Molecular Heterogeneity in Large-Scale Biological Data: Techniques and Applications

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
C. Deng, Timothy P. Daley, G. Brandine, Andrew D. Smith
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引用次数: 5

Abstract

High-throughput sequencing technologies have evolved at a stellar pace for almost a decade and have greatly advanced our understanding of genome biology. In these sampling-based technologies, there is an important detail that is often overlooked in the analysis of the data and the design of the experiments, specifically that the sampled observations often do not give a representative picture of the underlying population. This has long been recognized as a problem in statistical ecology and in the broader statistics literature. In this review, we discuss the connections between these fields, methodological advances that parallel both the needs and opportunities of large-scale data analysis, and specific applications in modern biology. In the process we describe unique aspects of applying these approaches to sequencing technologies, including sequencing error, population and individual heterogeneity, and the design of experiments.
大规模生物学数据中的分子异质性:技术与应用
近十年来,高通量测序技术以惊人的速度发展,极大地促进了我们对基因组生物学的理解。在这些基于抽样的技术中,有一个重要的细节在数据分析和实验设计中经常被忽视,特别是抽样观察结果通常不能给出潜在人群的代表性图像。这早已被认为是统计生态学和更广泛的统计文献中的一个问题。在这篇综述中,我们讨论了这些领域之间的联系,平行于大规模数据分析的需求和机会的方法进展,以及在现代生物学中的具体应用。在此过程中,我们描述了将这些方法应用于测序技术的独特方面,包括测序误差,群体和个体异质性以及实验设计。
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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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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