasizes the need to analyze coding variants and prioritize which amongst them are most likely to have a phenotypic effect. Moreover, large numbers of variants, including SNPs and indels, are being generated by advances in DNA sequencing technologies and they will require analysis. We propose to expand SIFT by developing an algorithm that will predict which small coding indels affect protein function and hence may be involved in disease. In addition, we propose to enhance the ability of SIFT to perform large-scale analysis for coding variants. These new features will be incorporated into the SIFT web server to enable genome-wide analysis. Executables and code will also be made freely available to the research community. Recent advances in DNA sequencing technologies are generating large numbers of genetic variation that necessitate analysis. Small insertions and deletions (indels) are common types of variation and 23% of variants linked with disease are due to indels. In order to improve identification of disease variants, we propose to expand our existing algorithm SIFT by enabling the prediction of small coding indels that affect protein function. In addition, we propose to enhance the ability of SIFT to perform large-scale analysis for coding variants.