Dataset Information

CscoreTool-M infers 3D sub-compartment probabilities within cell population.

ABSTRACT:

Motivation

Computational inference of genome organization based on Hi-C sequencing has greatly aided the understanding of chromatin and nuclear organization in three dimensions (3D). However, existing computational methods fail to address the cell population heterogeneity. Here we describe a probabilistic-modeling-based method called CscoreTool-M that infers multiple 3D genome sub-compartments from Hi-C data.

Results

The compartment scores inferred using CscoreTool-M represents the probability of a genomic region locating in a specific sub-compartment. Compared to published methods, CscoreTool-M is more accurate in inferring sub-compartments corresponding to both active and repressed chromatin. The compartment scores calculated by CscoreTool-M also help to quantify the levels of heterogeneity in sub-compartment localization within cell populations. By comparing proliferating cells and terminally differentiated non-proliferating cells, we show that the proliferating cells have higher genome organization heterogeneity, which is likely caused by cells at different cell-cycle stages. By analyzing 10 sub-compartments, we found a sub-compartment containing chromatin potentially related to the early-G1 chromatin regions proximal to the nuclear lamina in HCT116 cells, suggesting the method can deconvolve cell cycle stage-specific genome organization among asynchronously dividing cells. Finally, we show that CscoreTool-M can identify sub-compartments that contain genes enriched in housekeeping or cell-type-specific functions.

Availability and implementation

https://github.com/scoutzxb/CscoreTool-M.

SUBMITTER: Zheng X

PROVIDER: S-EPMC10206090 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

CscoreTool-M infers 3D sub-compartment probabilities within cell population.

Zheng Xiaobin X Tran Joseph R JR Zheng Yixian Y

Bioinformatics (Oxford, England) 20230501 5

<h4>Motivation</h4>Computational inference of genome organization based on Hi-C sequencing has greatly aided the understanding of chromatin and nuclear organization in three dimensions (3D). However, existing computational methods fail to address the cell population heterogeneity. Here we describe a probabilistic-modeling-based method called CscoreTool-M that infers multiple 3D genome sub-compartments from Hi-C data.<h4>Results</h4>The compartment scores inferred using CscoreTool-M represents th ...[more]

PMID: 37166448

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Project description:BackgroundThe evolutionary probability (EP) of an allele in a DNA or protein sequence predicts evolutionarily permissible (ePerm; EP ≥ 0.05) and forbidden (eForb; EP < 0.05) variants. EP of an allele represents an independent evolutionary expectation of observing an allele in a population based solely on the long-term substitution patterns captured in a multiple sequence alignment. In the neutral theory, EP and population frequencies can be compared to identify neutral and non-neutral alleles. This approach has been used to discover candidate adaptive polymorphisms in humans, which are eForbs segregating with high frequencies. The original method to compute EP requires the evolutionary relationships and divergence times of species in the sequence alignment (a timetree), which are not known with certainty for most datasets. This requirement impedes a general use of the original EP formulation. Here, we present an approach in which the phylogeny and times are inferred from the sequence alignment itself prior to the EP calculation. We evaluate if the modified EP approach produces results that are similar to those from the original method.ResultsWe compared EP estimates from the original and the modified approaches by using more than 18,000 protein sequence alignments containing orthologous sequences from 46 vertebrate species. For the original EP calculations, we used species relationships from UCSC and divergence times from TimeTree web resource, and the resulting EP estimates were considered to be the ground truth. We found that the modified approaches produced reasonable EP estimates for HGMD disease missense variant and 1000 Genomes Project missense variant datasets. Our results showed that reliable estimates of EP can be obtained without a priori knowledge of the sequence phylogeny and divergence times. We also found that, in order to obtain robust EP estimates, it is important to assemble a dataset with many sequences, sampling from a diversity of species groups.ConclusionWe conclude that the modified EP approach will be generally applicable for alignments and enable the detection of potentially neutral, deleterious, and adaptive alleles in populations.

Dataset Information

CscoreTool-M infers 3D sub-compartment probabilities within cell population.

Motivation

Results

Availability and implementation

Publications

CscoreTool-M infers 3D sub-compartment probabilities within cell population.

Similar Datasets

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