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.

Analytical tools and current challenges in the modern era of neuroepigenomics

Abstract

Over the past decade, rapid advances in epigenomics research have extensively characterized critical roles for chromatin regulatory events during normal periods of eukaryotic cell development and plasticity, as well as part of aberrant processes implicated in human disease. Application of such approaches to studies of the CNS, however, is more recent. Here we provide a comprehensive overview of available tools for analyzing neuroepigenomics data, as well as a discussion of pending challenges specific to the field of neuroscience. Integration of numerous unbiased genome-wide and proteomic approaches will be necessary to fully understand the neuroepigenome and the extraordinarily complex nature of the human brain. This will be critical to the development of future diagnostic and therapeutic strategies aimed at alleviating the vast array of heterogeneous and genetically distinct disorders of the CNS.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Initial pipelines of RNA-seq data analysis.
Figure 2: ChIP-seq analysis of brain.
Figure 3: Network inference approaches in neuroscience.

References

  1. Renthal, W. et al. Delta FosB mediates epigenetic desensitization of the c-fos gene after chronic amphetamine exposure. J. Neurosci. 28, 7344–7349 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Maze, I. et al. Essential role of the histone methyltransferase G9a in cocaine-induced plasticity. Science 327, 213–216 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Visel, A. et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 457, 854–858 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Cheung, I. et al. Developmental regulation and individual differences of neuronal H3K4me3 epigenomes in the prefrontal cortex. Proc. Natl. Acad. Sci. USA 107, 8824–8829 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Peleg, S. et al. Altered histone acetylation is associated with age-dependent memory impairment in mice. Science 328, 753–756 (2010).

    CAS  PubMed  Article  Google Scholar 

  6. Guo, J.U. et al. Neuronal activity modifies the DNA methylation landscape in the adult brain. Nat. Neurosci. 14, 1345–1351 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Maze, I. et al. Cocaine dynamically regulates heterochromatin and repetitive element unsilencing in nucleus accumbens. Proc. Natl. Acad. Sci. USA 108, 3035–3040 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Szulwach, K.E. et al. 5-hmC-mediated epigenetic dynamics during postnatal neurodevelopment and aging. Nat. Neurosci. 14, 1607–1616 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Zhou, Z. et al. Substance-specific and shared transcription and epigenetic changes in the human hippocampus chronically exposed to cocaine and alcohol. Proc. Natl. Acad. Sci. USA 108, 6626–6631 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. Hunter, R.G. et al. Acute stress and hippocampal histone H3 lysine 9 trimethylation, a retrotransposon silencing response. Proc. Natl. Acad. Sci. USA 109, 17657–17662 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. Mellén, M. et al. MeCP2 binds to 5hmC enriched within active genes and accessible chromatin in the nervous system. Cell 151, 1417–1430 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  12. Shulha, H.P. et al. Human-specific histone methylation signatures at transcription start sites in prefrontal neurons. PLoS Biol. 10, e1001427 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Sun, H. & Maze, I. et al. Morphine epigenomically regulates behavior through alterations in histone H3 lysine 9 dimethylation in the nucleus accumbens. J. Neurosci. 32, 17454–17464 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Lister, R. et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013).

    PubMed  PubMed Central  Google Scholar 

  15. Park, C.S. et al. Genome-wide analysis of H4K5 acetylation associated with fear memory in mice. BMC Genomics 14, 539 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Zhu, J. et al. Genome-wide chromatin state transitions associated with developmental and environmental cues. Cell 152, 642–654 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Feng, J. et al. Chronic cocaine-regulated epigenomic changes in mouse nucleus accumbens. Genome Biol. 15, R65 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. Guo, J.U. et al. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat. Neurosci. 17, 215–222 (2014).

    CAS  Article  PubMed  Google Scholar 

  19. Scobie, K.N. et al. Essential role of poly(ADP-ribosyl)ation in cocaine action. Proc. Natl. Acad. Sci. USA 111, 2005–2010 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. Shin, J., Ming, G. & Song, H. Decoding neuronal transcriptomes and epigenomes: high-throughput sequencing for neuroscience. Nat. Neurosci. 17, xxx–yyy (2014).

    Article  CAS  Google Scholar 

  21. Mortazavi, A. et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    CAS  PubMed  Article  Google Scholar 

  22. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Wilhelm, B.T. et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453, 1239–1243 (2008).

    CAS  PubMed  Article  Google Scholar 

  24. Marioni, J.C. et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Nookaew, I. et al. A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res. 40, 10084–10097 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Zhao, S. et al. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS ONE 9, e78644 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. DeLuca, D.S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Shen, L. et al. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  29. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Grabherr, M.G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Trapnell, C. et al. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Langmead, B. et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Article  Google Scholar 

  34. Engström, P.G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. Katz, Y. et al. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Deal, R.B. & Henikoff, S. A simple method for gene expression and chromatin profiling of individual cell types within a tissue. Dev. Cell 18, 1030–1040 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  39. Kozomara, A. & Griffiths-Jones, S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).

    CAS  Article  PubMed  Google Scholar 

  40. Friedländer, M.R. et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat. Biotechnol. 26, 407–415 (2008).

    PubMed  Article  CAS  Google Scholar 

  41. Lestrade, L. & Weber, M.J. snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs. Nucleic Acids Res. 34, D158–D162 (2006).

    CAS  PubMed  Article  Google Scholar 

  42. Sai Lakshmi, S. & Agrawal, S. piRNABank: a web resource on classified and clustered Piwi-interacting RNAs. Nucleic Acids Res. 36, D173–D177 (2008).

    CAS  PubMed  Article  Google Scholar 

  43. Chan, P.P. & Lowe, T.M. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, D93–D97 (2009).

    CAS  Article  PubMed  Google Scholar 

  44. Mituyama, T. et al. The Functional RNA Database 3.0: databases to support mining and annotation of functional RNAs. Nucleic Acids Res. 37, D89–D92 (2009).

    CAS  PubMed  Article  Google Scholar 

  45. Amaral, P.P. et al. lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res. 39, D146–D151 (2011).

    CAS  Article  PubMed  Google Scholar 

  46. Burge, S.W. et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 41, D226–D232 (2013).

    CAS  PubMed  Article  Google Scholar 

  47. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    CAS  Article  PubMed  Google Scholar 

  48. Xie, C. et al. NONCODEv4: exploring the world of long non-coding RNA genes. Nucleic Acids Res. 42, D98–D103 (2014).

    CAS  Article  PubMed  Google Scholar 

  49. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Robinson, M.D. et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  PubMed  Google Scholar 

  51. Law, C.W. et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. Smyth, G.K. limma: linear models for microarray data. in Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds. Gentleman, R., Carey, V., Huber, W., Irizarry, R. & Dudoit, S.) 397–420 (Springer, New York, 2005).

  53. Love, M.I. et al. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. bioRxiv beta 10.1101/002832 (2014).

  54. Rapaport, F. et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 14, R95 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  55. Liu, Y. et al. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301–304 (2014).

    CAS  PubMed  Article  Google Scholar 

  56. Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).

    CAS  PubMed  Google Scholar 

  57. Seyednasrollah, F. et al. Comparison of software packages for detecting differential expression in RNA-seq studies. Brief. Bioinform. 10.1093/bib/bbt086 (2013).

  58. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  60. Chen, K. et al. DANPOS: dynamic analysis of nucleosome position and occupancy by sequencing. Genome Res. 23, 341–351 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Kundaje, A. Phantompeakqualtools. https://code.google.com/p/phantompeakqualtools/ (2013).

  62. Landt, S.G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Chen, Y. et al. Systematic evaluation of factors influencing ChIP-seq fidelity. Nat. Methods 9, 609–614 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Carroll, T.S. et al. Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. Front. Genet. 5, 75 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  66. Zhang, Y. et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Shen, L. et al. diffReps: Detecting Differential Chromatin Modification Sites from ChIP-seq Data with Biological Replicates. PLoS ONE 8, e65598 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Zhu, L.J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 11, 237 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. Liang, K. & Keleş, S. Detecting differential binding of transcription factors with ChIP-seq. Bioinformatics 28, 121–122 (2012).

    CAS  PubMed  Article  Google Scholar 

  71. Wright, K. corrgram: plot a correlogram. R Package version 1.5 http://CRAN.R-project.org/package=corrgram (2013).

  72. Landt, S.G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. Shi, X. et al. ING2 PHD domain links histone H3 lysine 4 methylation to active gene repression. Nature 442, 96–99 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Saint-André, V. et al. Histone H3 lysine 9 trimethylation and HP1γ favor inclusion of alternative exons. Nat. Struct. Mol. Biol. 18, 337–344 (2011).

    Article  CAS  PubMed  Google Scholar 

  76. Cheng, C. et al. A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets. Genome Biol. 12, R15 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. Dong, X. et al. Modeling gene expression using chromatin features in various cellular contexts. Genome Biol. 13, R53 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. Kriaucionis, S. & Heintz, N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324, 929–930 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. Krueger, F. & Andrews, S.R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. Bock, C. Analysing and interpreting DNA methylation data. Nat. Rev. Genet. 13, 705–719 (2012).

    CAS  Article  PubMed  Google Scholar 

  82. Sun, D. et al. MOABS: model based analysis of bisulfite sequencing data. Genome Biol. 15, R38 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  83. Down, T.A. et al. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat. Biotechnol. 26, 779–785 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. Goldstein, D.B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009).

    CAS  PubMed  Article  Google Scholar 

  85. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

  87. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat Appl. Genet. Mol. Biol. 4, 17 (2005).

    Article  Google Scholar 

  88. Horvath, S. et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc. Natl. Acad. Sci. USA 103, 17402–17407 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  89. Miller, J.A. et al. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J. Neurosci. 28, 1410–1420 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. Miller, J.A. et al. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc. Natl. Acad. Sci. USA 107, 12698–12703 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  91. Miller, J.A. et al. Genes and pathways underlying regional and cell type changes in Alzheimer's disease. Genome Med 5, 48 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. Luo, R. et al. Genome-wide transcriptome profiling reveals the functional impact of rare de novo and recurrent CNVs in autism spectrum disorders. Am. J. Hum. Genet. 91, 38–55 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. Parikshak, N.N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. Rhinn, H. et al. Integrative genomics identifies APOE epsilon4 effectors in Alzheimer's disease. Nature 500, 45–50 (2013).

    CAS  PubMed  Article  Google Scholar 

  95. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease. Cell 153, 707–720 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9, 796–804 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. Zhu, J. et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet. 40, 854–861 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. Schadt, E.E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710–717 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. Swarup, V. & Geschwind, D.H. Alzheimer's disease: from big data to mechanism. Nature 500, 34–35 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. Evertts, A.G. et al. Modern approaches for investigating epigenetic signaling pathways. J. Appl. Physiol. (1985) 109, 927–933 (2010).

    CAS  Article  Google Scholar 

  101. Kullolli, M. et al. Intact microRNA analysis using high resolution mass spectrometry. J. Am. Soc. Mass Spectrom. 25, 80–87 (2014).

    CAS  PubMed  Article  Google Scholar 

  102. Karch, K.R. et al. Identification and interrogation of combinatorial histone modifications. Front. Genet. 4, 264 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  103. Ong, S.E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002).

    CAS  Article  PubMed  Google Scholar 

  104. Tweedie-Cullen, R.Y. et al. Identification of combinatorial patterns of post-translational modifications on individual histones in the mouse brain. PLoS ONE 7, e36980 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. Tan, M. et al. Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification. Cell 146, 1016–1028 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  106. Xie, Z. et al. Lysine succinylation and lysine malonylation in histones. Mol. Cell. Proteomics 11, 100–107 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. Dai, L. et al. Lysine 2-hydroxyisobutyrylation is a widely distributed active histone mark. Nat. Chem. Biol. 10, 365–370 (2014).

    CAS  Article  PubMed  Google Scholar 

  108. Britton, L.M. et al. Initial characterization of histone H3 serine 10 O-acetylation. Epigenetics 8, 1101–1113 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. Young, N.L. et al. High throughput characterization of combinatorial histone codes. Mol. Cell. Proteomics 8, 2266–2284 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. Garcia, B.A. et al. Characterization of neurohistone variants and post-translational modifications by electron capture dissociation mass spectrometry. Int. J. Mass Spectrom. 259, 184–196 (2007).

    CAS  Article  Google Scholar 

  111. Tian, Z. et al. Enhanced top-down characterization of histone post-translational modifications. Genome Biol. 13, R86 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  112. Frank, A.M. et al. Interpreting top-down mass spectra using spectral alignment. Anal. Chem. 80, 2499–2505 (2008).

    CAS  PubMed  Article  Google Scholar 

  113. DiMaggio, P.A. Jr. et al. A mixed integer linear optimization framework for the identification and quantification of targeted post-translational modifications of highly modified proteins using multiplexed electron transfer dissociation tandem mass spectrometry. Mol. Cell. Proteomics 8, 2527–2543 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. Perkins, D.N. et al. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    CAS  Article  PubMed  Google Scholar 

  115. Eng, J.K. et al. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).

    CAS  PubMed  Article  Google Scholar 

  116. Geer, L.Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958–964 (2004).

    CAS  PubMed  Article  Google Scholar 

  117. Wang, L.H. et al. pFind 2.0: a software package for peptide and protein identification via tandem mass spectrometry. Rapid Commun. Mass Spectrom. 21, 2985–2991 (2007).

    CAS  PubMed  Article  Google Scholar 

  118. Zhang, J. & Xin, L. et al. PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol. Cell Proteomics 11, M111 010587 (2012).

    PubMed  Article  CAS  Google Scholar 

  119. Beausoleil, S.A. et al. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24, 1285–1292 (2006).

    CAS  PubMed  Article  Google Scholar 

  120. Tackett, A.J. et al. Proteomic and genomic characterization of chromatin complexes at a boundary. J. Cell Biol. 169, 35–47 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  121. Voigt, P. et al. Asymmetrically modified nucleosomes. Cell 151, 181–193 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  122. Wang, C.I. et al. Chromatin proteins captured by ChIP-mass spectrometry are linked to dosage compensation in Drosophila. Nat. Struct. Mol. Biol. 20, 202–209 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  123. LeRoy, G. et al. Proteogenomic characterization and mapping of nucleosomes decoded by Brd and HP1 proteins. Genome Biol. 13, R68 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  124. Déjardin, J. & Kingston, R.E. Purification of proteins associated with specific genomic Loci. Cell 136, 175–186 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  125. Hoshino, A. & Fujii, H. Insertional chromatin immunoprecipitation: a method for isolating specific genomic regions. J. Biosci. Bioeng. 108, 446–449 (2009).

    CAS  PubMed  Article  Google Scholar 

  126. Byrum, S.D. et al. Purification of a specific native genomic locus for proteomic analysis. Nucleic Acids Res. 41, e195 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. King, M.C. & Wilson, A.C. Evolution at two levels in humans and chimpanzees. Science 188, 107–116 (1975).

    CAS  PubMed  Article  Google Scholar 

  128. McLean, C.Y. et al. Human-specific loss of regulatory DNA and the evolution of human-specific traits. Nature 471, 216–219 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  129. Chimpanzee Sequencing and Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437, 69–87 (2005).

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

  131. Xu, A.G. et al. Intergenic and repeat transcription in human, chimpanzee and macaque brains measured by RNA-Seq. PLoS Comput. Biol. 6, e1000843 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  132. Li, Z. et al. Evolutionary and ontogenetic changes in RNA editing in human, chimpanzee, and macaque brains. RNA 19, 1693–1702 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  133. Khrameeva, E.E. et al. Neanderthal ancestry drives evolution of lipid catabolism in contemporary Europeans. Nat. Commun. 5, 3584 (2014).

    PubMed  Article  CAS  Google Scholar 

  134. Heintzman, N.D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  135. Ripke, S. et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat. Genet. 45, 1150–1159 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. Brennand, K.J. et al. Modeling psychiatric disorders at the cellular and network levels. Mol. Psychiatry 17, 1239–1253 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. Lancaster, M.A. et al. Cerebral organoids model human brain development and microcephaly. Nature 501, 373–379 (2013).

    CAS  PubMed  Article  Google Scholar 

  139. Hackenberg, M. et al. miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res. 39, W132–W138 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. Jiang, Y. et al. Isolation of neuronal chromatin from brain tissue. BMC Neurosci. 9, 42 (2008).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  141. Evrony, G.D. et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  142. Grindberg, R.V. et al. RNA-sequencing from single nuclei. Proc. Natl. Acad. Sci. USA 110, 19802–19807 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  143. Shumway, M. et al. Archiving next generation sequencing data. Nucleic Acids Res. 38, D870–D871 (2010).

    CAS  PubMed  Article  Google Scholar 

  144. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  145. Tanizawa, H. et al. Mapping of long-range associations throughout the fission yeast genome reveals global genome organization linked to transcriptional regulation. Nucleic Acids Res. 38, 8164–8177 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. Gehlen, L.R. et al. Chromosome positioning and the clustering of functionally related loci in yeast is driven by chromosomal interactions. Nucleus 3, 370–383 (2012).

    PubMed  Article  Google Scholar 

  147. Yaffe, E. & Tanay, A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat. Genet. 43, 1059–1065 (2011).

    CAS  Article  PubMed  Google Scholar 

  148. Jin, F. et al. A high-resolution map of the three-dimensional chromatin interactome in human cells. Nature 503, 290–294 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank A. Soshnev for help with illustrations. This work was supported by grants from the US National Institute of Mental Health (5R01 MH094698 and P50 MH096890), US National Institute on Drug Abuse (P01 DA008227) and the Hope for Depression Research Foundation (HDRF).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eric J Nestler.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Maze, I., Shen, L., Zhang, B. et al. Analytical tools and current challenges in the modern era of neuroepigenomics. Nat Neurosci 17, 1476–1490 (2014). https://doi.org/10.1038/nn.3816

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3816

Further reading

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