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Producing genome structure populations with the dynamic and automated PGS software

Nature Protocols volume 13, pages 915–926 (2018)Cite this article

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Abstract

Chromosome conformation capture technologies such as Hi-C are widely used to investigate the spatial organization of genomes. Because genome structures can vary considerably between individual cells of a population, interpreting ensemble-averaged Hi-C data can be challenging, in particular for long-range and interchromosomal interactions. We pioneered a probabilistic approach for the generation of a population of distinct diploid 3D genome structures consistent with all the chromatin–chromatin interaction probabilities from Hi-C experiments. Each structure in the population is a physical model of the genome in 3D. Analysis of these models yields new insights into the causes and the functional properties of the genome's organization in space and time. We provide a user-friendly software package, called PGS, which runs on local machines (for practice runs) and high-performance computing platforms. PGS takes a genome-wide Hi-C contact frequency matrix, along with information about genome segmentation, and produces an ensemble of 3D genome structures entirely consistent with the input. The software automatically generates an analysis report, and provides tools to extract and analyze the 3D coordinates of specific domains. Basic Linux command-line knowledge is sufficient for using this software. A typical running time of the pipeline is ∼3 d with 300 cores on a computer cluster to generate a population of 1,000 diploid genome structures at topological-associated domain (TAD)-level resolution.

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Figure 1: Schematic of the PGS algorithm that deconvolves ensemble-averaged Hi-C data into a population of distinct diploid 3D genome structures.
Figure 2: PGS software workflows.
Figure 3: PGS setup.
Figure 4: Examples of PGS outputs.

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Acknowledgements

This work was supported by the Arnold and Mabel Beckman Foundation (BYI program) (to F.A.), the National Institutes of Health (grant U54DK107981 to F.A. and X.J.Z., grant P41 GM109824 to F.A., and a National Heart, Lung, and Blood Institute (NHLBI) grant (MAP-GEN U01HL108634) to X.J.Z.), and an NSF CAREER grant (1150287 to F.A.). F.A. is a Pew Scholar in Biomedical Sciences, supported by the Pew Charitable Trusts. We also thank W. Li for his contributions and discussions.

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Author notes

  1. Nan Hua, Harianto Tjong and Hanjun Shin: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biological Sciences, Molecular and Computational Biology, University of Southern California, Los Angeles, California, USA

    Nan Hua, Harianto Tjong, Hanjun Shin, Ke Gong, Xianghong Jasmine Zhou & Frank Alber

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  1. Nan Hua
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Contributions

H.T., K.G., and F.A. developed the method with the help of N.H.; N.H., H.T., and H.S. worked on the software design and implementation. N.H. and H.T. carried out analysis and developed tools included in the package. N.H., H.T. H.S., X.J.Z., and F.A. wrote the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Frank Alber.

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The authors declare no competing financial interests.

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Supplementary Data

Technical details of PGS. (PDF 221 kb)

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Hua, N., Tjong, H., Shin, H. et al. Producing genome structure populations with the dynamic and automated PGS software. Nat Protoc 13, 915–926 (2018). https://doi.org/10.1038/nprot.2018.008

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