Data acquisition, data storage, and data presentation in a modern genetics laboratory.

Reviews in immunogenetics Pub Date : 2000-01-01
D E Geraghty, S Fortelny, B Guthrie, M Irving, H Pham, R Wang, R Daza, B Nelson, J Stonehocker, L Williams, Q Vu
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Abstract

Modern genetic analysis can be divided into three main areas of investigation. The first is data acquisition, in the form of genomic sequence and the cataloguing of polymorphism data of the single nucleotide polymorphism variety (so called SNPs). Once identified, such genetic information can be adapted into high throughput tests to examine genetic information in large populations, making the analysis of sufficiently large numbers both cost and time effective so that relatively low-penetrant genetic effects can be accurately detected. The third step is correlating variation with phenotype (e.g. disease susceptibility or resistance) for a variety of disorders is paramount in our motivation and indeed is a common goal of modern human genetic analysis. While the technology to acquire vast amounts of genetic data is now well established and continues to expand, the ability to deal with such data, from the process of acquisition, storage, and analysis depends fundamentally on a solid informatics infrastructure as an essential component. Indeed, most of the major gains in productivity in this field are to be realized on the informatics front, and involve automating data acquisition, defining and sorting data in databases for quality control and analysis and facilitating access to data for the large variety of data analyses. Informatics-related issues including those relating to data acquisition, database structure, and analysis tools are summarized here in an effort to define some of the issues relevant to establishing informatics infrastructure in a small genetics laboratory focused on resequencing human immune response genes. From inherited diseases to drug efficacy to the specific genetic changes occurring during tumor development, this new field of medical genetics promises a profound impact on the state of human health. Ultimately, any and all advances in this field will continue to depend on major investments in informatics.

现代遗传学实验室的数据采集、数据存储和数据呈现。
现代遗传分析可分为三个主要的研究领域。首先是数据获取,以基因组序列和单核苷酸多态性(snp)的多态性数据编目的形式进行。一旦确定,这种遗传信息可用于高通量测试,以检查大群体中的遗传信息,使分析足够大的数量既节省成本又节省时间,从而可以准确地检测到相对低渗透的遗传效应。第三步是将各种疾病的变异与表型(例如疾病易感性或抗性)联系起来,这在我们的动机中是至关重要的,而且确实是现代人类遗传分析的共同目标。虽然获取大量遗传数据的技术现在已经建立并继续扩展,但从获取、存储和分析过程中处理这些数据的能力从根本上取决于作为重要组成部分的坚实信息学基础设施。事实上,这一领域生产力的大多数重大进步将在信息学方面实现,包括自动化数据获取、在数据库中定义和分类数据以进行质量控制和分析以及便利获取数据以进行各种数据分析。信息学相关的问题,包括与数据采集、数据库结构和分析工具相关的问题,在这里总结了一些与在一个专注于人类免疫反应基因重测序的小型遗传学实验室中建立信息学基础设施相关的问题。从遗传疾病到药物疗效再到肿瘤发展过程中发生的特定基因变化,这一新的医学遗传学领域有望对人类健康状况产生深远的影响。最终,这一领域的任何和所有进展都将继续依赖于信息学方面的重大投资。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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