3D Chromosome and Genome Structure Modeling and Visualization

Keywords: Hi-C; Machine learning; Deep Learning; Optimization algorithm; Bioinformatics; 3D chromosome structure; 3D genome; Chromosome conformation capture
Fall 2015 - present
Source: Trieu, T., & Cheng, J. (2017). 3D genome structure modeling by Lorentzian objective function. Nucleic acids research, 45(3), 1049-1058

Description

The development of chromosomal conformation capture techniques, particularly, the Hi-C technique, has made the analysis and study of the spatial conformation of a genome an important topic in bioinformatics and computational biology. Aided by high-throughput next generation sequencing techniques, the Hi-C technology can generate read pairs that indicate the chromosomal locations within spatial proximity and large-scale intra- and inter-chromosomal interaction occuring within a genome (Lieberman-Aiden et al, 2009). This data can be used to reconstruct 3D structures of chromosomes that can be used to study DNA replication, gene regulation, genome interaction, genome folding, and genome function. This data is called the Hi-C data. Generally, before Hi-C data are used for model construction, they are converted to a matrix form known as a contact matrix or a contact map is a N * N matrix, extracted from a Hi-C data, showing the number of interactions between chromosomal regions. The size of the matrix (N) is the number of equal-size regions of a chromosome. The length of equal-size regions (e.g. 1 Mb base pair) is called resolution. Each entry in the matrix contains a count of read pairs that connect two corresponding chromosome regions in a Hi-C experiment. Therefore, the chromosome contact matrix represents all the observed interactions between the regions (or bins) in a chromosome.

Our focus on this project area can be subdivided into two:

3D Chromosome and Genome Structure Modeling:
This work focuses on developing novel and high-performing prediction tools and algorithms for advanced chromosome and genome 3D structure reconstruction; and the development of more robust tools that yields superior reconstruction accuracy for different Hi-C resolution dataset.

3D Chromosome and Genome Structure Visualization:
As important as 3D construction is, equally important is the visualization of the constructed 3D structure and the presentation of the relationships existing within them. Highlighting the structural relationships present within the genome structure is important for explaining otherwise unobservable functions when examining DNA sequence information alone. This work focuses on developing novel and high-performing tools and software for advanced visualization of chromosome and genome 3D structures

This work is supported by :
  • NSF CISE Research Initiation Initiative (CRII) Grant Award | Award Dates: 2022 to Present
  • UCCS Committee on Research and Creative Works (CRCW) Seed Grant Award | Award Dates: 2020-2022

    Publications

    1. Vadnais, D., & Oluwadare, O. (2023). ParticleChromo3D+: A Web Server for ParticleChromo3D Algorithm for 3D Chromosome Structure Reconstruction. Current Issues in Molecular Biology, 45(3), 2549-2560. [@ Current Issues in Molecular Biology Journal.]

    2. Hovenga, V., Kalita, J., & Oluwadare, O. (2023). HiC-GNN: A Generalizable Model for 3D Chromosome Reconstruction Using Graph Convolutional Neural Networks. Computational and Structural Biotechnology Journal, 2023 Jan 1;21:812-36. [@ Computational and Structural Biotechnology Journal.][Funding: NSF]

    3. Vadnais, D., Middleton, M. & Oluwadare, O. (2022). ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data. BioData Mining 15, 19 (2022). https://doi.org/10.1186/s13040-022-00305-x. [@ BioData Mining ]

    4. Collins, B.; Oluwadare, O.; Brown, P. (2021) ChromeBat: A Bio-Inspired Approach to 3D Genome Reconstruction. Genes 2021, 12, 1757. https://doi.org/10.3390/genes12111757 [@ Genes ]

    5. Hovenga, V; Oluwadare, O. (2021). CBCR: A Curriculum Based Strategy For Chromosome Reconstruction Int. J. Mol. Sci. 22, no. 8: 4140. https://doi.org/10.3390/ijms22084140 [@ Int. J. Mol. Sci. ]

    6. Oluwatosin Oluwadare, Max Highsmith, Douglass Turner, Erez Lieberman-Aiden & Jianlin Cheng. GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data. BMC Mol and Cell Biol 21, 60 (2020). [ @ BMC Mol and Cell Biol ]

    7. Oluwadare, Oluwatosin, Max Highsmith, and Jianlin Cheng. An Overview of Methods for Reconstructing 3-D Chromosome and Genome Structures from Hi-C Data. Biological Procedures Online 21.1 (2019):7. [@ Biological Procedures Online]

    8. Trieu, Tuan, Oluwatosin Oluwadare, and Jianlin Cheng. Hierarchical Reconstruction of High-Resolution 3D Models of Large Chromosome. Scientific reports. 2019 Mar 21;9(1):4971. [@ Scientific report]

    9. Trieu, Tuan*, Oluwatosin Oluwadare*, Julia Wopata, and Jianlin Cheng. GenomeFlow: A Comprehensive Graphical Tool for Modeling and Analyzing 3D Genome Structure. Bioinformatics (2018).(* Co-first author) [@ Bioinformatics]

    10. Oluwadare, Oluwatosin, Yuxiang Zhang, and Jianlin Cheng. A maximum likelihood algorithm for reconstructing 3D structures of human chromosomes from chromosomal contact data. BMC genomics 19.1 (2018): 161.. [@ BMC Genomics]

    11. J. Nowotny, A. Wells, O. Oluwadare, L. Xu, R. Cao, T. Trieu, C. He, J. Cheng. GMOL: an interactive tool for 3D genome structure visualization. Scientific Reports, accepted, 2016. [@ Scientific Reports]

    12. J. Nowotny, S. Ahmed, L. Xu, O. Oluwadare, H. Chen, N. Hensley, T. Trieu, R. Cao, J. Cheng. Iterative reconstruction of three-dimensional models of human chromosomes from chromosomal contact data. BMC Bioinformatics, 16(1):338, 2015. [@ BMC Bioinformatics]

    Codes

    All our algorithms are made public, open-source, and freely accessible to all through our GitHub repository