ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data

被引:3
|
作者
Vadnais, David [1 ]
Middleton, Michael [1 ]
Oluwadare, Oluwatosin [1 ]
机构
[1] Univ Colorado, Dept Comp Sci, Colorado Springs, CO 80907 USA
关键词
Hi-C; 3D chromosome structure; Particle Swarm Optimization; Chromosome conformation capture; 3D genome; GENOME; TECHNOLOGIES; ORGANIZATION; PRINCIPLES; BIASES; MODEL;
D O I
10.1186/s13040-022-00305-x
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: The three-dimensional (3D) structure of chromatin has a massive effect on its function. Because of this, it is desirable to have an understanding of the 3D structural organization of chromatin. To gain greater insight into the spatial organization of chromosomes and genomes and the functions they perform, chromosome conformation capture (3C) techniques, particularly Hi-C, have been developed. The Hi-C technology is widely used and well-known because of its ability to profile interactions for all read pairs in an entire genome. The advent of Hi-C has greatly expanded our understanding of the 3D genome, genome folding, gene regulation and has enabled the development of many 3D chromosome structure reconstruction methods. Results: Here, we propose a novel approach for 3D chromosome and genome structure reconstruction from Hi-C data using Particle Swarm Optimization (PSO) approach called ParticleChromo3D. This algorithm begins with a grouping of candidate solution locations for each chromosome bin, according to the particle swarm algorithm, and then iterates its position towards a global best candidate solution. While moving towards the optimal global solution, each candidate solution or particle uses its own local best information and a randomizer to choose its path. Using several metrics to validate our results, we show that ParticleChromo3D produces a robust and rigorous representation of the 3D structure for input Hi-C data. We evaluated our algorithm on simulated and real Hi-C data in this work. Our results show that ParticleChromo3D is more accurate than most of the existing algorithms for 3D structure reconstruction. Conclusions: Our results also show that constructed ParticleChromo3D structures are very consistent, hence indicating that it will always arrive at the global solution at every iteration. The source code for ParticleChromo3D, the simulated and real Hi-C datasets, and the models generated for these datasets are available here: https://github.com/ OluwadareLab/ParticleChromo3D
引用
收藏
页数:26
相关论文
共 50 条
  • [41] Rich Chromatin Structure Prediction from Hi-C Data
    Malik, Laraib
    Patro, Rob
    ACM-BCB' 2017: PROCEEDINGS OF THE 8TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY,AND HEALTH INFORMATICS, 2017, : 184 - 193
  • [42] Estimation of 3D Protein Structure by Means of Parallel Particle Swarm Optimization
    Perez Hernandez, Luis German
    Rodriguez Vazquez, Katya
    Garduno Juarez, Ramon
    2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [43] MCI Conversion Prediction Using 3D Zernike Moments and the Improved Dynamic Particle Swarm Optimization Algorithm
    Bolourchi, Pouya
    Gholami, Mohammadreza
    Moradi, Masoud
    Beheshti, Iman
    Demirel, Hasan
    APPLIED SCIENCES-BASEL, 2023, 13 (07):
  • [44] GAN-based data augmentation to improve 3D chromatin features identification in Hi-C data
    Li, Chong
    Mohammad, Erfan
    Song, Chen
    Shi, Xinghua
    EUROPEAN JOURNAL OF HUMAN GENETICS, 2024, 32 : 297 - 298
  • [45] Hi-C enables the 3D characterization of small supernumerary marker chromosomes
    Jungnitsch, J.
    Klever, M.
    Melo, U.
    Schoepflin, R.
    Prada-Medina, C. A.
    Liehr, T.
    Mundlos, S.
    Spielmann, M.
    EUROPEAN JOURNAL OF HUMAN GENETICS, 2020, 28 (SUPPL 1) : 589 - 589
  • [46] Mapping 3D genome architecture through in situ DNase Hi-C
    Vijay Ramani
    Darren A Cusanovich
    Ronald J Hause
    Wenxiu Ma
    Ruolan Qiu
    Xinxian Deng
    C Anthony Blau
    Christine M Disteche
    William S Noble
    Jay Shendure
    Zhijun Duan
    Nature Protocols, 2016, 11 : 2104 - 2121
  • [47] Mapping 3D genome architecture through in situ DNase Hi-C
    Ramani, Vijay
    Cusanovich, Darren A.
    Hause, Ronald J.
    Ma, Wenxiu
    Qiu, Ruolan
    Deng, Xinxian
    Blau, C. Anthony
    Disteche, Christine M.
    Noble, William S.
    Shendure, Jay
    Duan, Zhijun
    NATURE PROTOCOLS, 2016, 11 (11) : 59 - 76
  • [48] A Novel 3D Reconstruction Algorithm Based on Hybrid Immune Particle Swarm Optimization
    Chen Zhi-Ming
    Cao Jian-Zhong
    Huang Jin-Qiu
    PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE, 2010, : 5228 - 5231
  • [49] Hybrid Optimization Algorithm Based on Double Particle Swarm in 3D NoC Mapping
    Fang, Juan
    Cai, Huayi
    Lv, Xin
    MICROMACHINES, 2023, 14 (03)
  • [50] Research on an improved algorithm for 3D NoC floorplanning based on particle swarm optimization
    School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin
    300387, China
    不详
    300384, China
    Open. Cybern. Syst. J., 1 (1145-1154):