TY - JOUR

T1 - Predicting Genome Architecture: Challenges and Solutions

AU - Belokopytova, Polina

AU - Fishman, Veniamin

N1 - Funding Information: We thank Emil Valeev and Olga Gladkih who helped us with designing illustrations. Funding. This work was supported by the RSF grant #19-74-00102. Computations have shown in Figure 3 were performed using nodes of the Novosibirsk State University high-throughput computation cluster [supported by the Ministry of Education and Science of Russian Federation, grant #2019-0546 (FSUS-2020-0040)].

PY - 2021/1/22

Y1 - 2021/1/22

N2 - Genome architecture plays a pivotal role in gene regulation. The use of high-throughput methods for chromatin profiling and 3-D interaction mapping provide rich experimental data sets describing genome organization and dynamics. These data challenge development of new models and algorithms connecting genome architecture with epigenetic marks. In this review, we describe how chromatin architecture could be reconstructed from epigenetic data using biophysical or statistical approaches. We discuss the applicability and limitations of these methods for understanding the mechanisms of chromatin organization. We also highlight the emergence of new predictive approaches for scoring effects of structural variations in human cells.

AB - Genome architecture plays a pivotal role in gene regulation. The use of high-throughput methods for chromatin profiling and 3-D interaction mapping provide rich experimental data sets describing genome organization and dynamics. These data challenge development of new models and algorithms connecting genome architecture with epigenetic marks. In this review, we describe how chromatin architecture could be reconstructed from epigenetic data using biophysical or statistical approaches. We discuss the applicability and limitations of these methods for understanding the mechanisms of chromatin organization. We also highlight the emergence of new predictive approaches for scoring effects of structural variations in human cells.

KW - Hi-C

KW - machine learning

KW - modeling

KW - polymer physics

KW - predicting approaches

UR - http://www.scopus.com/inward/record.url?scp=85100573399&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/368634ae-a940-3f09-a121-abf927840469/

U2 - 10.3389/fgene.2020.617202

DO - 10.3389/fgene.2020.617202

M3 - Review article

C2 - 33552135

AN - SCOPUS:85100573399

VL - 11

JO - Frontiers in Genetics

JF - Frontiers in Genetics

SN - 1664-8021

M1 - 617202

ER -