Articolo in rivista, 2019, ENG, 10.1109/TCBB.2018.2791439
Caudai C.; Salerno E.; Zoppe M.; Tonazzini A.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-IFC, Pisa, Italy; CNR-ISTI, Pisa, Italy
We present a method to infer 3D chromatin configurations from Chromosome Conformation Capture data. Quite a few methods have been proposed to estimate the structure of the nuclear DNA in homogeneous populations of cells from this kind of data. Many of them transform contact frequencies into Euclidean distances between pairs of chromatin fragments, and then reconstruct the structure by solving a distance-to-geometry problem. To avoid inconsistencies, our method is based on a score function that does not require any frequency-to-distance translation. We propose a multiscale chromatin model where the chromatin fibre is suitably partitioned at each scale. The partial structures are estimated independently, and connected to rebuild the whole fibre. Our score function consists in a data-fit part and a penalty part, balanced automatically at each scale and each subchain. The penalty part enforces "soft" geometric constraints. As many different structures can fit the data, our sampling strategy produces a set of solutions with similar scores. The procedure contains a few parameters, independent of both the scale and the genomic segment treated. The partition of the fibre, along with intrinsically parallel parts, make this method computationally efficient. Results from human genome data support the biological plausibility of our solutions.
IEEE/ACM transactions on computational biology and bioinformatics (Print) 16 (2), pp. 550–559
Bayesian estimation, Bioinformatics, Biological system modeling, Chromatin configuration, Chromosome conformation capture, Computational modeling, Data models, DNA, Genomics, Polymers
Caudai Claudia, Zoppe Monica Maria, Salerno Emanuele, Tonazzini Anna
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 386323
Year: 2019
Type: Articolo in rivista
Creation: 2018-04-13 11:04:08.000
Last update: 2021-04-09 23:41:35.000
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:386323
DOI: 10.1109/TCBB.2018.2791439
Scopus: 2-s2.0-85041192987
ISI Web of Science (WOS): 000469284500019