Chromosome structure modeling tools and their evaluation in bacteria

Abstract

The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.

Keywords: Hi-C; algorithm evaluation; chromatin interaction; chromosome modeling; prokaryotes.

Figure 1

Classification of chromosome 3D structure reconstruction methods.

Figure 2

The general process of constraint-based modeling for chromosome 3D structure reconstruction. Stage 1: convert the input data (usually Hi-C contact matrix or local genomic features) into distance matrix, or directly use Hi-C contacts as constraints for modeling. Stage 2: define the objective function and constraint parameters. Stage 3: optimize the structure to obtain the final conformation.

Figure 3

Representation of chromosomal polymer models in thermodynamic modeling. The outer layer provides an intuitive depiction of three model types, while the inner layer illustrates the factors considered during modeling.

Figure 4

Illustration of 3D models of the E. coli chromosome reconstructed using eleven consensus-based algorithms.

Figure 5

Illustration of 3D models of the E. coli chromosome reconstructed using six ensemble-based algorithms.

Figure 6

Comparison between the reconstructed structure (from noisy data) and the original structure using RMSD. Both the absolute RMSD value and its trend with the increase of noise level should be considered.

Figure 7

The time required to reconstruct the 3D model of the standard spiral ring structure with various bin numbers. This figure shows the time efficiency of different modeling algorithms.