With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.
Keywords: 3D, three dimensional; ATAC-seq, assay for transposase-accessible chromatin using sequencing; CTCF, CCCTC-binding factor; ChIA-PET, chromatin interaction analysis by paired-end tag sequencing; ChIP-seq, chromatin immunoprecipitation sequencing; Chromatin architectures; Computational methods; ICE, iterative correction; MNase, micrococcal nuclease; MPE-seq, methidiumpropyl-EDTA sequencing; Mb, megabases; Micro-C, micrococcal nuclease applied chromosome conformation capture; Nucleosome; PCA, principle component analysis; PF, pioneer factor; TAD, topologically associating domain; TCC, tethered chromosome conformation capture; TF, transcription factor.
© 2022 The Author(s).