Developing novel statistical methodology incorporating biological structure for high-throughput genomic data analysis

开发结合生物结构的新型统计方法以进行高通量基因组数据分析

• 批准号: G0701810/2
• 负责人: Christopher Yau
• 金额: $ 2.31万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0701810%2F2
• 关键词: Developing; novel; statistical; methodology; incorporating

The tools of modern genetics now enable scientists to explore and probe the human genome at unprecedented levels of scale and detail. Miniaturisation and parallelisation now enable geneticists to perform many thousands of experiments on a single chip that can fit into the palm of the hand. However, geneticists are now faced with unprecedented quantities of data that defy human efforts to analyse and interpret them. Advanced computational based methods of analysis offer a solution but existing algorithms are frequently based on ad-hoc heuristics and fail to incorporate biological knowledge and structure that a human interpreter might. This can lead to many discoveries that are subsequently found to be false in follow-up studies - a process which is highly wasteful of time and resources. This study seeks to meet the challenges posed by modern genetics by developing formal statistical methods that are able to handle massive data sets, exploit known biological structure, and assist in the decision making process of clinicians and experimental scientists. The use of sophisticated data analysis methods with advanced genomic technologies will minimise false discoveries and allow efforts to be focused on those discoveries that may then lead to furthering the understanding of human disease.

现代遗传学的工具现在使科学家能够以前所未有的规模和细节来探索和探测人类基因组。现在，微型化和并行化使遗传学家能够在一块可以放在手掌中的芯片上进行数千次实验。然而，遗传学家现在面临着史无前例的大量数据，这些数据违背了人类分析和解释它们的努力。先进的基于计算的分析方法提供了一种解决方案，但现有的算法往往基于特定的启发式算法，无法纳入人类翻译可能具有的生物学知识和结构。这可能导致许多发现，但随后在后续研究中被发现是错误的--这是一个非常浪费时间和资源的过程。这项研究试图通过开发能够处理海量数据集、利用已知生物结构并帮助临床医生和实验科学家决策过程的正式统计方法来应对现代遗传学带来的挑战。将复杂的数据分析方法与先进的基因组技术结合使用，将最大限度地减少错误发现，并使人们能够将精力集中在这些发现上，从而可能导致对人类疾病的进一步了解。