Abstract
The packing of genomic DNA from double helix into highly-order hierarchical assemblies has a great impact on chromosome flexibility, dynamics and functions. The open and accessible regions of chromosomes are primary binding positions for regulatory elements and are crucial to nuclear processes and biological functions. Motivated by the success of flexibility-rigidity index (FRI) in biomolecular flexibility analysis and drug design, we propose an FRI-based model for quantitatively characterizing chromosome flexibility. Based on Hi-C data, a flexibility index for each locus can be evaluated. Physically, flexibility is tightly related to packing density. Highly compacted regions are usually more rigid, while loosely packed regions are more flexible. Indeed, a strong correlation is found between our flexibility index and DNase and ATAC values, which are measurements for chromosome accessibility. In addition, the genome regions with higher chromosome flexibility have a higher chance to be bound by transcription factors. Recently, the Gaussian network model (GNM) is applied to analyze the chromosome accessibility and a mobility profile has been proposed to characterize chromosome flexibility. Compared with GNM, our FRI is slightly more accurate (1% to 2% increase) and significantly more efficient in both computational time and costs. For a 5Kb resolution Hi-C data, the flexibility evaluation process only takes FRI a few minutes on a single-core processor. In contrast, GNM requires 1.5 hours on 10 CPUs. Moreover, interchromosome interactions can be easily combined into the flexibility evaluation, thus further enhancing the accuracy of our FRI. In contrast, the consideration of interchromosome information into GNM will significantly increase the size of its Laplacian (or Kirchhoff) matrix, thus becoming computationally extremely challenging for the current GNM. The software and supplementary document are available at https://github.com/jiajiepeng/FRI_chrFle.
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Acknowledgements
This work was supported in part by Nanyang Technological University Startup (M4081842.110), Singapore Ministry of Education Academic Research fund (Tier 1 RG126/16, RG31/18), and the National Natural Science Foundation of China (Grant Nos. 61702421, 61332014, 61772426). The author Kelin Xia would like to thank Amartya Sanyal for his introduction and discussion of Hi-C experiments and data analysis.
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Jiajie Peng is an associate professor in the School of Computer Science at Northwestern Polytechnical University, China. His research focuses on the development of data mining and artificial intelligence algorithms to solve problems in medicine and biology.
Jinjin Yang is a graduate student in the School of Computer Science, Northwestern Polytechnical University, China. She is interested in artificial intelligence, bioinformatics and data mining.
D Vijay Anand is currently a research fellow at the School of Physical and Mathematical Sciences at the Nanyang Technological University, Singapore. He received his PhD degree in mesoscale modelling and simulation of soft matter from the Indian Institute of Technology Madras, India. He did postdoctoral work at the Indian Institute of Science, India on collective cell migrations. His research interests include molecular modelling and simulation of soft materials to explore their function, dynamics and transport of these biological systems by an integrated.
Xuequn Shang, a professor and doctoral supervisor of Northwestern Polytechnical University, China. Her research focuses on data mining, machine learning, bioinformatics, educational big data, and data management.
Kelin Xia obtained his PhD degree from the Chinese Academy of Sciences, China in 2013. He was a visiting scholar in the Department of Mathematics, Michigan State University, USA from 2009 to 2012. From January 2013 to May 2016, he worked as a visiting assistant professor at Michigan State University, USA. He joined Nanyang Technological University, Singapore in June 2016. His research focused on scientific computation, mathematical molecular biology, and topological data analysis (TDA) of complex data in biomolecular systems.
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Peng, J., Yang, J., Anand, D.V. et al. Flexibility and rigidity index for chromosome packing, flexibility and dynamics analysis. Front. Comput. Sci. 16, 164902 (2022). https://doi.org/10.1007/s11704-021-0304-1
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DOI: https://doi.org/10.1007/s11704-021-0304-1