TRANSLATION OF AUTOMATED SEUENCER DATA TO DNA SEQUENCES

自动测序仪数据到 DNA 序列的翻译

• 批准号: 2208900
• 负责人: Clark Tibbetts
• 金额: $ 26.74万
• 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 1997-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2208900
• 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 primary objective of this research is to improve automated analysis of gel-based DNA sequencing ladders, through pattern recognition-based translation of raw instrument data to DNA sequences. We emphasize neural networks, adapted to particular sequencing conditions and instruments. The performances of pattern recognition and conventional basecalling software will be evaluated: (1) as experimental errors challenge description of the natural allelic diversity of human adenoviral genomes; (2) for detection and specification of heterozygous loci in diploid template experiments; (3) for primer selection and assembly operations of large scale sequencing projects. Distributions of basecalling errors will be analyzed in the contexts of neighboring nucleotide identities and as results of different sequencing strategies. Three principal advantages are expected from pattern recognition basecalling software: (1) analysis of contextual arrays of oligomer traces improves basecalling accuracy; (2) specifically tasked, neural network and algorithmic processors support on-line signal conditioning and basecalling in real time; and (3) the signal conditioning and pattern recognition modules support objective measures of confidence for each basecall. This project will significantly and positively impact progress towards the stated goals of the human genome initiative. No incremental costs for hardware or strategic modifications are required. Cost savings can be realized through automation of labor intensive review and editing of primary data. Real-time basecalling supports higher throughput instruments, exploiting faster separation of larger parallel arrays of sequencing ladders. Objective basecall confidence parameters support overlap assignment during sequence assembly, and should facilitate sequence - match searches through expanding databases.

本研究的主要目的是提高自动化分析， 基于凝胶的DNA测序梯，通过基于模式识别的 将原始仪器数据转换为DNA序列。我们强调神经 网络，适合特定的测序条件和仪器。的 模式识别和传统碱基识别软件的性能 将被评估：（1）作为实验错误的挑战描述的 人类腺病毒基因组的天然等位基因多样性;（2）用于检测 和二倍体模板实验中杂合基因座的指定; (3)用于大规模测序的引物选择和组装操作 项目碱基判定错误的分布将在 相邻核苷酸身份的背景和不同的结果 排序策略。 模式识别有三个主要优点 碱基判定软件：（1）寡聚物痕迹的上下文阵列的分析 提高碱基判定的准确性;（2）具体任务，神经网络和 算法处理器支持在线信号调节和基本调用 真实的时间;（3）信号调理和模式识别 模块支持每个基本判定的置信度的客观度量。 这一项目将对实现千年发展目标的进展产生重大和积极的影响。 人类基因组计划的目标。没有增加费用， 需要进行硬件或战略性修改。成本节约可以 通过劳动密集型审查和编辑的自动化实现， 主数据.实时碱基判定支持更高的吞吐量 仪器，利用更快的分离较大的平行阵列， 阶梯排序客观basecall置信参数支持 在序列组装过程中重叠分配，并应便于 通过扩展数据库进行序列匹配搜索。