3D Genome Reconstruction from Partially Phased Hi-C Data
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3D Genome Reconstruction from Partially Phased Hi-C Data. / Cifuentes, Diego; Draisma, Jan; Henriksson, Oskar; Korchmaros, Annachiara; Kubjas, Kaie.
I: Bulletin of Mathematical Biology, Bind 86, Nr. 4, 33, 2024, s. 1.30.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - 3D Genome Reconstruction from Partially Phased Hi-C Data
AU - Cifuentes, Diego
AU - Draisma, Jan
AU - Henriksson, Oskar
AU - Korchmaros, Annachiara
AU - Kubjas, Kaie
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - The 3-dimensional (3D) structure of the genome is of significant importance for many cellular processes. In this paper, we study the problem of reconstructing the 3D structure of chromosomes from Hi-C data of diploid organisms, which poses additional challenges compared to the better-studied haploid setting. With the help of techniques from algebraic geometry, we prove that a small amount of phased data is sufficient to ensure finite identifiability, both for noiseless and noisy data. In the light of these results, we propose a new 3D reconstruction method based on semidefinite programming, paired with numerical algebraic geometry and local optimization. The performance of this method is tested on several simulated datasets under different noise levels and with different amounts of phased data. We also apply it to a real dataset from mouse X chromosomes, and we are then able to recover previously known structural features.
AB - The 3-dimensional (3D) structure of the genome is of significant importance for many cellular processes. In this paper, we study the problem of reconstructing the 3D structure of chromosomes from Hi-C data of diploid organisms, which poses additional challenges compared to the better-studied haploid setting. With the help of techniques from algebraic geometry, we prove that a small amount of phased data is sufficient to ensure finite identifiability, both for noiseless and noisy data. In the light of these results, we propose a new 3D reconstruction method based on semidefinite programming, paired with numerical algebraic geometry and local optimization. The performance of this method is tested on several simulated datasets under different noise levels and with different amounts of phased data. We also apply it to a real dataset from mouse X chromosomes, and we are then able to recover previously known structural features.
KW - 13P25
KW - 14P05
KW - 3D genome organization
KW - 65H14
KW - 90C90
KW - 92-08
KW - 92E10
KW - Applied algebraic geometry
KW - Diploid organisms
KW - Hi-C
KW - Numerical algebraic geometry
U2 - 10.1007/s11538-024-01263-7
DO - 10.1007/s11538-024-01263-7
M3 - Journal article
C2 - 38386111
AN - SCOPUS:85185698287
VL - 86
SP - 1.30
JO - Bulletin of Mathematical Biology
JF - Bulletin of Mathematical Biology
SN - 0092-8240
IS - 4
M1 - 33
ER -
ID: 384873416