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Abstract Higher-order genome organization and its variation in different cellular conditions remain poorly understood. Recent high-coverage genome-wide chromatin interaction mapping using Hi-C has revealed spatial segregation of chromosomes in the human genome into distinct subcompartments. However, subcompartment annotation, which requires Hi-C data with high sequencing coverage, is currently only available in the GM12878 cell line, making it impractical to compare subcompartment patterns across cell types. Here we develop a computational approach, SNIPER (Subcompartment iNference using Imputed Probabilistic ExpRessions), based on denoising autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. SNIPER accurately reveals subcompartments using moderate coverage Hi-C datasets and outperforms an existing method that uses epigenomic features in GM12878. We apply SNIPER to eight additional cell lines and find that chromosomal regions with conserved and cell-type specific subcompartment annotations have different patterns of functional genomic features. SNIPER enables the identification of subcompartments without high-coverage Hi-C data and provides insights into the function and mechanisms of spatial genome organization variation across cell types.
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A Heuristic Strategy for Multi-Mapping Reads to Enhance Hi-C Data
Current Hi-C analysis approaches focus on uniquely mapped reads and little research has been carried out to include multi-mapping reads, which leads to a lack of biological signals from DNA repetitive regions. We propose a heuristic strategy to assign multi-mapping reads to loci according to the distance to their closest restriction enzyme cutting sites. We demonstrate that the heuristic strategy can rescue multi-mapping reads thus enhance the quality of Hi-C data. Compared with mHi-C, it not only improves replicate reproducibility in the same cell type, but also maintains the difference between replicates of different cell types. Moreover, the strategy identifies much more common statistically significant chromatin interactions between Hi-C experiments of different restriction enzymes and has a huge advantage on computing resources. Therefore, the heuristic strategy can be used to enhance Hi-C data by utilizing multi-mapping reads.
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- Award ID(s):
- 1946202
- PAR ID:
- 10318378
- Date Published:
- Journal Name:
- 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BIBE52308.2021.9635215
