STATISTICAL ANALYSIS OF LONG DNA SEQUENCES

长 DNA 序列的统计分析

• 批准号: 6107290
• 负责人: GABRIEL A SCHACHTEL
• 金额: $ 5.68万
• 依托单位: UNIVERSITY OF TEXAS SAN ANTONIO
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-01 至 1999-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6107290
• 关键词: DNA; Herpesviridae; biotechnology; computer assisted sequence analysis; computer program /software; computer system design /evaluation; genetic markers; genetic transcription; genome; mathematical model; nucleic acid repetitive sequence; nucleic acid sequence; nucleosomes; protein folding; statistics /biometry

Rapid accumulation of genomic sequences has increased the demand for methods to decipher the genetic information gathered in data banks such as GenBank. While many methods for a thorough micro-analysis of small sequences have been developed in the past, there is still a shortage of powerful procedures for macro-analyses of large DNA sequences. Combining statistical analysis and modern computer power makes it feasible to search with high speed for diagnostic patterns within long sequences and to evaluate similarities and differences between them in order to recover much of the biochemical information hidden in these organic molecules. The objectives of the proposed study are to develop novel methods of computerized statistical analysis and simultaneously apply them to analyze available large genomic sequences, as the genomes of the medically important herpesvirus family. The methods aim at the identification of biologically active sites (e.g. origins replication, and nucleosome positioning signals). The anticipated approach is based on an automated scanning of long DNA letter sequences and to find similarities or differences between sequences, e.g., by using dinucleotide distance measures. Probabilistic criteria (as r-scan and spectral envelopes) will be applied to all detected sites of possible biological interest to sort out the most likely candidates among them. This project seeks close collaboration between experimental and theoretical scientists, namely virologists and mathematicians. The interdisciplinary approach ensures that the theoretical work is linked to current biomedical problems and that newly developed methods are immediately applied.

基因组序列的快速积累增加了对 破译数据库中收集的遗传信息的方法， 与GenBank 虽然有许多方法可以对小的 过去已经开发了序列，但仍然缺乏 强大的宏观分析程序的大DNA序列。 结合 统计分析和现代计算机能力使搜索成为可能 在长序列内具有高速诊断模式， 评估它们之间的相似性和差异，以便恢复更多 隐藏在这些有机分子中的生化信息。 这项研究的目的是开发新的方法， 计算机化的统计分析，并同时应用它们来分析 可用的大基因组序列，作为医学上的 重要疱疹病毒家族。 该方法旨在鉴定生物活性位点（例如， 复制起点和核小体定位信号）。 预期 该方法基于长DNA字母序列的自动扫描， 寻找序列之间的相似性或差异，例如，通过使用 二核苷酸距离测量。 概率标准（如r扫描和 光谱包络）将应用于所有检测到的可能 生物学兴趣，从中挑选出最有可能的候选人。 该项目寻求实验和理论之间的密切合作 科学家，即病毒学家和数学家。 跨学科 这种方法确保了理论工作与当前的生物医学 新开发的方法立即得到应用。