Abstract
Over the past decade, methods for predicting three-dimensional (3-D) chromosome and genome structures have proliferated. This has been primarily due to the development of high-throughput, next-generation chromosome conformation capture (3C) technologies, which have provided next-generation sequencing data about chromosome conformations in order to map the 3-D genome structure. The introduction of the Hi-C technique—a variant of the 3C method—has allowed researchers to extract the interaction frequency (IF) for all loci of a genome at high-throughput and at a genome-wide scale. In this review we describe, categorize, and compare the various methods developed to map chromosome and genome structures from 3C data—particularly Hi-C data. We summarize the improvements introduced by these methods, describe the approach used for method evaluation, and discuss how these advancements shape the future of genome structure construction.
Highlights
After decades of research about the organization of the nucleus of the eukaryotic cell, there exists substantial evidence that the genome architecture plays a key role in nuclear functions. [1–8]
The tool developed by Zou et al, called HSA, takes advantage of the uniqueness of the contact map obtained from different restriction enzymes in Hi-C experiments; it creates a generalized linear model through an iterative algorithm that combines simulated annealing and Hamiltonian dynamics
Validation and Evaluation According to the literature on chromosome and genome 3-D construction methods, algorithms are most often validated by a simulated dataset to assess their reconstruction ability, the consistency with the Hi-C data, known genome and chromosome structural features [49], or Fluorescence in situ hybridization (FISH) data
Summary
After decades of research about the organization of the nucleus of the eukaryotic cell, there exists substantial evidence that the genome architecture plays a key role in nuclear functions. [1–8]. Distance-Based Methods Over the years, a number of approaches have been proposed for chromosome 3-D structure inference from Hi-C contact data. The tool developed by Zou et al, called HSA, takes advantage of the uniqueness of the contact map obtained from different restriction enzymes in Hi-C experiments; it creates a generalized linear model through an iterative algorithm that combines simulated annealing and Hamiltonian dynamics.
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