基因组数据压缩

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2019-07-20 DOI:10.1146/ANNUREV-BIODATASCI-072018-021229
M. Hernaez, Dmitri S. Pavlichin, T. Weissman, Idoia Ochoa
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引用次数: 22

摘要

最近,由于测序技术的进步,人们对基因组测序在效率和可负担性方面的兴趣越来越大。这些发展使许多人能够将全基因组测序视为个性化医疗和公共卫生的宝贵工具。因此,正在生成越来越大且普遍存在的基因组数据集。这对这些数据的存储和传输提出了重大挑战。现在,将基因组数据存储十年的成本已经高于最初获得数据的成本。这种情况要求有效地表示基因组信息。在这篇综述中,我们强调了根据基因组数据设计专用压缩机的必要性,并描述了已经提出的主要解决方案。我们还给出了存储这些数据的一般指南,并总结了我们对基因组格式和压缩器未来的想法。
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Genomic Data Compression
Recently, there has been growing interest in genome sequencing, driven by advances in sequencing technology, in terms of both efficiency and affordability. These developments have allowed many to envision whole-genome sequencing as an invaluable tool for both personalized medical care and public health. As a result, increasingly large and ubiquitous genomic data sets are being generated. This poses a significant challenge for the storage and transmission of these data. Already, it is more expensive to store genomic data for a decade than it is to obtain the data in the first place. This situation calls for efficient representations of genomic information. In this review, we emphasize the need for designing specialized compressors tailored to genomic data and describe the main solutions already proposed. We also give general guidelines for storing these data and conclude with our thoughts on the future of genomic formats and compressors.
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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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