Computational methods for analyzing and modeling genome structure and organization

Abstract

Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D genome organization to essential biological processes. In this article, we review the state of the art in analytic and modeling techniques with a focus on their application to answering various biological questions related to chromatin structure. We summarize the limitations of these computational techniques and suggest future directions, including the importance of incorporating multiple sources of experimental data in building a more comprehensive model of the genome. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Models of Systems Properties and Processes > Mechanistic Models.

Inferring diploid 3D chromatin structures from Hi-C data

“…Chromosome conformation capture techniques such as Hi-C measure the frequency of interactions between pairs of loci, thereby allowing a systematic analysis of genome structure. Although Hi-C contact matrices yield valuable insights, modeling and visualizing genome structures in 3D can unveil relationships and higher-order structural patterns that are not apparent in the raw data [12,36,26,23] by providing a humanly interpretable 3D structure, orienting genomic regions relative to various nuclear landmarks, and serving as a framework for integrating other data types [5]. Embedding contact count data in a 3D Euclidean space can also reduce noise in the underlying Hi-C data.…”

FAN-C: A Feature-rich Framework for the Analysis and Visualisation of C data

“…Hi-C matrix generation tools convert FASTQ data from a Hi-C experiment into a normalised matrix of interaction strengths between pairs of genomic regions, accounting for false-positive interactions in the process. Feature analysis tools act on the Hi-C matrix to derive measures, models, and statistics that answer specific biological questions, such as the identification of topologically associating domains (Dixon et al 2012;Sexton et al 2012;Nora et al 2012) and chromatin loops (Varoquaux et al 2014;Rao et al 2014), the 3D modelling of the chromatin fibre (Le Dily et al 2017;Lin et al 2019), or the identification of differential contacts between samples. Visualisation tools then enable the static display, and sometimes interactive exploration of the Hi-C matrix, and generally also of associated genomic data (Yardimci and Noble 2017;Ing-Simmons and Vaquerizas 2019).…”

Comparison of the Hi-C, GAM and SPRITE methods by use of polymer models of chromatin

“…The comparison of the performance of the different technologies in those loci is interesting also because, for instance, disease-linked structural variants located around the Sox9 and Epha4 genes have been shown to induce gene mis-expression as a consequence of the rewiring of contacts with local enhancers 6,27,28 ; and the HoxD locus presents a complex 3D compartmentalization which is thought to have a broad functional role in controlling transcriptional states during differentiation 29,30 . Different computational approaches [31][32][33][34] and polymer models 25,27,[35][36][37][38][39][40][41][42][43][44] have been discussed to reconstruct chromatin 3D conformations. Here, we focused on the String&Binders (SBS) polymer model 27,37,39 because it has been already validated against Hi-C data in those loci [25][26][27] .…”