Quantitative prediction of enhancer-promoter interactions

Standard

Quantitative prediction of enhancer-promoter interactions. / Belokopytova, Polina S.; Nuriddinov, Miroslav A.; Mozheiko, Evgeniy A. et al.

In: Genome Research, Vol. 30, No. 1, 01.01.2020, p. 72-84.

Research output: Contribution to journal › Article › peer-review

Harvard

Belokopytova, PS, Nuriddinov, MA, Mozheiko, EA, Fishman, D & Fishman, V 2020, 'Quantitative prediction of enhancer-promoter interactions', Genome Research, vol. 30, no. 1, pp. 72-84. https://doi.org/10.1101/gr.249367.119

APA

Belokopytova, P. S., Nuriddinov, M. A., Mozheiko, E. A., Fishman, D., & Fishman, V. (2020). Quantitative prediction of enhancer-promoter interactions. Genome Research, 30(1), 72-84. https://doi.org/10.1101/gr.249367.119

Vancouver

Belokopytova PS, Nuriddinov MA, Mozheiko EA, Fishman D, Fishman V. Quantitative prediction of enhancer-promoter interactions. Genome Research. 2020 Jan 1;30(1):72-84. doi: 10.1101/gr.249367.119

Author

Belokopytova, Polina S. ; Nuriddinov, Miroslav A. ; Mozheiko, Evgeniy A. et al. / Quantitative prediction of enhancer-promoter interactions. In: Genome Research. 2020 ; Vol. 30, No. 1. pp. 72-84.

BibTeX

@article{303e5bc5b7224eafbf42540bd8220760,

title = "Quantitative prediction of enhancer-promoter interactions",

abstract = "Recent experimental and computational efforts have provided large data sets describing three-dimensional organization of mouse and human genomes and showed the interconnection between the expression profile, epigenetic state, and spatial interactions of loci. These interconnections were utilized to infer the spatial organization of chromatin, including enhancer-promoter contacts, from one-dimensional epigenetic marks. Here, we show that the predictive power of some of these algorithms is overestimated due to peculiar properties of the biological data. We propose an alternative approach, which provides high-quality predictions of chromatin interactions using information on gene expression and CTCF-binding alone. Using multiple metrics, we confirmed that our algorithm could efficiently predict the three-dimensional architecture of both normal and rearranged genomes.",

author = "Belokopytova, {Polina S.} and Nuriddinov, {Miroslav A.} and Mozheiko, {Evgeniy A.} and Daniil Fishman and Veniamin Fishman",

year = "2020",

month = jan,

day = "1",

doi = "10.1101/gr.249367.119",

language = "English",

volume = "30",

pages = "72--84",

journal = "Genome Research",

issn = "1088-9051",

publisher = "Cold Spring Harbor Laboratory Press",

number = "1",

}

RIS

TY - JOUR

T1 - Quantitative prediction of enhancer-promoter interactions

AU - Belokopytova, Polina S.

AU - Nuriddinov, Miroslav A.

AU - Mozheiko, Evgeniy A.

AU - Fishman, Daniil

AU - Fishman, Veniamin

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Recent experimental and computational efforts have provided large data sets describing three-dimensional organization of mouse and human genomes and showed the interconnection between the expression profile, epigenetic state, and spatial interactions of loci. These interconnections were utilized to infer the spatial organization of chromatin, including enhancer-promoter contacts, from one-dimensional epigenetic marks. Here, we show that the predictive power of some of these algorithms is overestimated due to peculiar properties of the biological data. We propose an alternative approach, which provides high-quality predictions of chromatin interactions using information on gene expression and CTCF-binding alone. Using multiple metrics, we confirmed that our algorithm could efficiently predict the three-dimensional architecture of both normal and rearranged genomes.

AB - Recent experimental and computational efforts have provided large data sets describing three-dimensional organization of mouse and human genomes and showed the interconnection between the expression profile, epigenetic state, and spatial interactions of loci. These interconnections were utilized to infer the spatial organization of chromatin, including enhancer-promoter contacts, from one-dimensional epigenetic marks. Here, we show that the predictive power of some of these algorithms is overestimated due to peculiar properties of the biological data. We propose an alternative approach, which provides high-quality predictions of chromatin interactions using information on gene expression and CTCF-binding alone. Using multiple metrics, we confirmed that our algorithm could efficiently predict the three-dimensional architecture of both normal and rearranged genomes.

UR - http://www.scopus.com/inward/record.url?scp=85077761338&partnerID=8YFLogxK

U2 - 10.1101/gr.249367.119

DO - 10.1101/gr.249367.119

M3 - Article

C2 - 31804952

AN - SCOPUS:85077761338

VL - 30

SP - 72

EP - 84

JO - Genome Research

JF - Genome Research

SN - 1088-9051

IS - 1

ER -

ID: 23121663