Titlebook: Computational Methods for 3D Genome Analysis; Ryuichiro Nakato Book 2025 The Editor(s) (if applicable) and The Author(s), under exclusive

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Feedback im Kontext von Potenzialanalysen, to detailed microscopic analysis. However, the circularity of prokaryotic genomes requires a number of tricks for Hi-C/3C-seq data analysis. Here, I provide a practical guide to use the HiC-Pro pipeline for Hi-C/3C-seq data obtained from prokaryotes.

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https://doi.org/10.1007/978-3-658-24760-7 mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome str

Prophylaxis

https://doi.org/10.1007/978-3-658-24760-7updated version that significantly improves extensibility, usability, and computational efficiency compared to its predecessor. It features pretrained models tailored for a wide range of experimental platforms, such as Hi-C, Micro-C, ChIA-PET, HiChIP, HiCAR, and TrAC-loop. This chapter offers a step

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https://doi.org/10.1007/978-3-658-24760-7d by data resolution constraints. Consequently, comprehensive characterizations of sub-compartments have been limited to a select number of Hi-C experiments, with systematic comparisons across a wide range of tissues and conditions still lacking. Our original Calder algorithm marked a significant ad

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https://doi.org/10.1007/978-3-658-08361-8sembles data analysis for bulk Hi-C, the unique challenges of scHi-C, such as high noise and protocol-specific biases, require specialized data processing strategies. In this tutorial chapter, we focus on using pairtools, a suite of tools optimized for scHi-C data, demonstrating its application on a