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Genome-scale analysis of replication timing: from bench to bioinformatics

Nature Protocols volume 6, pages 870–895 (2011)Cite this article

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Abstract

Replication timing profiles are cell type–specific and reflect genome organization changes during differentiation. In this protocol, we describe how to analyze genome-wide replication timing (RT) in mammalian cells. Asynchronously cycling cells are pulse labeled with the nucleotide analog 5-bromo-2-deoxyuridine (BrdU) and sorted into S-phase fractions on the basis of DNA content using flow cytometry. BrdU-labeled DNA from each fraction is immunoprecipitated, amplified, differentially labeled and co-hybridized to a whole-genome comparative genomic hybridization microarray, which is currently more cost effective than high-throughput sequencing and equally capable of resolving features at the biologically relevant level of tens to hundreds of kilobases. We also present a guide to analyzing the resulting data sets based on methods we use routinely. Subjects include normalization, scaling and data quality measures, LOESS (local polynomial) smoothing of RT values, segmentation of data into domains and assignment of timing values to gene promoters. Finally, we cover clustering methods and means to relate changes in the replication program to gene expression and other genetic and epigenetic data sets. Some experience with R or similar programming languages is assumed. All together, the protocol takes ∼3 weeks per batch of samples.

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Figure 1: Two-dimensional cell-cycle sorting for S- and G1-phases.
Figure 2: A typical cell-cycle profile for a mammalian fibroblast population obtained during FACS analysis by plotting cell count versus DNA content.
Figure 3: Distribution of signal intensities before and after normalization.
Figure 4: Dependence of timing ratios on signal intensity.
Figure 5: Verification of scale normalization between data sets.
Figure 6: Replication timing values across chromosome 1.
Figure 7: Autocorrelation functions for two RT experiments and their average.
Figure 8: A typical NimbleGen microarray image after a successful experiment.
Figure 9
Figure 10

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Acknowledgements

We thank J.C. Rivera Mulia and A. Rycyk for helpful comments on the manuscript. Research in the Gilbert lab is funded by NIH Grants GM083337 and GM085354.

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Authors and Affiliations

  1. Department of Biological Science, Florida State University, Tallahassee, Florida, USA

    Tyrone Ryba, Dana Battaglia, Benjamin D Pope & David M Gilbert

  2. Biological Macromolecules Laboratory, National Institute of Genetics, Mishima, Japan

    Ichiro Hiratani

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Contributions

D.M.G. and I.H. conceived the study and designed the experiments. T.R. and I.H. devised the computational methods. T.R., D.B., B.D.P. and D.M.G. wrote the manuscript.

Corresponding author

Correspondence to David M Gilbert.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Data 1

Example data file L1210LymphoblastR1_532.pair (TXT 38582 kb)

Supplementary Data 2

Example data file L1210LymphoblastR1_635.pair (TXT 38532 kb)

Supplementary Data 3

Example data file L1210LymphoblastR2_532.pair (TXT 39297 kb)

Supplementary Data 4

Example data file L1210LymphoblastR2_635.pair (TXT 39203 kb)

Supplementary Data 5

Example targets file T.txt (TXT 0 kb)

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Ryba, T., Battaglia, D., Pope, B. et al. Genome-scale analysis of replication timing: from bench to bioinformatics. Nat Protoc 6, 870–895 (2011). https://doi.org/10.1038/nprot.2011.328

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