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Titlebook: Spatial Genome Organization; Methods and Protocol Tom Sexton Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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书目名称Spatial Genome Organization
副标题Methods and Protocol
编辑Tom Sexton
视频video
概述Includes cutting-edge techniques.Provides step-by-step detail essential for reproducible results.Contains key implementation advice from the experts
丛书名称Methods in Molecular Biology
图书封面Titlebook: Spatial Genome Organization; Methods and Protocol Tom Sexton Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive lic
描述This detailed volume explores a variety of cutting-edge techniques used to interrogate spatial genome organization. Beginning with a section covering the vital chromosome conformation capture (3C) technique, this collection continues with chapters on targeted Hi-C approaches, sequencing-based approaches to assess nuclear environment, as well as single-cell technologies to better characterize the heterogeneity and dynamics of nuclear architectures and approaches to visualize them by microscopy. Finally, in order to be able to ask functional questions about the role of spatial chromatin organization in genomic control, the last section provides methods for acute manipulations of chromatin architecture. Written for the highly successful .Methods in Molecular Biology. series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. .Authoritative and practical, .Spatial Genome Organization: Methods and Protocols. is an ideal resource for researchers searching for the best techniques to address their own specific research ques
出版日期Book 2022
关键词Chromosome conformation capture; Chromatin architecture; Nuclear environment; Targeted Hi-C; Genome topo
版次1
doihttps://doi.org/10.1007/978-1-0716-2497-5
isbn_softcover978-1-0716-2499-9
isbn_ebook978-1-0716-2497-5Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Busines
The information of publication is updating

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Elena Slobodyanyuk,Claudia Cattoglio,Tsung-Han S. Hsiehcases. Recent research has explored using linked open datasets for handling exceptions. However, most research uses domain-dependent datasets developed by specific authorities. To handle legal rule exceptions, which typically need knowledge from various domains and need to be updated in response to
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search space. In recent years, machine learning methods such as deep learning have achieved remarkable results in various fields. Although machine learning methods depend on data sets, it is sometimes difficult to prepare good-quality data for some tasks, and unsupervised learning methods have also
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