4DNvestigator: time series genomic data analysis toolbox

Abstract

Data on genome organization and output over time, or the 4D Nucleome (4DN), require synthesis for meaningful interpretation. Development of tools for the efficient integration of these data is needed, especially for the time dimension. We present the ‘4DNvestigator’, a user-friendly network-based toolbox for the analysis of time series genome-wide genome structure (Hi-C) and gene expression (RNA-seq) data. Additionally, we provide methods to quantify network entropy, tensor entropy, and statistically significant changes in time series Hi-C data at different genomic scales.

Functional organization of the maternal and paternal human 4D Nucleome

“…This approach is useful for quantifying differences between maternal and paternal genomes throughout the cell cycle, highlighting gene sets with large structural or expression differences over time. In previous work, we have also shown that this method may be broadly applicable to time-series analysis of different cell types ( Lindsly et al., 2021 ).…”

“…These matrices represent the local genomic structure of the differentially expressed genes, and are slightly smaller than average TAD size ( ∼ 1 Mb). We then compared the correlation matrices of the log 2 -transformed local Hi-C data and determined whether or not the matrices have statistically significant differences (p < 0.05) ( Koziol et al., 1997 ; Lindsly et al., 2021 ). We applied this comparison to all genes that were differentially expressed in RNA-seq (Bru-seq) and found that 515 (403) genes had at least one comparison in which both the expression and local structure had significantly changed.…”

“…In network science, the relative importance of a node in a network is commonly determined using network centrality ( Newman, 2018 ). For Hi-C data, we consider genomic loci as nodes and use network centrality to measure the importance of each locus at each cell cycle phase ( Liu et al., 2018 ; Lindsly et al., 2021 ). We initially performed a global analysis of the maternal and paternal 4DN by combining RNA-seq with multiple network centrality measures ( method details ) ( Lindsly et al., 2021 ).…”

“…For Hi-C data, we consider genomic loci as nodes and use network centrality to measure the importance of each locus at each cell cycle phase ( Liu et al., 2018 ; Lindsly et al., 2021 ). We initially performed a global analysis of the maternal and paternal 4DN by combining RNA-seq with multiple network centrality measures ( method details ) ( Lindsly et al., 2021 ). We found differences between the maternal and paternal genomes and across cell cycle phases, but only Chromosome X had clear maternal and paternal separation ( Figures S9 and S10 ).…”

“…In contrast, we observed a pronounced lack of ABE in crucial biological pathways and essential genes. Our findings highlight advantages of integrating genomics analyses in a cell cycle and allele-specific manner and represent an allele-specific extension of the 4D Nucleome (4DN) ( Chen et al., 2015 ; Ried and Rajapakse, 2017 ; Dekker et al., 2017 ; Lindsly et al., 2021 ). This approach will be beneficial to investigation of human phenotypic traits and their penetrance, genetic diseases, vulnerability to complex disorders, and tumorigenesis.…”