Abstract

Upon fertilization, drastic chromatin reorganization occurs during preimplantation development1. However, the global chromatin landscape and its molecular dynamics in this period remain largely unexplored in humans. Here we investigate chromatin states in human preimplantation development using an improved assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq)2. We find widespread accessible chromatin regions in early human embryos that overlap extensively with putative cis-regulatory sequences and transposable elements. Integrative analyses show both conservation and divergence in regulatory circuitry between human and mouse early development, and between human pluripotency in vivo and human embryonic stem cells. In addition, we find widespread open chromatin regions before zygotic genome activation (ZGA). The accessible chromatin loci are readily found at CpG-rich promoters. Unexpectedly, many others reside in distal regions that overlap with DNA hypomethylated domains in human oocytes and are enriched for transcription factor-binding sites. A large portion of these regions then become inaccessible after ZGA in a transcription-dependent manner. Notably, such extensive chromatin reorganization during ZGA is conserved in mice and correlates with the reprogramming of the non-canonical histone mark H3K4me3, which is uniquely linked to genome silencing3,4,5. Taken together, these data not only reveal a conserved principle that underlies the chromatin transition during mammalian ZGA, but also help to advance our understanding of epigenetic reprogramming during human early development and in vitro fertilization.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

  • 20 June 2018

    In this Letter, the ‘Open chromatin’ label in Fig. 4a should have been centred above the first three columns, and the black horizontal line underneath the label should have been removed. In addition, there should have been a vertical black line between the last two sets of panels for consistency. Minor changes have also been made to Fig. 1 and to the legend of Fig. 3. These errrors have been corrected online, and see Supplementary Information to the accompanying Amendment for the original Fig. 4.

References

  1. 1.

    Burton, A. & Torres-Padilla, M. E. Chromatin dynamics in the regulation of cell fate allocation during early embryogenesis. Nat. Rev. Mol. Cell Biol. 15, 723–735 (2014).

  2. 2.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  3. 3.

    Zhang, B. et al. Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature 537, 553–557 (2016).

  4. 4.

    Dahl, J. A. et al. Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition. Nature 537, 548–552 (2016).

  5. 5.

    Andreu-Vieyra, C. V. et al. MLL2 is required in oocytes for bulk histone 3 lysine 4 trimethylation and transcriptional silencing. PLoS Biol. 8, https://doi.org/10.1371/journal.pbio.1000453 (2010).

  6. 6.

    Yeom, Y. I. et al. Germline regulatory element of Oct-4 specific for the totipotent cycle of embryonal cells. Development 122, 881–894 (1996).

  7. 7.

    Lee, M. T., Bonneau, A. R. & Giraldez, A. J. Zygotic genome activation during the maternal-to-zygotic transition. Annu. Rev. Cell Dev. Biol. 30, 581–613 (2014).

  8. 8.

    Fenouil, R. et al. CpG islands and GC content dictate nucleosome depletion in a transcription-independent manner at mammalian promoters. Genome Res. 22, 2399–2408 (2012).

  9. 9.

    Gerdes, P., Richardson, S. R., Mager, D. L. & Faulkner, G. J. Transposable elements in the mammalian embryo: pioneers surviving through stealth and service. Genome Biol. 17, 100 (2016).

  10. 10.

    Weinberger, L., Ayyash, M., Novershtern, N. & Hanna, J. H. Dynamic stem cell states: naive to primed pluripotency in rodents and humans. Nat. Rev. Mol. Cell Biol. 17, 155–169 (2016).

  11. 11.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  12. 12.

    Iturbide, A. & Torres-Padilla, M. E. Starting embryonic transcription for the first time. Nat. Genet. 49, 820–821 (2017).

  13. 13.

    Theunissen, T. W. et al. Systematic identification of culture conditions for induction and maintenance of naive human pluripotency. Cell Stem Cell 15, 471–487 (2014).

  14. 14.

    Takashima, Y. et al. Resetting transcription factor control circuitry toward ground-state pluripotency in human. Cell 158, 1254–1269 (2014).

  15. 15.

    Guo, G. et al. Klf4 reverts developmentally programmed restriction of ground state pluripotency. Development 136, 1063–1069 (2009).

  16. 16.

    Kuckenberg, P., Kubaczka, C. & Schorle, H. The role of transcription factor Tcfap2c/TFAP2C in trophectoderm development. Reprod. Biomed. Online 25, 12–20 (2012).

  17. 17.

    Morrisey, E. E. et al. GATA6 regulates HNF4 and is required for differentiation of visceral endoderm in the mouse embryo. Genes Dev. 12, 3579–3590 (1998).

  18. 18.

    Wu, J. et al. The landscape of accessible chromatin in mammalian preimplantation embryos. Nature 534, 652–657 (2016).

  19. 19.

    Neijts, R. et al. Polarized regulatory landscape and Wnt responsiveness underlie Hox activation in embryos. Genes Dev. 30, 1937–1942 (2016).

  20. 20.

    Hendrickson, P. G. et al. Conserved roles of mouse DUX and human DUX4 in activating cleavage-stage genes and MERVL/HERVL retrotransposons. Nat. Genet. 49, 925–934 (2017).

  21. 21.

    Paynton, B. V. & Bachvarova, R. Polyadenylation and deadenylation of maternal mRNAs during oocyte growth and maturation in the mouse. Mol. Reprod. Dev. 37, 172–180 (1994).

  22. 22.

    Xue, Z. et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500, 593–597 (2013).

  23. 23.

    Yan, L. et al. Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1131–1139 (2013).

  24. 24.

    Okae, H. et al. Genome-wide analysis of DNA methylation dynamics during early human development. PLoS Genet. 10, e1004868 (2014).

  25. 25.

    Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

  26. 26.

    Zheng, H. et al. Resetting epigenetic memory by reprogramming of histone modifications in mammals. Mol. Cell 63, 1066–1079 (2016).

  27. 27.

    Zhang, A. et al. Dynamic changes of histone H3 trimethylated at positions K4 and K27 in human oocytes and preimplantation embryos. Fertil. Steril. 98, 1009–1016 (2012).

  28. 28.

    Inoue, A., Jiang, L., Lu, F., Suzuki, T. & Zhang, Y. Maternal H3K27me3 controls DNA methylation-independent imprinting. Nature 547, 419–424 (2017).

  29. 29.

    Zenk, F. et al. Germ line-inherited H3K27me3 restricts enhancer function during maternal-to-zygotic transition. Science 357, 212–216 (2017).

  30. 30.

    Inoue, A., Jiang, L., Lu, F. & Zhang, Y. Genomic imprinting of Xist by maternal H3K27me3. Genes Dev. 31, 1927–1932 (2017).

  31. 31.

    Capalbo, A. et al. FISH reanalysis of inner cell mass and trophectoderm samples of previously array-CGH screened blastocysts shows high accuracy of diagnosis and no major diagnostic impact of mosaicism at the blastocyst stage. Hum. Reprod. 28, 2298–2307 (2013).

  32. 32.

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protocols 9, 171–181 (2014).

  33. 33.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  34. 34.

    Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

  35. 35.

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protocols 7, 562–578 (2012).

  36. 36.

    Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).

  37. 37.

    Schug, J. et al. Promoter features related to tissue specificity as measured by Shannon entropy. Genome Biol. 6, R33 (2005).

  38. 38.

    Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).

  39. 39.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  40. 40.

    McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

  41. 41.

    Dennis, G., Jr et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, 3 (2003).

  42. 42.

    Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

  43. 43.

    Kobayashi, H. et al. Contribution of intragenic DNA methylation in mouse gametic DNA methylomes to establish oocyte-specific heritable marks. PLoS Genet. 8, e1002440 (2012).

  44. 44.

    Petropoulos, S. et al. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 167, 285 (2016).

  45. 45.

    Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

  46. 46.

    Hawkins, R. D. et al. Distinct epigenomic landscapes of pluripotent and lineage-committed human cells. Cell Stem Cell 6, 479–491 (2010).

  47. 47.

    Xie, W. et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153, 1134–1148 (2013).

Download references

Acknowledgements

We appreciate the laboratory members’ comments during preparation of the manuscript. We are grateful for the animal core facility, the sequencing core facility, and biocomputing facility at Tsinghua University. This work was supported by the National Key R&D Program of China (2016YFC0900300 to W.X. and Y.S., 2017YFA0102802 to J.N.), the National Basic Research Program of China (2015CB856201 to W.X.), the National Natural Science Foundation of China (31422031 and 31725018 to W.X., 31471404 to Y.S., 91740115 to J.N., and 31501205 to J.X.), and the THU-PKU Center for Life Sciences (W.X.). W.X. is a recipient of HHMI International Research Scholar.

Reviewer information

Nature thanks I. Hyun and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Jingyi Wu, Jiawei Xu, Bofeng Liu, Guidong Yao, Peizhe Wang, Zili Lin.

Affiliations

  1. Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, THU-PKU Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China

    • Jingyi Wu
    • , Bofeng Liu
    • , Zili Lin
    • , Qiujun Wang
    • , Yuanyuan Li
    •  & Wei Xie
  2. Center for Reproductive Medicine, Henan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    • Jiawei Xu
    • , Guidong Yao
    • , Tong Li
    • , Senlin Shi
    • , Nan Zhang
    • , Xiangyang Zhang
    • , Wenbin Niu
    • , Wenyan Song
    • , Haixia Jin
    • , Yihong Guo
    • , Shanjun Dai
    • , Linli Hu
    • , Lanlan Fang
    •  & Yingpu Sun
  3. Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, China

    • Peizhe Wang
    • , Fuyu Duan
    • , Jia Ming
    •  & Jie Na
  4. PKU-THU Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    • Bo Huang
  5. State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    • Xuepeng Wang
    •  & Wei Li
  6. University of Chinese Academy of Sciences, Beijing, China

    • Xuepeng Wang
    •  & Wei Li
  7. Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Jingyi Wu
  8. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Jingyi Wu

Authors

  1. Search for Jingyi Wu in:

  2. Search for Jiawei Xu in:

  3. Search for Bofeng Liu in:

  4. Search for Guidong Yao in:

  5. Search for Peizhe Wang in:

  6. Search for Zili Lin in:

  7. Search for Bo Huang in:

  8. Search for Xuepeng Wang in:

  9. Search for Tong Li in:

  10. Search for Senlin Shi in:

  11. Search for Nan Zhang in:

  12. Search for Fuyu Duan in:

  13. Search for Jia Ming in:

  14. Search for Xiangyang Zhang in:

  15. Search for Wenbin Niu in:

  16. Search for Wenyan Song in:

  17. Search for Haixia Jin in:

  18. Search for Yihong Guo in:

  19. Search for Shanjun Dai in:

  20. Search for Linli Hu in:

  21. Search for Lanlan Fang in:

  22. Search for Qiujun Wang in:

  23. Search for Yuanyuan Li in:

  24. Search for Wei Li in:

  25. Search for Jie Na in:

  26. Search for Wei Xie in:

  27. Search for Yingpu Sun in:

Contributions

Y.S. and W.X. conceived and designed the project. J.W. and B.L. developed miniATAC–seq. B.L., J.W. and J.X. performed the ATAC–seq library construction and sequencing, J.X., W.N. and N.Z. performed RNA-seq library construction and sequencing. J.W. and B.L. analysed the data. G.Y., S.S., S.D. T.L. and X.Z. collected the human oocytes, sperm and 3PN embryos, and performed ICSI and ICM separation. J.X., S.S. and X.Z. performed α-amanitin treatment experiments. Y.G., L.H., W.S., H.J. and L.F. recruited the oocyte and sperm volunteers. P.W., Z.L., B.H. and J.M. performed the mouse embryo experiments. F.D. and X.W. provided primed and naive human ES cells. Q.W. and Y.L. performed NGS sequencing. W.L., J.N., Y.S. and W.X. supervised the project or various experiments. J.W., J.X., B.L. and W.X. wrote the manuscript with the help from all authors.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Jie Na or Wei Xie or Yingpu Sun.

Extended data figures and tables

  1. Extended Data Fig. 1 Validation of miniATAC-seq.

    a, UCSC browser view showing the ATAC-seq signals using various numbers of mouse ES cells. b, Scatter plots comparing the miniATAC-seq signals from various numbers of mouse ES cells with conventional ATAC-seq using 50,000 mouse ES cells. The Pearson correlation coefficients are also shown. c, Box plots showing the ATAC-seq enrichment for peaks from 50,000 mouse ES cells that are recaptured or missed by miniATAC-seq using lower numbers of mouse ES cells.

  2. Extended Data Fig. 2 Validation of RNA-seq data in human early embryos.

    a, Microscopy images of human 2PN and 3PN embryos at the zygote, two-cell, four-cell, eight-cell and blastocyst stages. ICSI was used to avoid the cumulus cell contamination. High-quality score embryos were selected for subsequent study (represented by magnified images). b, Heat maps showing gene expression levels for differentially expressed genes between human embryos of pre-lineage segregation and post-lineage specification44. Expression of ICMs in this study is also shown for two replicates. Epi, epiblast, PrE, primitive endoderm. c, Bar charts showing the Spearman correlation between the two replicates of RNA-seq samples. d, Hierarchical clustering of RNA-seq datasets from this study (using Smart-seq) and previous studies22,23 (using a different mRNA-seq method45). Pearson correlation was used to measure distances. Different colours represent various stages.

  3. Extended Data Fig. 3 Validation of ATAC-seq data in human early embryos.

    a, Scatter plots showing the ATAC-seq signals between replicates at each stage in human early development or between 2PN and 3PN embryos. b, UCSC browser view showing the landscape of accessible chromatin in replicates of human early embryos. c, Bar charts showing the genome coverages of ATAC-seq peaks of each stage or DNase-seq peaks for human ES cells (ENCODE). d, The overlap between ATAC-seq peaks and annotated promoters (TSS ± 0.5 kb) or distal DNase I hypersensitive sites in ES cells. A random set of peaks that match the lengths of individual ATAC-seq peaks were used as a control. e, UCSC browser views showing the ATAC-seq and RNA-seq enrichment near a representative gene. Open chromatin regions are shaded.

  4. Extended Data Fig. 4 Relationship of chromatin accessibility and transcription in human early embryos.

    a, Heat maps showing three classes of promoter accessibility (high, dynamic and low) for stage-specific genes (maternal genes excluded). CpG densities and H3K27me3 levels in human ES cells and fibroblasts (IMR90)46 are also shown. b, GO analysis results for gene classes in a. (The ‘low’ class does not have gene set enrichment with P < 10−2). c, Box plots showing promoter enrichment of H3K27me3 in human ES and IMR90 cells46 for each class in a. P values based on a one-sided t-test are shown. d, Scatter plots showing promoter ATAC-seq enrichment and gene expression (maternal genes excluded) for all genes (black) or genes with promoters of low (green), medium (blue), or high (red) CpG densities. Spearman correlation coefficients are shown.

  5. Extended Data Fig. 5 Features of distal ATAC-seq peaks in human embryos.

    a, Top, enrichment of repeats in ATAC-seq promoter and distal peaks compared to that in random peaks for early human embryos, human ES cells, and somatic cell types. The enrichment was calculated as a log2 ratio for the numbers of observed peaks that overlap with repeats divided by the numbers for random peaks. A random set of peaks that match the lengths of individual ATAC-seq peaks was used. Bottom, a similar analysis was performed for the enrichment of repeat subfamilies in distal peaks. b, Middle, heat maps showing the ATAC-seq signals for stage-specific distal ATAC-seq peaks in human embryos. Left, GREAT analysis40 results are also shown. Right, the expression of predicted targets among GREAT listed nearby genes for putative enhancers (distal peaks) (Methods) are shown for each stage. c, Bar charts showing the expression of TFAP2C in epiblast (EPI), primitive endoderm (PE) and trophectoderm (TE) lineages in human embryos from embryonic days 5–7 based on a previous study44. d, UCSC browser view showing the ATAC-seq signals around POU5F1 in human early embryos and ES cells (primed, naive 114 and naive 213). Human ES cell DNase-seq data (ENCODE) are shown as a control.

  6. Extended Data Fig. 6 Transcription and chromatin states before major human ZGA.

    a, Box plot showing the expression levels of two-cell specific (left) and eight-cell specific (right) genes in MII oocytes and embryos with or without α-amanitin treatment. P values based on a one-sided t-test are shown. b, The average ATAC-seq enrichment for each stage is shown at the promoters of stage specific genes at the same stage identified by mRNA-seq. Two-cell ATAC-seq enrichment for a random set of promoters were similarly analysed as a control. c, Heat maps showing expression levels of possible minor ZGA genes activated at the two-to-four-cell stage for their expression in germinal vesicle oocytes, MII oocytes, and two-to-four-cell embryos. Promoter ATAC-seq enrichment for two-to-four-cell stages and CpG densities are also shown. d, UCSC browser view showing the promoter ATAC-seq signals specifically appearing in two-cell embryos. e, Left, heat maps showing ATAC-seq enrichment at the accessible promoters present in both two- and eight-cell embryos, as well as those specific to each stage. Right, the human oocyte, blastocyst, sperm and ES cell DNA methylation levels around these promoters are also shown.

  7. Extended Data Fig. 7 Accessible chromatin state in one-cell and four-cell human embryos.

    a, UCSC browser view showing the ATAC-seq signals in human early embryos. b, Scatter plots showing the ATAC-seq signals between two-cell human embryos and 3PN one-cell, two-cell and four-cell embryos. c, Hierarchical clustering results showing the relationships of chromatin states among human embryos based on whole-genome ATAC-seq enrichment.

  8. Extended Data Fig. 8 Characterization of two-cell distal open chromatin in human embryos.

    a, Heat maps showing the ATAC-seq signals and CpG density for promoter and distal ATAC-seq peaks at the two-cell stage. b, Top, Venn diagram showing the overlap between two- and eight-cell distal ATAC-seq peaks. Bottom, motifs identified in two- and eight-cell embryo shared distal peaks as well as peaks specific for each stage are also shown. c, The enrichment of human ES cell H3K4me1 and H3K27ac marks47 around two-cell-specific distal peaks or two-to-eight-cell shared distal peaks is shown. A random set of peaks that match the lengths of individual two-cell-specific ATAC-seq peaks was used as a control. The upstream and downstream regions are 2 × peak lengths away from peak boundary. d, Bar chart showing the percentages of ATAC-seq peaks that overlap with oocyte PMDs for total peaks, peaks shared by various stages, or peaks specific for each stage. e, Heat maps showing the enrichment of ATAC-seq around the human oocyte PMDs in human early embryos and TBEs. The upstream and downstream regions are 1 × PMD length away from the PMD boundary. f, The average enrichment of ATAC-seq around the oocyte PMDs in human early embryos and TBEs. The upstream and downstream regions are 1 × PMD length away from the PMD boundary.

  9. Extended Data Fig. 9 Accessible chromatin state in mouse oocytes and pre-ZGA embryos.

    a, The expression of human KDM5B and mouse Kdm5b in oocytes, early embryos and ES cells. b, UCSC browser view showing DNA methylation levels (mC) in mouse sperm and oocyte, as well as open chromatin (DNase-seq or ATAC-seq) and H3K4me3 and H3K27me3 enrichment in mouse oocytes, zygotes and two-cell embryos. Mouse ES cell H3K4me3 signals are also shown to mark the promoter regions. c, Heat map showing the Spearman correlation between allelic DNase-seq and H3K4me3 signals in zygotes. M, maternal; P, paternal. d, UCSC browser view showing allelic DNase-seq, H3K4me3 and H3K27me3 enrichment in the mouse zygote. The mouse ES cell H3K4me3 signal is also shown. Regions showing paternal open chromatin and H3K4me3 in zygotes are shaded.

  10. Extended Data Fig. 10 Accessible chromatin state in mouse TBEs.

    a, UCSC browser view showing DNA methylation levels in mouse sperm and oocyte, and ATAC-seq signals in normal mouse embryos as well as allelic ATAC-seq and H3K4me3 enrichment in TBE samples. Mouse ES cell H3K4me3 signals are also shown to mark the promoter regions. b, Hierarchical clustering results showing the relationships of allelic accessible chromatin states between zygotes, TBEs, and two- and eight-cell embryos in mouse. c, Heat map showing open chromatin regions that are unique to zygotes (DNase-seq) or 45 h control embryos (ATAC-seq). The ATAC-seq enrichment in TBE samples is then matched and shown. d, Heat map showing the Spearman correlation between allelic ATAC-seq and H3K4me3 signals in TBEs. e, Transcription factor motifs identified from distal DNase-seq and allelic distal ATAC-seq peaks are shown. Motifs shared by pre-ZGA and post-ZGA stages or are specific for post-ZGA stages are noted. For transcription factors that have multiple family members with similar motifs (KLF and GATA), the highest expression and motif enrichment among all family members at each stage are shown. A random set of peaks that match the lengths and number of zygote maternal peaks was used as a control. It is worth noting that the RNA levels of CTCF appear to decline in TBEs, presumably owing to RNA degradation during extended transcription inhibition.

Supplementary information

  1. Reporting Summary

  2. Supplementary Table 1

    The mapping information for ATAC-seq and RNA-seq samples. The numbers of mapped and monoclonal reads for ATAC-seq and numbers of mapped and unique reads for RNA-seq are listed.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41586-018-0080-8

Further reading Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.