Genome-wide prediction and analysis of coding variants

编码变体的全基因组预测和分析

• 批准号: 7681317
• 负责人: Sean Murphy
• 金额: $ 29.82万
• 依托单位: J. CRAIG VENTER INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7681317
• 关键词: Accounting; Affect; Algorithms; Amino Acid Substitution; Amino Acids; Benchmarking; Biochemical; Caucasians; Caucasoid Race; Code; Communities; Computer software; DNA; DNA Sequence; Data; Data Set; Databases; Development; Diploidy; Disease; Gene Frequency; Gene Mutation; Genes; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Health; Human; Internet; Lead; Link; Location; Methods; Metric; Minor; Missense Mutation; Mutation; Natural Selections; Output; Process; Property; Protein Region; Proteins; Research; Research Personnel; Sampling; Single Nucleotide Polymorphism; Sorting - Cell Movement; Structure; Technology; Variant; base; computer based statistical methods; genetic variant; genome wide association study; genome-wide; genome-wide analysis; high throughput analysis; human disease; improved; insertion/deletion mutation; interest; novel strategies; protein function; theories; tool; web site

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.

描述（由申请人提供）：与疾病相关的遗传变异的鉴定是将序列数据与改善人类健康的新方法联系起来的重要一步。在当前已知与人类疾病直接相关的序列变体中，有57％基于对相应蛋白质中单个非同义氨基酸取代的突变。与疾病相关的变体中有23％是由于基因中的插入和缺失（indels）引起的。因此，人类健康中的一个重要问题是鉴定编码变体，SNP和Indels，它影响蛋白质功能，可能与疾病有关。为此，我们开发了SIFT，这是一种预测氨基酸取代是否影响蛋白质功能的算法。该算法可在SIFT网站（http://blocks.fhcrc.org/sift/sift.html）上获得，该算法被研究社区广泛使用，通常用作类似预测算法的基准。 SIFT和其他类似工具的受欢迎程度强调了分析编码变体的必要性，并确定其中哪些最有可能具有表型效应。此外，DNA测序技术的进步正在产生大量的变体，包括SNP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型编码会影响蛋白质功能，因此可能参与疾病。此外，我们建议提高SIFT对编码变体进行大规模分析的能力。这些新功能将被合并到SIFT Web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社区。 DNA测序技术的最新进展正在产生大量需要分析的遗传变异。小插入和缺失（indels）是常见的变异类型，与疾病相关的变异中有23％是由于indels引起的。为了改善疾病变异的识别，我们建议通过启用影响蛋白质功能的小型编码indels来扩展现有算法筛分。此外，我们建议提高SIFT对编码变体进行大规模分析的能力。