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

View ORCID ProfileLuca Fiorillo, Francesco Musella, View ORCID ProfileRieke Kempfer, View ORCID ProfileAndrea M. Chiariello, View ORCID ProfileSimona Bianco, View ORCID ProfileAlexander Kukalev, View ORCID ProfileIbai Irastorza-Azcarate, View ORCID ProfileAndrea Esposito, Mattia Conte, View ORCID ProfileAntonella Prisco, View ORCID ProfileAna Pombo, View ORCID ProfileMario Nicodemi
doi: https://doi.org/10.1101/2020.04.24.059915
Luca Fiorillo
1, 80126 Naples, Italy
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Francesco Musella
1, 80126 Naples, Italy
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Rieke Kempfer
2Berlin Institute for Medical Systems Biology, Max-Delbrück Centre (MDC) for Molecular Medicine, Berlin, Germany
3Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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Andrea M. Chiariello
1, 80126 Naples, Italy
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Simona Bianco
1, 80126 Naples, Italy
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Alexander Kukalev
2Berlin Institute for Medical Systems Biology, Max-Delbrück Centre (MDC) for Molecular Medicine, Berlin, Germany
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Ibai Irastorza-Azcarate
2Berlin Institute for Medical Systems Biology, Max-Delbrück Centre (MDC) for Molecular Medicine, Berlin, Germany
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Andrea Esposito
1, 80126 Naples, Italy
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Mattia Conte
1, 80126 Naples, Italy
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Antonella Prisco
4CNR-IGB, via Pietro Castellino 111, Naples, Italy
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Ana Pombo
2Berlin Institute for Medical Systems Biology, Max-Delbrück Centre (MDC) for Molecular Medicine, Berlin, Germany
3Humboldt-Universität zu Berlin, 10117 Berlin, Germany
5Berlin Institute of Health (BIH), Berlin, Germany
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Mario Nicodemi
1, 80126 Naples, Italy
2Berlin Institute for Medical Systems Biology, Max-Delbrück Centre (MDC) for Molecular Medicine, Berlin, Germany
5Berlin Institute of Health (BIH), Berlin, Germany
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  • For correspondence: mario.nicodemi@na.infn.it
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Abstract

Powerful technologies have been developed to probe chromatin 3D physical interactions genome-wide, such as Hi-C, GAM and SPRITE. Due to their intrinsic differences and without a benchmarking reference, it is currently difficult to assess how well each method represents the genome 3D structure and their relative performance. Here, we develop a computational approach to implement Hi-C, GAM and SPRITE in-silico to compare the three methods in a simplified, yet controlled framework against known polymer 3D structures. We test our approach on models of three 6-Mb genomic regions, around the Sox9 and the HoxD genes in mouse ES cells, and around the Epha4 gene in mouse CHLX-12 cells. The model-derived contact matrices consistently match Hi-C, GAM and SPRITE experiments. We show that in-silico Hi-C, GAM and SPRITE average data are overall faithful to the 3D structures of the polymer models. We find that the inherent variability of model single-molecule 3D conformations and experimental efficiency differently affect the contact data of the different methods. Similarly, the noise-to-signal levels vary with genomic distance differently in in-silico Hi-C, SPRITE and GAM. We benchmark the performance of each technology in bulk and in single-cell experiments, and identify the minimal number of cells required for replicates to return statistically consistent chromatin contact measures. Under the same experimental conditions, SPRITE requires the lowest number of cells, Hi-C is close to SPRITE, while GAM is the most reproducible method to capture interactions at large genomic distances.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 25, 2020.
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Comparison of the Hi-C, GAM and SPRITE methods by use of polymer models of chromatin
Luca Fiorillo, Francesco Musella, Rieke Kempfer, Andrea M. Chiariello, Simona Bianco, Alexander Kukalev, Ibai Irastorza-Azcarate, Andrea Esposito, Mattia Conte, Antonella Prisco, Ana Pombo, Mario Nicodemi
bioRxiv 2020.04.24.059915; doi: https://doi.org/10.1101/2020.04.24.059915
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Comparison of the Hi-C, GAM and SPRITE methods by use of polymer models of chromatin
Luca Fiorillo, Francesco Musella, Rieke Kempfer, Andrea M. Chiariello, Simona Bianco, Alexander Kukalev, Ibai Irastorza-Azcarate, Andrea Esposito, Mattia Conte, Antonella Prisco, Ana Pombo, Mario Nicodemi
bioRxiv 2020.04.24.059915; doi: https://doi.org/10.1101/2020.04.24.059915

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