Methods for ChIP-seq Normalization and Their Application for Analysis of Regulatory Elements in Brain Cells

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has become one of the major tools to elucidate gene expression programs. Similar to other molecular profiling methods, ChIP-seq is sensetive to several technical biases which affect downstream results, especially in cases when material quality is difficult to control, for example, frozen post-mortem human tissue. However methods for bioinformatics analysis improve every year and allow to mitigate these effects after sequencing by adjusting for both technical ChIP-seq biases and more general biological biases like post-mortem interval or cell heterogenity of the sample. Here we review a wide selection of ChIP-seq normalization methods with a focus on application in specific experimental settings, in particular when brain tissue is investigated.

About the authors

F. E. Gusev

Vavilov Institute of General Genetics Russian Academy of Sciences; Center for Genetics and Life Science, “Sirius” University of Science and Technology

Author for correspondence.
Email: gusev@vigg.ru
Russia, 119991, Moscow; Russia, 354340, Krasnodar krai, p. Sirius

T. V. Andreeva

Vavilov Institute of General Genetics Russian Academy of Sciences; Center for Genetics and Life Science, “Sirius” University of Science and Technology

Author for correspondence.
Email: an_tati@vigg.ru
Russia, 119991, Moscow; Russia, 354340, Krasnodar krai, p. Sirius

E. I. Rogaev

Vavilov Institute of General Genetics Russian Academy of Sciences; Center for Genetics and Life Science, “Sirius” University of Science and Technology

Author for correspondence.
Email: evivrog@gmail.com
Russia, 119991, Moscow; Russia, 354340, Krasnodar krai, p. Sirius

References

  1. Fyodorov D.V., Zhou B.-R., Skoultchi A.I., Bai Y. Emerging roles of linker histones in regulating chromatin structure and function // Nat. Rev. Mol. Cell. Biol. 2018. V. 19. № 3. P. 192–206. https://doi.org/10.1038/nrm.2017.94
  2. Park P.J. ChIP-seq: advantages and challenges of a maturing technology // Nat. Rev. Genet. 2009. V. 10. № 10. P. 669–680. https://doi.org/10.1038/nrg2641
  3. Furey T.S. ChIP-seq and beyond: New and improved methodologies to detect and characterize protein-DNA interactions // Nat. Rev. Genet. 2012. V. 13. № 12. P. 840–852. https://doi.org/10.1038/nrg3306
  4. Altman N. Batches and blocks, sample pools and subsamples in the design and analysis of gene expression studies // Batch Effects and Noise in Microarray Experiments. UK, Chichester: John Wiley & Sons, Ltd, 2009. P. 33–50. https://doi.org/10.1002/9780470685983.ch4
  5. Goh W.W.B., Wang W., Wong L. Why batch effects matter in omics data, and how to avoid them // Trends Biotechnol. 2017. V. 35. № 6. P. 498–507. https://doi.org/10.1016/j.tibtech.2017.02.012
  6. Jung Y.L., Luquette L.J., Ho J.W.K. et al. Impact of sequencing depth in ChIP-seq experiments // Nucl. Acids Res. 2014. V. 42. № 9. https://doi.org/10.1093/nar/gku178
  7. Sundaram A.Y.M., Hughes T., Biondi S. et al. A comparative study of ChIP-seq sequencing library preparation methods // BMC Genomics. 2016. V. 17. № 1. P. 816. https://doi.org/10.1186/s12864-016-3135-y
  8. Teng M., Du D., Chen D., Irizarry R.A. Characterizing batch effects and binding site-specific variability in ChIP-seq data // NAR Genomics and Bioinformatics. 2021. V. 3. № 4. https://doi.org/10.1093/nargab/lqab098
  9. Orlando D.A., Chen M.W., Brown V.E. et al. Quantitative ChIP-seq normalization reveals global modulation of the epigenome // Cell Reports. 2014. V. 9. № 3. P. 1163–1170. https://doi.org/10.1016/j.celrep.2014.10.018
  10. Gu B., Lee M.G. Histone H3 lysine 4 methyltransferases and demethylases in self-renewal anddifferentiation of stem cells // Cell & Bioscience. 2013. V. 3. № 1. https://doi.org/10.1186/2045-3701-3-39
  11. Nakato R., Sakata T. Methods for ChIP-seq analysis: A practical workflow and advanced applications // Methods. 2021. V. 187. P. 44–53. https://doi.org/10.1016/j.ymeth.2020.03.005
  12. Price E.M., Robinson W.P. Adjusting for batch effects in DNA methylation microarray data, a lesson learned // Front. Genet. 2018. V. 9. https://doi.org/10.3389/fgene.2018.00083
  13. Lun A.T.L., Smyth G.K. csaw: A Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows // Nucl. Acids Res. 2016. V. 44. № 5. https://doi.org/10.1093/nar/gkv1191
  14. Diaz A., Park K., Lim D.A., Song J.S. Normalization, bias correction, and peak calling for ChIP-seq // Stat. Appl. Genet. Mol. Biol. 2012. V. 11. № 3. https://doi.org/10.1515/1544-6115.1750
  15. Stark R., Brown G. DiffBind: Differential Binding Analysis of ChIP-seq Peak Data. Bioconductor version: Release (3.16), 2022. https://doi.org/10.18129/B9.bioc.DiffBind
  16. Robinson M.D., McCarthy D.J., Smyth G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data // Bioinformatics. 2010. V. 26. № 1. P. 139–140. https://doi.org/10.1093/bioinformatics/btp616
  17. Ji H., Jiang H., Ma W., Wong W.H. Using CisGenome to analyze ChIP-chip and ChIP-seq data // Curr. Protoc. Bioinformatics. 2011. https://doi.org/10.1002/0471250953.bi0213s33
  18. Kharchenko P.V., Tolstorukov M.Y., Park P.J. Design and analysis of ChIP-seq experiments for DNA-binding proteins // Nat. Biotechnol. 2008. V. 26. № 12. P. 1351–1359. https://doi.org/10.1038/nbt.1508
  19. Xu H., Handoko L., Wei X. et al. A signal-noise model for significance analysis of ChIP-seq with negative control // Bioinformatics. 2010. V. 26. № 9. P. 1199–1204. https://doi.org/10.1093/bioinformatics/btq128
  20. Liang K., Keleş S. Normalization of ChIP-seq data with control // BMC Bioinformatics. 2012. V. 13. № 1. https://doi.org/10.1186/1471-2105-13-199
  21. Shao Z., Zhang Y., Yuan G.-C. et al. MAnorm: A robust model for quantitative comparison of ChIP-Seq data sets // Genome Biol. 2012. V. 13. № 3. https://doi.org/10.1186/gb-2012-13-3-r16
  22. Tu S., Li M., Chen H. et al. MAnorm2 for quantitatively comparing groups of ChIP-seq samples // Genome Res. 2021. V. 31. № 1. P. 131–145. https://doi.org/10.1101/gr.262675.120
  23. Nair N.U., Sahu A.D., Bucher P., Moret B.M.E. ChIPnorm: A statistical method for normalizing and identifying differential regions in histone modification ChIP-seq libraries // PLoS One. 2012. V. 7. № 8. https://doi.org/10.1371/journal.pone.0039573
  24. Polit L., Kerdivel G., Gregoricchio S. et al. CHIPIN: ChIP-seq inter-sample normalization based on signal invariance across transcriptionally constant genes // BMC Bioinformatics. 2021. V. 22. № 1. P. 407. https://doi.org/10.1186/s12859-021-04320-3
  25. Allhoff M., Seré K., F Pires J. et al. Differential peak calling of ChIP-seq signals with replicates with THOR // Nucl. Acids Res. 2016. V. 44. № 20. https://doi.org/10.1093/nar/gkw680
  26. Lovén J., Orlando D.A., Sigova A.A. et al. Revisiting global gene expression analysis // Cell. 2012. V. 151. № 3. P. 476–482. https://doi.org/10.1016/j.cell.2012.10.012
  27. Kanno J., Aisaki K., Igarashi K. et al. “Per cell” normalization method for mRNA measurement by quantitative PCR and microarrays // BMC Genomics. 2006. V. 7. № 1. https://doi.org/10.1186/1471-2164-7-64
  28. Egan B., Yuan C.-C., Craske M.L. et al. An alternative approach to ChIP-Seq normalization enables detection of genome-wide changes in histone H3 lysine 27 trimethylation upon EZH2 inhibition // PLoS One. 2016. V. 11. № 11. https://doi.org/10.1371/journal.pone.0166438
  29. Jin H., Kasper L.H., Larson J.D. et al. ChIPseqSpikeInFree: a ChIP-seq normalization approach to reveal global changes in histone modifications without spike-in // Bioinformatics. 2020. V. 36. № 4. P. 1270–1272. https://doi.org/10.1093/bioinformatics/btz720
  30. Pathania M., De Jay N., Maestro N. et al. H3.3K27M cooperates with Trp53 loss and PDGFRA gain in mouse embryonic neural progenitor cells to induce invasive high-grade gliomas // Cancer Cell. 2017. V. 32. № 5. P. 684–700. e9. https://doi.org/10.1016/j.ccell.2017.09.014
  31. Xiang G., Keller C.A., Giardine B. et al. S3norm: Simultaneous normalization of sequencing depth and signal-to-noise ratio in epigenomic data // Nucl. Acids Res. 2020. V. 48. № 8. P. e43. https://doi.org/10.1093/nar/gkaa105
  32. Angelini C., Heller R., Volkinshtein R., Yekutieli D. Is this the right normalization? A diagnostic tool for ChIP-seq normalization // BMC Bioinformatics. 2015. V. 16. № 1. P. 150. https://doi.org/10.1186/s12859-015-0579-z
  33. Bryois J., Garrett M.E., Song L. et al. Evaluation of chromatin accessibility in prefrontal cortex of individuals with schizophrenia // Nat. Commun. 2018. V. 9. № 1. P. 3121. https://doi.org/10.1038/s41467-018-05379-y
  34. Tsai P.-C., Glastonbury C.A., Eliot M.N. et al. Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health // Clin. Epigenetics. 2018. V. 10. P. 126. https://doi.org/10.1186/s13148-018-0558-0
  35. Ritchie M.E., Phipson B., Wu D. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies // Nucl. Acids Res. 2015. V. 43. № 7. P. e47. https://doi.org/10.1093/nar/gkv007
  36. Love M.I., Huber W., Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 // Genome Biol. 2014. V. 15. № 12. P. 550. https://doi.org/10.1186/s13059-014-0550-8
  37. Zhang Y., Parmigiani G., Johnson W.E. ComBat-seq: Batch effect adjustment for RNA-seq count data // NAR Genomics and Bioinformatics. 2020. V. 2. № 3. https://doi.org/10.1093/nargab/lqaa078
  38. Johnson W.E., Li C., Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods // Biostatistics. 2007. V. 8. № 1. P. 118–127. https://doi.org/10.1093/biostatistics/kxj037
  39. Shulha H.P., Cheung I., Guo Y. et al. Coordinated cell type–specific epigenetic remodeling in prefrontal cortex begins before birth and continues into early adulthood // PLoS Genetics. 2013. V. 9. № 4. https://doi.org/10.1371/journal.pgen.1003433
  40. Gusev F.E., Reshetov D.A., Mitchell A.C. et al. Epigenetic-genetic chromatin footprinting identifies novel and subject-specific genes active in prefrontal cortex neurons // The FASEB J. 2019. V. 33. № 7. P. 8161–8173. https://doi.org/10.1096/fj.201802646R
  41. Nott A., Holtman I.R., Coufal N.G. et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association // Science. 2019. V. 366. № 6469. P. 1134–1139. https://doi.org/10.1126/science.aay0793
  42. Dunham I., Kundaje A., Aldred S.F. et al. An integrated encyclopedia of DNA elements in the human genome // Nature. 2012. V. 489. № 7414. P. 57–74. https://doi.org/10.1038/nature11247
  43. Ouyang Z., Bourgeois-Tchir N., Lyashenko E. et al. Characterizing the composition of iPSC derived cells from bulk transcriptomics data with CellMap // Sci. Rep. 2022. V. 12. № 1. P. 17394. https://doi.org/10.1038/s41598-022-22115-1
  44. Jew B., Alvarez M., Rahmani E. et al. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information // Nat. Commun. 2020. V. 11. № 1. P. 1971. https://doi.org/10.1038/s41467-020-15816-6
  45. Li H., Sharma A., Luo K. et al. DeconPeaker, A deconvolution model to identify cell types based on chromatin accessibility in ATAC-seq data of mixture samples // Frontiers in Genet. 2020. V. 11.
  46. Leek J.T. svaseq: removing batch effects and other unwanted noise from sequencing data // Nucl. Acids Res. 2014. V. 42. № 21. P. e161. https://doi.org/10.1093/nar/gku864
  47. Risso D., Ngai J., Speed T.P., Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples // Nat. Biotechnol. 2014. V. 32. № 9. P. 896–902. https://doi.org/10.1038/nbt.2931
  48. Akbarian S., Liu C., Knowles J.A. et al. The psychENCODE project // Nat. Neurosci. 2015. V. 18. № 12. P. 1707–1712. https://doi.org/10.1038/nn.4156
  49. Amiri A., Coppola G., Scuderi S. et al. Transcriptome and epigenome landscape of human cortical development modeled in organoids // Science. 2018. V. 362. № 6420. https://doi.org/10.1126/science.aat6720
  50. Girdhar K., Hoffman G.E., Jiang Y. et al. Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome // Nat. Neurosci. 2018. V. 21. № 8. P. 1126–1136. https://doi.org/10.1038/s41593-018-0187-0
  51. Girdhar K., Hoffman G.E., Bendl J. et al. Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains // Nat. Neurosci. 2022. V. 25. № 4. P. 474–483. https://doi.org/10.1038/s41593-022-01032-6
  52. Persico G., Casciaro F., Amatori S. et al. Histone H3 Lysine 4 and 27 Trimethylation Landscape of Human Alzheimer’s Disease // Cells. Multidisciplinary Digital Publ. Institute. 2022. V. 11. № 4. https://doi.org/10.3390/cells11040734
  53. Klein H.-U., McCabe C., Gjoneska E. et al. Epigenome-wide study uncovers large-scale changes in histone acetylation driven by tau pathology in the aging and Alzheimer human brain // Nat. Neurosci. 2019. V. 22. № 1. P. 37–46. https://doi.org/10.1038/s41593-018-0291-1
  54. Mack S.C., Singh I., Wang X. et al. Chromatin landscapes reveal developmentally encoded transcriptional states that define human glioblastoma // J. Exp. Med. 2019. V. 216. № 5. P. 1071–1090. https://doi.org/10.1084/jem.20190196
  55. Anders S., Huber W. Differential expression analysis for sequence count data // Genome Biol. 2010. V. 11. № 10. https://doi.org/10.1186/gb-2010-11-10-r106
  56. Stępniak K., Machnicka M.A., Mieczkowski J. et al. Mapping chromatin accessibility and active regulatory elements reveals pathological mechanisms in human gliomas // Nat. Commun. 2021. V. 12. № 1. P. 3621. https://doi.org/10.1038/s41467-021-23922-2

Supplementary files

Supplementary Files
Action
1. JATS XML
2.

Download (144KB)

Copyright (c) 2023 Ф.Е. Гусев, Т.В. Андреева, Е.И. Рогаев

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies