TRANSLATION OF AUTOMATED SEQUENCER DATA TO DNA SEQUENCES

自动测序仪数据到 DNA 序列的翻译

• 批准号: 2208899
• 负责人: Clark Tibbetts
• 金额: $ 23.96万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-02-01 至 1995-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2208899
• 关键词: DNA; artificial intelligence; automated data processing; bioengineering /biomedical engineering; computer assisted sequence analysis; computer program /software; computer system design /evaluation; genome; human genetic material tag; image processing; nucleic acid sequence

The objective of this proposal is to significantly improve automated determination of DNA sequences. Practical performance limits of automated DNA sequencers are determined by the separation of oligonucleotides effected by polyacrylamide gel electrophoresis. Designs of contemporary instruments are basically similar. As oligomers in a DNA sequencing ladder pass the detector(s), multi-component analysis specifies the radioactive or fluorescent label associated with each oligomer. Under ideal conditions, determination of the sequence of terminal nucleotides is straightforward. When separations of oligomers or signal levels are not optimal, ambiguities or errors are likely. These are miscalled bases, extra or missing bases, or unidentified bases in the DNA sequence file, typically at about 1 to 3 errors per 100 bases. An error rate near 1% is a common target for DNA sequencing performance, since comparison with complementary strand sequence data should then reduce errors to about 1 per 10,000 base pairs. This is only possible if every mismatch of the sequence and its complement is identified and correctly reconciled. Even then, error rates from 0.01% to 0.1% approximate the variation among alleles in a gene pool: some such alleles can correlate with severe burdens of inherited pathology. Small improvements in single strand error rate will have substantial impact on quality of finished sequences from 1/10,000 bp to 1/1,000,000 bp. Improvements are needed if automated systems are to provide longer spans of DNA sequences with fewer errors. The emphasis of this proposal is on raw data acquisition and new methods for translation of the raw data to finished DNA sequences. An expert system, rule-based method will be developed to reinforce conventional translation of raw data to DNA sequences. An independent, pattern-recognition system will also be developed and tested, using techniques for construction and training of neural nets. We will also evaluate two new approaches to utilize single label, single data channels for more efficient determination of DNA sequences. Alternative approaches to oligonucleotide separation for sequence analysis will also be investigated. In pursuit of these specific aims we will take advantage of the relative separations and intensities of successive oligomers in DNA sequencing ladders, as independent determinants of DNA sequence-specific data stream patterns.

该提案的目的是大大提高自动化 确定DNA序列。 确定了自动DNA测序仪的实际性能极限 通过聚丙烯酰胺凝胶分离寡核苷酸 电泳 现代乐器的设计基本上是 相似 当DNA测序梯中的寡聚体通过检测器时， 多组分分析指定放射性或荧光标记 与每一个低聚物相关联。 在理想条件下， 末端核苷酸的序列是简单的。 当 低聚物或信号水平的分离不是最佳的，不明确 或错误可能。 这些都是错误的碱基，额外的或缺失的碱基， 或DNA序列文件中未鉴定的碱基，通常在约1至 每100个碱基3个错误。 接近1%的错误率是DNA测序性能的常见目标， 因为与互补链序列数据比较应该 将错误减少到每10，000个碱基对中约1个。 这是唯一可能 如果序列及其互补序列的每个错配都被识别， 正确的和解。 即使这样，错误率从0.01%到0.1% 基因库中等位基因之间的近似变化;某些等位基因 可能与严重的遗传病理负担有关。 小 单链错误率的改善将对 成品序列的质量从1/10，000 bp到1/1，000，000 bp。 如果自动化系统要提供更长的跨度， 错误更少的DNA序列。 这项建议的重点是 原始数据采集和将原始数据转换为 完成DNA序列 一个专家系统，基于规则的方法将被开发，以加强 将原始数据转换为DNA序列的传统翻译。 一个独立的， 模式识别系统也将开发和测试，使用 构造和训练神经网络的技术。 我们还将 评估两种利用单标签、单数据通道的新方法 以更有效地确定DNA序列。 替代 用于序列分析的寡核苷酸分离方法也将 追究 为了实现这些具体目标，我们将 的相对分离和连续的强度的优势 DNA测序梯中的寡聚体，作为DNA的独立决定簇 序列特定的数据流模式。