Key Points
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Epigenetic modification, which is crucial for normal gene expression, is distributed heterogeneously in the mammalian genome.
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Molecular techniques allowing whole-genome studies of cytosine methylation and chromatin composition are allowing us to precisely define this heterogeneity.
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Mining of genomic annotations is also beginning to reveal relationships between epigenetic regulation and DNA sequences.
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These approaches are likely to yield insights into the DNA sequences that guide epigenetic processes to specific genomic regions — the goal of epigenomic studies.
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The data generated by these approaches are difficult to analyse, not only because the number of variables generated is large, but also because these variables are correlated with each other.
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These correlations include the co-localization of groups of transposable elements in the mammalian genome, the reasons for which remain unknown. The distinctive distribution of such elements at regions undergoing monoallelic expression suggests that the accumulation of these supposedly neutral elements might be influenced by evolutionary selection.
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The main problem that epigenomic studies are likely to face does not involve data generation but data analysis. We propose that statistical model selection techniques might be the best way of dealing with these unusually complex data sets.
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Even with the best statistical approaches, the assumption of a causative, mechanistic relationship between a DNA sequence feature and a correlated epigenetic outcome might be incorrect, because epigenetic modification can also influence DNA sequence composition.
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The study of epigenomics should ultimately involve the integration of molecular, bioinformatic and statistical techniques, and extends to encompass genome evolution and transposable element biology.
Abstract
Epigenomic studies aim to define the location and nature of the genomic sequences that are epigenetically modified. Much progress has been made towards whole-genome epigenetic profiling using molecular techniques, but the analysis of such large and complex data sets is far from trivial given the correlated nature of sequence and functional characteristics within the genome. We describe the statistical solutions that help to overcome the problems with data-set complexity, in anticipation of the imminent wealth of data that will be generated by new genome-wide epigenetic profiling and DNA sequence analysis techniques. So far, epigenomic studies have succeeded in identifying CpG islands, but recent evidence points towards a role for transposable elements in epigenetic regulation, causing the fields of study of epigenetics and transposable element biology to converge.
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Dedicated with affection to F. Ruddle, on the occasion of his retirement.
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Glossary
- CHROMATIN IMMUNOPRECIPITATION
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Intact nuclei are gently fixed to maintain the physical relationship of DNA-binding molecules to genomic DNA. The chromatin (DNA plus bound molecules) is sheared to small fragments and exposed to an antibody that immunoprecipitates one of the bound molecules selectively. The sites of binding of the molecule (usually protein) of interest are apparent from their enrichment in the immunoprecipitated fraction of the genome.
- GENOMIC IMPRINTING
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The epigenetic marking of a locus on the basis of parental origin, which results in monoallelic gene expression.
- ANDROGENETIC
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A diploid offspring that is produced from two sets of haploid paternal gametes and no maternal contribution.
- PARTHENOGENETIC
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A diploid offspring that is produced from two sets of haploid maternal gametes and no paternal contribution.
- BISULPHITE SEQUENCING
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A technique that is used to identify methylcytosines that depends on the relative resistance of the conversion of methylcytosine to uracil compared with cytosine. PCR amplification and sequencing of the DNA following conversion shows a thymine where a cytosine was located, whereas persistence of a cytosine reflects its methylation in the starting DNA sample.
- MALDI MASS SPECTROMETRY
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Matrix-assisted laser desorption/ionization mass spectroscopy is based on the co-crystallization of a test compound with an ultraviolet-light-absorbing matrix, which allows ionization using laser excitation to determine the mass of the test compound.
- L1 LINES
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The currently active long interspersed nuclear element in the eutherian genome. These elements are capable of retrotransposition but lack the long terminal repeats that characterize retroviruses.
- MIR AND ALU SINES
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Short interspersed nuclear elements, of which the Alu type is currently active in primates, whereas the MIR (mammalian interspersed repeat) type became extinct since eutherians diverged from marsupials.
- UNIVARIATE ANALYSIS
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Analysis of functions of one variable.
- MULTIVARIABLE ANALYSIS
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Analysis of functions of several variables.
- GIEMSA (G) BANDS
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The chromosomal bands that are resistant to protease treatment (relative to reverse (R) bands), allowing them to stain more darkly with Giemsa stain. In chromosomal ideograms, the G bands are indicated by black/grey regions, the R bands by white regions.
- HIERARCHICAL CLUSTERING
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An unsupervised clustering technique. Each data point initially forms a separate cluster and then clusters are merged sequentially based on similarity, reducing the number of clusters at each step until only one cluster is left.
- K-MEANS CLUSTERING
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An unsupervised clustering technique. Data points are partitioned into a predetermined number of non-hierarchical clusters based on similarity.
- LOGISTIC REGRESSION
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A statistical model that is used when the outcome is binary in nature. Relates the log odds of Pr(event) to a linear combination of predictor variables.
- TREE-BASED CART MODELS
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A statistical tool that is used for identifying structure in data that uses binary recursive partitioning to obtain a tree classifier.
- DISCRIMINANT FUNCTION ANALYSIS
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A statistical method that is used to determine which variables and function best maximize the distance between two groups. Similar to logistic regression computationally, but generally less flexible in its assumptions.
- BOOTSTRAP METHODS
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Computer-intensive methods for statistical analysis. Treats the observed sample as the population and resamples from this population.
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Fazzari, M., Greally, J. Epigenomics: beyond CpG islands. Nat Rev Genet 5, 446–455 (2004). https://doi.org/10.1038/nrg1349
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DOI: https://doi.org/10.1038/nrg1349
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