Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

The effects of chromatin organization on variation in mutation rates in the genome

Nature Reviews Genetics volume 16, pages 213–223 (2015)Cite this article

Subjects

Key Points

  • Regional variation in mutation rates is an important phenomenon that affects genome evolution. It is determined by features of genomic landscape, with chromatin having an important influence.

  • Pairwise studies have revealed the complexity of correlation between chromatin and mutation rates. Some studies support a link between open chromatin and repressed mutations, whereas some argue for a link between closed chromatin and decreased mutation rates. Other studies highlight patterns that are base-specific, depend on epigenomic modifications in a genomic region, or are shaped by selection.

  • As features characterizing chromatin states are correlated with each other and with other genomic landscape features, multivariate segmentation analyses (using hidden Markov models) are providing a more nuanced depiction of the relationship between chromatin and germline mutation rates. Specifically, a prevalent genomic state with moderately high substitution and deletion rates is located in regions with closed chromatin, whereas a less abundant state with very high substitution, insertion and deletion rates is located in regions with open chromatin.

  • Several recent studies indicate a positive association between increased somatic mutation rates and closed chromatin in cancer genomes.

  • In several types of cancer, driver mutations are located in genes that regulate chromatin, leading to the hypothesis that consequent global or local chromatin remodelling results in malignancy.

  • Transcription of genes is influenced by chromatin state and leads to a biased substitution pattern that is probably due to transcription-coupled repair.

Abstract

The variation in local rates of mutations can affect both the evolution of genes and their function in normal and cancer cells. Deciphering the molecular determinants of this variation will be aided by the elucidation of distinct types of mutations, as they differ in regional preferences and in associations with genomic features. Chromatin organization contributes to regional variation in mutation rates, but its contribution differs among mutation types. In both germline and somatic mutations, base substitutions are more abundant in regions of closed chromatin, perhaps reflecting error accumulation late in replication. By contrast, a distinctive mutational state with very high levels of insertions and deletions (indels) and substitutions is enriched in regions of open chromatin. These associations indicate an intricate interplay between the nucleotide sequence of DNA and its dynamic packaging into chromatin, and have important implications for current biomedical research. This Review focuses on recent studies showing associations between chromatin state and mutation rates, including pairwise and multivariate investigations of germline and somatic (particularly cancer) mutations.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Variability in rates of base substitutions, small insertions and small deletions.
Figure 2: Aspects of chromatin organization that can affect evolutionary rates.
Figure 3: The distribution of mutation rate variation states across a typical autosome and genomic landscape features that characterize chromatin.

Similar content being viewed by others

References

  1. Wolfe, K. H., Sharp, P. M. & Li, W. H. Mutation rates differ among regions of the mammalian genome. Nature 337, 283–285 (1989).

    CAS  PubMed  Google Scholar 

  2. Makalowski, W. & Boguski, M. S. Synonymous and nonsynonymous substitution distances are correlated in mouse and rat genes. J. Mol. Evol. 47, 119–121 (1998).

    CAS  PubMed  Google Scholar 

  3. Hardison, R. C. et al. Covariation in frequencies of substitution, deletion, transposition, and recombination during eutherian evolution. Genome Res. 13, 13–26 (2003). This early genome-wide study illustrates not only regional variation but also regional co-variation among mutation rates of different types.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Hodgkinson, A. & Eyre-Walker, A. Variation in the mutation rate across mammalian genomes. Nature Rev. Genet. 12, 756–766 (2011). This is an excellent review on regional variation in mutation rates.

    CAS  PubMed  Google Scholar 

  5. Chiaromonte, F. et al. Association between divergence and interspersed repeats in mammalian noncoding genomic DNA. Proc. Natl Acad. Sci. USA 98, 14503–14508 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Kvikstad, E. M., Tyekucheva, S., Chiaromonte, F. & Makova, K. D. A macaque's-eye view of human insertions and deletions: differences in mechanisms. PLoS Comput. Biol. 3, e176 (2007).

    PubMed Central  Google Scholar 

  7. Yang, S. et al. Patterns of insertions and their covariation with substitutions in the rat, mouse, and human genomes. Genome Res. 14, 517–527 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Kvikstad, E. M. & Makova, K. D. The (r)evolution of SINE versus LINE distributions in primate genomes: sex chromosomes are important. Genome Res. 20, 600–613 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ananda, G., Chiaromonte, F. & Makova, K. D. A genome-wide view of mutation rate co-variation using multivariate analyses. Genome Biol. 12, R27 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Wilson Sayres, M. A. & Makova, K. D. Genome analyses substantiate male mutation bias in many species. Bioessays 33, 938–945 (2011).

    PubMed  Google Scholar 

  11. Campos-Sanchez, R., Kapusta, A., Feschotte, C., Chiaromonte, F. & Makova, K. D. Genomic landscape of human, bat, and ex vivo DNA transposon integrations. Mol. Biol. Evol. 31, 7 (2014).

    Google Scholar 

  12. Kimura, M. Evolutionary rate at the molecular level. Nature 217, 624–626 (1968).

    CAS  PubMed  Google Scholar 

  13. Chuang, J. H. & Li, H. Functional bias and spatial organization of genes in mutational hot and cold regions in the human genome. PLoS Biol. 2, e29 (2004).

    PubMed  PubMed Central  Google Scholar 

  14. Li, J. & Miller, W. Significance of interspecies matches when evolutionary rate varies. J. Comp. Biol. 10, 537–554 (2003).

    CAS  Google Scholar 

  15. Schuster-Bockler, B. & Lehner, B. Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 488, 504–507 (2012). This comprehensive analysis links chromatin states with base substitution mutations in cancer genomes.

    PubMed  Google Scholar 

  16. Hodgkinson, A., Chen, Y. & Eyre-Walker, A. The large-scale distribution of somatic mutations in cancer genomes. Hum. Mutat. 33, 136–143 (2012).

    CAS  PubMed  Google Scholar 

  17. Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015). This is a large-scale analysis of the association between mutation rates and chromatin states in several cancers and cell types.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Hellman, A. & Chess, A. Extensive sequence-influenced DNA methylation polymorphism in the human genome. Epigenet. Chromatin 3, 11 (2010).

    Google Scholar 

  19. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  20. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Stamatoyannopoulos, J. A. et al. Human mutation rate associated with DNA replication timing. Nature Genet. 41, 393–395 (2009).

    CAS  PubMed  Google Scholar 

  22. Tyekucheva, S. et al. Human–macaque comparisons illuminate variation in neutral substitution rates. Genome Biol. 9, R76 (2008).

    PubMed  PubMed Central  Google Scholar 

  23. Hellmann, I. et al. Why do human diversity levels vary at a megabase scale? Genome Res. 15, 1222–1231 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Schaibley, V. M. et al. The influence of genomic context on mutation patterns in the human genome inferred from rare variants. Genome Res. 23, 1974–1984 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Linardopoulou, E. V. et al. Human subtelomeres are hot spots of interchromosomal recombination and segmental duplication. Nature 437, 94–100 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Wagstaff, B. J. et al. Rescuing Alu: recovery of new inserts shows LINE-1 preserves Alu activity through A-tail expansion. PLoS Genet. 8, e1002842 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Ehrlich, M. & Wang, R. Y. 5-Methylcytosine in eukaryotic DNA. Science 212, 1350–1357 (1981).

    CAS  PubMed  Google Scholar 

  28. Gaffney, D. J. & Keightley, P. D. The scale of mutational variation in the murid genome. Genome Res. 15, 1086–1094 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Laurent, L. et al. Dynamic changes in the human methylome during differentiation. Genome Res. 20, 320–331 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Cairns, B. R. Chromatin remodeling: insights and intrigue from single-molecule studies. Nature Struct. Mol. Biol. 14, 989–996 (2007).

    CAS  Google Scholar 

  31. Zhou, V. W., Goren, A. & Bernstein, B. E. Charting histone modifications and the functional organization of mammalian genomes. Nature Rev. Genet. 12, 7–18 (2011).

    PubMed  Google Scholar 

  32. Beisel, C. & Paro, R. Silencing chromatin: comparing modes and mechanisms. Nature Rev. Genet. 12, 123–135 (2011).

    CAS  PubMed  Google Scholar 

  33. Fraser, P. & Bickmore, W. Nuclear organization of the genome and the potential for gene regulation. Nature 447, 413–417 (2007).

    CAS  PubMed  Google Scholar 

  34. Vanin, E. F., Henthorn, P. S., Kioussis, D., Grosveld, F. & Smithies, O. Unexpected relationships between four large deletions in the human β-globin gene cluster. Cell 35, 701–709 (1983).

    CAS  PubMed  Google Scholar 

  35. Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    CAS  PubMed  Google Scholar 

  36. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009). This paper presents genome-wide mapping of DNA interaction frequencies.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Kieffer-Kwon, K. R. et al. Interactome maps of mouse gene regulatory domains reveal basic principles of transcriptional regulation. Cell 155, 1507–1520 (2013).

    CAS  PubMed  Google Scholar 

  38. Filion, G. J. et al. Systematic protein location mapping reveals five principal chromatin types in Drosophila cells. Cell 143, 212–224 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Prendergast, J. G. et al. Chromatin structure and evolution in the human genome. BMC Evol. Biol. 7, 72 (2007).

    PubMed  PubMed Central  Google Scholar 

  40. Chen, X. et al. Nucleosomes suppress spontaneous mutations base-specifically in eukaryotes. Science 335, 1235–1238 (2012).

    CAS  PubMed  Google Scholar 

  41. Sasaki, S. et al. Chromatin-associated periodicity in genetic variation downstream of transcriptional start sites. Science 323, 401–404 (2009).

    CAS  PubMed  Google Scholar 

  42. Michaelson, J. J. et al. Whole-genome sequencing in autism identifies hot spots for de novo germline mutation. Cell 151, 1431–1442 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Taylor, J., Tyekucheva, S., Zody, M., Chiaromonte, F. & Makova, K. D. Strong and weak male mutation bias at different sites in the primate genomes: insights from the human–chimpanzee comparison. Mol. Biol. Evol. 23, 565–573 (2006).

    CAS  PubMed  Google Scholar 

  44. Cohen, N. M., Kenigsberg, E. & Tanay, A. Primate CpG islands are maintained by heterogeneous evolutionary regimes involving minimal selection. Cell 145, 773–786 (2011).

    CAS  PubMed  Google Scholar 

  45. Fei, J. & Ha, T. Watching DNA breath one molecule at a time. Proc. Natl Acad. Sci. USA 110, 17173–17174 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Prendergast, J. G. & Semple, C. A. Widespread signatures of recent selection linked to nucleosome positioning in the human lineage. Genome Res. 21, 1777–1787 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Tolstorukov, M. Y., Volfovsky, N., Stephens, R. M. & Park, P. J. Impact of chromatin structure on sequence variability in the human genome. Nature Struct. Mol. Biol. 18, 510–515 (2011).

    CAS  Google Scholar 

  48. Tang, Y. et al. H2A.Z nucleosome positioning has no impact on genetic variation in Drosophila genome. PLoS ONE 8, e58295 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Warnecke, T., Batada, N. N. & Hurst, L. D. The impact of the nucleosome code on protein-coding sequence evolution in yeast. PLoS Genet. 4, e1000250 (2008).

    PubMed  PubMed Central  Google Scholar 

  50. Washietl, S., Machne, R. & Goldman, N. Evolutionary footprints of nucleosome positions in yeast. Trends Genet. 24, 583–587 (2008).

    CAS  PubMed  Google Scholar 

  51. Ying, H., Epps, J., Williams, R. & Huttley, G. Evidence that localized variation in primate sequence divergence arises from an influence of nucleosome placement on DNA repair. Mol. Biol. Evol. 27, 637–649 (2010).

    CAS  PubMed  Google Scholar 

  52. Ying, H. & Huttley, G. Exploiting CpG hypermutability to identify phenotypically significant variation within human protein-coding genes. Genome Biol. Evol. 3, 938–949 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    CAS  PubMed  Google Scholar 

  55. Kadyrova, L. Y. et al. A reversible histone H3 acetylation cooperates with mismatch repair and replicative polymerases in maintaining genome stability. PLoS Genet. 9, e1003899 (2013).

    PubMed  PubMed Central  Google Scholar 

  56. Everitt, B. S. An R and S-Plus Companion to Multivariate Analysis (Springer, 2005).

    Google Scholar 

  57. Mardia, K. V., Kent, J. T. & Bibby, J. M. Multivariate Analysis (Academic Press, 1979).

    Google Scholar 

  58. Soneson, C., Lilljebjorn, H., Fioretos, T. & Fontes, M. Integrative analysis of gene expression and copy number alterations using canonical correlation analysis. BMC Bioinformatics 11, 191 (2010).

    PubMed  PubMed Central  Google Scholar 

  59. van der Sluis, S., Posthuma, D. & Dolan, C. V. TATES: efficient multivariate genotype–phenotype analysis for genome-wide association studies. PLoS Genet. 9, e1003235 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Gonzalez, I., Cao, K. A., Davis, M. J. & Dejean, S. Visualising associations between paired 'omics' data sets. BioData Min. 5, 19 (2012).

    PubMed  PubMed Central  Google Scholar 

  61. Kuruppumullage Don, P., Ananda, G., Chiaromonte, F. & Makova, K. D. Segmenting the human genome based on states of neutral genetic divergence. Proc. Natl Acad. Sci. USA 110, 14699–14704 (2013). This study presents segmentation of the human genome based on states of neutral genomic divergence used as a proxy for germline mutation rate.

    PubMed  PubMed Central  Google Scholar 

  62. Eddy, S. R. What is a hidden Markov model? Nature Biotech. 22, 1315–1316 (2004).

    CAS  Google Scholar 

  63. Majoros, W. H., Pertea, M., Antonescu, C. & Salzberg, S. L. GlimmerM, Exonomy and Unveil: three ab initio eukaryotic genefinders. Nucleic Acids Res. 31, 3601–3604 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Liu, L., De, S. & Michor, F. DNA replication timing and higher-order nuclear organization determine single-nucleotide substitution patterns in cancer genomes. Nature Commun. 4, 1502 (2013).

    Google Scholar 

  67. Cheedipudi, S., Genolet, O. & Dobreva, G. Epigenetic inheritance of cell fates during embryonic development. Front. Genet. 5, 19 (2014).

    PubMed  PubMed Central  Google Scholar 

  68. Wilson Sayres, M. A., Venditti, C., Pagel, M. & Makova, K. D. Do variations in substitution rates and male mutation bias correlate with life-history traits? A study of 32 mammalian genomes. Evolution 65, 2800–2815 (2011).

    PubMed  Google Scholar 

  69. Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Ho, A. S. et al. The mutational landscape of adenoid cystic carcinoma. Nature Genet. 45, 791–798 (2013).

    CAS  PubMed  Google Scholar 

  71. Jones, D. T. et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 488, 100–105 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Peifer, M. et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature Genet. 44, 1104–1110 (2012).

    CAS  PubMed  Google Scholar 

  73. Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012).

    CAS  PubMed  Google Scholar 

  74. Lan, F. & Shi, Y. Histone H3.3 and cancer: a potential reader connection. Proc. Natl Acad. Sci. USA (2014).

  75. Im, A. P. et al. DNMT3A and IDH mutations in acute myeloid leukemia and other myeloid malignancies: associations with prognosis and potential treatment strategies. Leukemia 28, 1774–1783 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Shlush, L. I. et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506, 328–333 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Walton, E. L., Francastel, C. & Velasco, G. DNMT3B prefers germ line genes and centromeric regions: lessons from the ICF syndrome and cancer and implications for diseases. Biology 3, 578–605 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Lee, R. S. & Roberts, C. W. Rhabdoid tumors: an initial clue to the role of chromatin remodeling in cancer. Brain Pathol. 23, 200–205 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Lee, R. S. et al. A remarkably simple genome underlies highly malignant pediatric rhabdoid cancers. J. Clin. Invest. 122, 2983–2988 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Green, P., Ewing, B., Miller, W., Thomas, P. J. & Green, E. D. Transcription-associated mutational asymmetry in mammalian evolution. Nature Genet. 33, 514–517 (2003).

    CAS  PubMed  Google Scholar 

  81. Louie, E., Ott, J. & Majewski, J. Nucleotide frequency variation across human genes. Genome Res. 13, 2594–2601 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Polak, P. & Arndt, P. F. Transcription induces strand-specific mutations at the 5′ end of human genes. Genome Res. 18, 1216–1223 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Mugal, C. F., von Grunberg, H. H. & Peifer, M. Transcription-induced mutational strand bias and its effect on substitution rates in human genes. Mol. Biol. Evol. 26, 131–142 (2009).

    CAS  PubMed  Google Scholar 

  84. Bailey, J. A. et al. Recent segmental duplications in the human genome. Science 297, 1003–1007 (2002).

    CAS  PubMed  Google Scholar 

  85. Samonte, R. V. & Eichler, E. E. Segmental duplications and the evolution of the primate genome. Nature Rev. Genet. 3, 65–72 (2002).

    CAS  PubMed  Google Scholar 

  86. Kelkar, Y., Tyekucheva, S., Chiaromonte, F. & Makova, K. D. The genome-wide determinants of microsatellite evolution. Genome Res. 18, 30–38 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

K.D.M. is supported by the US National Science Foundation grant DBI-0965596, and R.C.H. is supported by the US National Institutes of Health grants R01DK065806, RC2HG005573 and U54HG006998. The authors are grateful to P. Kuruppumullage Don and R. Campos-Sanchez for help with Figure 1.

Author information

Authors and Affiliations

  1. Department of Biology, Huck Institute for Genome Sciences, The Pennsylvania State University, University Park, State College, 16802, Pennsylvania, USA

    Kateryna D. Makova

  2. Department of Biochemistry and Molecular Biology, Huck Institute for Genome Sciences, The Pennsylvania State University, University Park, State College, 16802, Pennsylvania, USA

    Ross C. Hardison

Authors
  1. Kateryna D. Makova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ross C. Hardison
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kateryna D. Makova.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

PowerPoint slides

Glossary

Regional variation in mutation rates

(RViMR). The phenomenon whereby the rate of mutation changes along individual chromosomes.

Nucleosome occupancy

A measure of the degree to which a certain DNA region is packaged into a nucleosome, with the DNA wrapped tightly around a core of eight histone proteins.

Epigenomic features

Biochemical features that are associated with genomic DNA sequences but that are not the sequences themselves; examples include DNA methylation, histone modifications in chromatin, nuclease accessibility and transcription factor binding.

Genomic landscape features

Features that characterize the genome at levels beyond the primary DNA sequence. These include GC content, recombination rates, proximity to the closest telomere and replication timing.

CpG dinucleotides

Positions in the DNA sequence in which a cytosine is followed by a guanine.

Open chromatin

Chromatin in which the DNA is readily accessible to enzymes in the nucleus; it can be interpreted as regions with less compaction than bulk nucleosomes, depleted of nucleosomes or having highly remodelled nucleosomes.

Chromosome conformation capture

A method to quantitatively estimate the frequency of interaction between two different genomic regions using a crosslinking and intermolecular ligation assay to identify interacting sites.

Chromatin acetylation

Covalent modification of specific lysine residues in the amino-terminal tails of histones by the addition of an acetyl group.

Canonical correlation analysis

(CCA). A statistical analysis that considers two groups of variables simultaneously and finds significant linear combinations between them that have maximum correlations with each other.

Hidden Markov model

(HMM). A statistical model that analyses a sequence of observations defined by underlying states that are not observable ('hidden') but that can be inferred from the data. These states alternate along the sequence following a Markovian structure; that is, the state defining a given observation depends on the state governing the preceding observation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Makova, K., Hardison, R. The effects of chromatin organization on variation in mutation rates in the genome. Nat Rev Genet 16, 213–223 (2015). https://doi.org/10.1038/nrg3890

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3890

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing