基因组学中的草图和亚线性数据结构

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Annual Review of Biomedical Data Science Pub Date : 2019-07-20 DOI:10.1146/ANNUREV-BIODATASCI-072018-021156

G. Marçais, Brad Solomon, Robert Patro, Carl Kingsford

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引用次数: 35

摘要

大规模基因组学需要随着数据的增长而亚线性扩展的计算方法。我们回顾了基因组分析方法中使用的几种数据结构和绘制技术。具体来说，我们关注四个关键思想，它们采用不同的方法来实现次线性空间的使用和处理时间：压缩全文索引、近似成员查询数据结构、位置敏感哈希和最小化方案。我们对这些技术进行了高水平的描述，并给出了每种技术的几个代表性应用。

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Sketching and Sublinear Data Structures in Genomics

Large-scale genomics demands computational methods that scale sublinearly with the growth of data. We review several data structures and sketching techniques that have been used in genomic analysis methods. Specifically, we focus on four key ideas that take different approaches to achieve sublinear space usage and processing time: compressed full-text indices, approximate membership query data structures, locality-sensitive hashing, and minimizers schemes. We describe these techniques at a high level and give several representative applications of each.

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来源期刊

Annual Review of Biomedical Data Science

CiteScore

11.10

自引率

1.70%

发文量

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.