Multi-omics integration strategies for animal epigenetic studies — A review
Open accessAbstract
Genome-wide studies provide considerable insights into the genetic background of animals; however, the inheritance of several heritable factors cannot be elucidated. Epigenetics explains these heritabilities, including those of genes influenced by environmental factors. Knowledge of the mechanisms underlying epigenetics enables understanding the processes of gene regulation through interactions with the environment. Recently developed next-generation sequencing (NGS) technologies help understand the interactional changes in epigenetic mechanisms. There are large sets of NGS data available; however, the integrative data analysis approaches still have limitations with regard to reliably interpreting the epigenetic changes. This review focuses on the epigenetic mechanisms and profiling methods and multi-omics integration methods that can provide comprehensive biological insights in animal genetic studies.
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In mammalian somatic cells, DNA methylation occurs predominantly in 98% of 5'CpG3' dinucleotides; in embryonic stem cells, only 75% of CpG dinucleotides are methylated.
“In mammalian somatic cells, this modification occurs predominantly in 98% of 5’CpG3’ dinucleotides; on the other hand, in embryonic stem cells, only 75% of CpG dinucleotides are methylated.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The DNA methylation section gives the percentages as the share of DNA methylation that occurs at CpG sites: 98% in mammalian somatic cells and 75% in embryonic stem cells. The claim instead presents them as the share of CpG dinucleotides that are methylated. The same section says 60%–80% of CpGs in the human genome are methylated. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe number of methylations or demethylations can affect gene expression, referred to as hypomethylation and hypermethylation, respectively.
“A major mechanism in silencing gene expression is methylation (Figure 1(AII)) and the number of methylations or demethylations can affect gene expression; this is commonly referred to as hypomethylation and hypermethylation, respectively.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The Epigenetic Mechanisms sections on DNA methylation and histone modification say that methylation patterns can affect gene expression, including silencing transcription at methylated CpG-island promoters. They do not call methylation “hypomethylation” or demethylation “hypermethylation,” as the citing sentence does; a search for those terms found no such passage in our copy. Page numbers were not available. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe first level of chromatin organization is the nucleosome, composed of two copies of histones, which are the core protein components of the chromatin complex.
“The first level of chromatin organization is known as the nucleosome, and it is composed of two copies of histones, which are the core protein components of the chromatin complex.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The Histone modification section identifies the nucleosome as the first level of chromatin organization and histones as core proteins of chromatin. But it specifies two copies *each* of H2A, H2B, H3, and H4—eight histone proteins in the core—not simply two copies of histones. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThere is an external histone to the octamer known as the linker histone or H1 that participates in compaction of nucleosomes.
“There is an external histone to the octamer known as the linker histone or H1 that participates in the compaction of the nucleosomes [53].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The Histone modification section identifies the nucleosome’s octameric core as containing H2A, H2B, H3, and H4, which supports the claim that H1 is not part of the octamer. It does not identify H1 as a linker histone or say that H1 participates in nucleosome compaction (page unavailable). (LLM, conf=0.82)
Experimental — accuracy not yet measuredDNA methylation plays an important role in regulation of gene expression, where methylation of CpG islands and promoters is a recognition platform for transcription factors or proteins that recruit factors.
“DNA methylation plays an important role in different molecular mechanisms, such as the regulation of gene expression, where the methylation of CpG islands and promoters is a recognition platform for different transcription factors or proteins that recruit different factors.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The DNA methylation section (page not provided) says methylated CpG islands can inhibit transcription and that DNA methylation has a functional role in transcriptional regulation. The DNA methylation profiling section also describes a protein that binds methylated CpG. Neither passage establishes the more specific claim that methylated CpG islands and promoters serve as a recognition platform for transcription factors or proteins that recruit other factors. (LLM, conf=0.85)
Experimental — accuracy not yet measured
miRNAs are derived from transcripts, intronic and intergenic sequences, form a unique hairpin structure processed into mature form, and mediate gene silencing at the post-transcriptional level by coupling to an RNA-induced silencing complex in the cytoplasm.
“This type of ncRNA is derived from transcripts, intronic and intergenic sequences, that form a unique hairpin structure to be processed into their mature form, which allows them to mediate gene silencing at the post-transcriptional level by coupling to an RNA-induced silencing complex in the cytoplasm.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredlncRNAs can serve as precursors of siRNAs.
“It is currently recognized that lncRNAs can serve as precursors of siRNAs; they can serve to modulate gene expression through the recruitment of chromatin-modifying enzymes, and they also serve as scaffolds for multiple proteins necessary for the assembly of ribonucleoprotein complexes, which can modulate the marks on histone tails [53,61].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The RNA interference section says that some lncRNAs serve as precursors of short RNAs and identifies siRNAs as one type of short non-coding RNA. It does not specifically say that lncRNAs serve as precursors of siRNAs, so the claim is more specific than the source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIt is estimated that 30% of human genes are regulated by miRNAs.
“Very interestingly, it is estimated that 30% of human genes are regulated by miRNAs [53,61].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The RNA interference section says miRNAs regulate many genes and describes a study of potential targets among 13,000 human genes. It does not estimate that miRNAs regulate 30% of human genes; its 50% figure concerns chromosomal regions, not genes. No page number is available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredlncRNAs can modulate gene expression through the recruitment of chromatin-modifying enzymes.
“It is currently recognized that lncRNAs can serve as precursors of siRNAs; they can serve to modulate gene expression through the recruitment of chromatin-modifying enzymes, and they also serve as scaffolds for multiple proteins necessary for the assembly of ribonucleoprotein complexes, which can modulate the marks on histone tails [53,61].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The RNA interference section says lncRNAs have roles in regulating gene expression. It does not say they do so by recruiting chromatin-modifying enzymes; the Histone modification section discusses those enzymes without linking their recruitment to lncRNAs. No page numbers are available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredMethylation of the gene body appears to contribute to elongation during transcription, regulation of alternative splicing, and regulation of non-coding RNAs.
“However, the methylation of the gene body appears to contribute to elongation during transcription, the regulation of alternative splicing, and the regulation of non-coding RNAs.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The DNA methylation section (page unavailable) says methylation in genes may stimulate transcriptional elongation and splicing, supporting those parts of the claim. It does not specify alternative splicing or say that gene-body methylation regulates non-coding RNAs. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMature miRNAs are RNA molecules around 20 nucleotides in length, widely conserved among species, with diverse functions, and involved in regulation of gene expression in multicellular eukaryotes and some unicellular eukaryotes.
“Mature miRNAs are RNA molecules of around 20 nucleotides in length that are widely conserved among species with diverse functions and involved in the regulation of gene expression in multicellular eukaryotes and some unicellular eukaryotes.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The RNA interference section describes miRNAs as evolutionarily conserved, approximately 24 nucleotides long, and regulators of hundreds of genes through effects on mRNA. That broadly supports the claimed size and gene-regulatory role, but the section does not say that miRNAs function in some unicellular eukaryotes. (LLM, conf=0.86)
Experimental — accuracy not yet measuredInteractions between RNA and DNA can change gene expression without altering the DNA sequence.
“These interactions between RNA and DNA can change gene expression without altering the DNA sequence, which remains constant.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The Introduction (page unavailable) says epigenetic changes can affect gene expression without altering the DNA sequence and lists RNA interference as a mechanism. The RNA interference section says non-coding RNAs regulate gene expression, but it does not identify direct interactions between RNA and DNA as the mechanism claimed. (LLM, conf=0.83)
Experimental — accuracy not yet measuredDNA methylation can occur in non-CpG dinucleotides, such as CpA, CpT, or CpC.
“However, there is evidence that the methylation of DNA can occur in non-CpG dinucleotides, such as CpA, CpT, or CpC [53].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
A major mechanism in silencing gene expression is methylation.
“A major mechanism in silencing gene expression is methylation (Figure 1(AII)) and the number of methylations or demethylations can affect gene expression; this is commonly referred to as hypomethylation and hypermethylation, respectively.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
Histones H2A, H2B, H3, and H4 are assembled in the nucleus as an octamer core surrounded by DNA.
“Histones H2A, H2B, H3, and H4 are assembled in the nucleus, as an octamer core surrounded by DNA.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
DNA methylation is highly relevant during X chromosome inactivation, genomic imprinting, chromosome stability maintenance, and suppression of repetitive elements in the genome.
“In addition, DNA methylation is highly relevant during X chromosome inactivation, genomic imprinting, chromosome stability maintenance, and the suppression of repetitive elements in the genome [53].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
DNA methylation can remodel chromatin structure and inhibit transcription and subsequent gene expression.
“Also, it can remodel the chromatin structure and inhibit transcription and subsequent gene expression [53].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
DNA methylation consists of the covalent addition of a methyl group (-CH3) to carbon five of the pyrimidine ring of cytosine, resulting in 5-methylcytosine (5 mC).
“This mechanism consists of the covalent addition of a methyl group (-CH3) to carbon five of the pyrimidine ring of cytosine, resulting in 5-methylcytosine (5 mC) [53].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
lncRNAs are molecules of 200 or more nucleotides in length and can originate from a sense or antisense form depending on their proximity to protein-coding genes.
“lncRNAs are a different type of molecule of 200 or more nucleotides in length, and can originate from a sense or antisense form, depending on their proximity to the genes that encode proteins.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
lncRNAs can be bidirectional and can come from intronic or intergenic sequences, or from disruption of open reading frames or chromosomal rearrangements.
“These lncRNAs can be bidirectional, and they can come from intronic or intergenic sequences. They can also be derived from the disruption of open reading frames or chromosomal rearrangements.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
lncRNAs serve as scaffolds for multiple proteins necessary for the assembly of ribonucleoprotein complexes, which can modulate the marks on histone tails.
“It is currently recognized that lncRNAs can serve as precursors of siRNAs; they can serve to modulate gene expression through the recruitment of chromatin-modifying enzymes, and they also serve as scaffolds for multiple proteins necessary for the assembly of ribonucleoprotein complexes, which can modulate the marks on histone tails [53,61].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [53]
The RNA interference section says some lncRNAs regulate gene expression, while the Histone modification section describes changes to histone tails. Neither passage says lncRNAs scaffold proteins into ribonucleoprotein complexes or links those complexes to histone-tail marks. Searches for those mechanisms found no supporting passage in our copy; page numbers were not provided. (LLM, conf=0.73)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
MicroRNAs: Target Recognition and Regulatory Functions
Bartel, D. P. (2009) · Cell · doi:10.1016/j.cell.2009.01.002 · Cited by 20,068 · see citing works
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855 · see citing works
Chromatin modifications and their function
Kouzarides, T. (2007) · Cell · doi:10.1016/j.cell.2007.02.005 · Cited by 10,883 · see citing works
Jenuwein, T., & Allis, C. D. (2001) · Science · doi:10.1126/science.1063127 · Cited by 9,756 · see citing works
Crystal structure of the nucleosome core particle at 2.8 Å resolution
Luger, K., Mäder, A. W., Richmond, R. K., Sargent, D. F., & Richmond, T. J. (1997) · Nature · doi:10.1038/38444 · Cited by 9,466 · see citing works
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327 · see citing works
The language of covalent histone modifications
Strahl, B. D., & Allis, C. D. (2000) · Nature · doi:10.1038/47412 · Cited by 8,679 · see citing works
Functions of DNA methylation: islands, start sites, gene bodies and beyond
Jones, P. A. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3230 · Cited by 6,061 · see citing works
Regulation of chromatin by histone modifications
Bannister, A. J., & Kouzarides, T. (2011) · Cell Research · doi:10.1038/cr.2011.22 · Cited by 6,012 · see citing works
The nuclear RNase III Drosha initiates microRNA processing
Lee, Y., Ahn, C., Han, J., Choi, H., Kim, J., Yim, J., Lee, J., Provost, P., Rådmark, O., Kim, S., et al. (2003) · Nature · doi:10.1038/nature01957 · Cited by 5,094 · see citing works
Self-citationEvolution and Functions of Long Noncoding RNAs
Ponting, C. P., Oliver, P. L., & Reik, W. (2009) · Cell · doi:10.1016/j.cell.2009.02.006 · Cited by 4,917 · see citing works
Human DNA methylomes at base resolution show widespread epigenomic differences
Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., et al. (2009) · Nature · doi:10.1038/nature08514 · Cited by 4,673 · see citing works
The widespread regulation of microRNA biogenesis, function and decay
Krol, J., Loedige, I., & Filipowicz, W. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2843 · Cited by 4,672 · see citing works
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398 · see citing works
Molecular Mechanisms of Long Noncoding RNAs
Wang, K., & Chang, H. Y. (2011) · Molecular Cell · doi:10.1016/j.molcel.2011.08.018 · Cited by 4,390 · see citing works
Wightman, B., Ha, I., & Ruvkun, G. (1993) · Cell · doi:10.1016/0092-8674(93)90530-4 · Cited by 4,292 · see citing works
Epigenetics in human disease and prospects for epigenetic therapy
Egger, G., Liang, G., Aparicio, A., & Jones, P. A. (2004) · Nature · doi:10.1038/nature02625 · Cited by 3,232 · see citing works
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771 · see citing works
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome biology · doi:10.1186/gb-2003-4-9-r60 · Cited by 2,383 · see citing works
Small silencing RNAs: an expanding universe
Ghildiyal, M., & Zamore, P. D. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2504 · Cited by 2,314 · see citing works
Long noncoding RNAs: functional surprises from the RNA worldPreprint
Wilusz, J. E., Sunwoo, H., & Spector, D. L. (2009) · Genes & Development · doi:10.1101/gad.1800909 · Cited by 2,298 · see citing works
Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning
Cokus, S. J., Feng, S.-H., Zhang, X.-Y., Chen, Z., Merriman, B., Haudenschild, C., Pradhan, S., Nelson, S., Pellegrini, M., & Jacobsen, S. (2008) · Nature · doi:10.1038/nature06745 · Cited by 2,278 · see citing works
High density synthetic oligonucleotide arrays
Lipshutz, R. J., Fodor, S. P. A., Gingeras, T., & Lockhart, D. J. (1999) · Nature Genetics · doi:10.1038/4447 · Cited by 2,204 · see citing works
Active Genes Are Tri-Methylated at K4 of Histone H3
Santos-Rosa, H., Schneider, R., Bannister, A. J., Sherriff, J., Bernstein, B. E., Emre, N. C. T., Schreiber, S. L., Mellor, J., & Kouzarides, T. (2002) · Nature · doi:10.1038/nature01080 · Cited by 2,138 · see citing works
A microRNA in a Multiple-Turnover RNAi Enzyme Complex
Hutvágner, G., & Zamore, P. D. (2002) · Science · doi:10.1126/science.1073827 · Cited by 2,081 · see citing works
Chromatin modification and epigenetic reprogramming in mammalian development
Li, E. (2002) · Nature Reviews Genetics · doi:10.1038/nrg887 · Cited by 1,900 · see citing works
Weber, M., Davies, J. J., Wittig, D., Oakeley, E., Haase, M., Lam, W., & Schübeler, D. (2005) · Nature Genetics · doi:10.1038/ng1598 · Cited by 1,885 · see citing works
Dent, S., Denu, J. M., & Allis, C. D. (2001) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.70.1.81 · Cited by 1,844 · see citing works
Protein Arginine Methylation in Mammals: Who, What, and Why
Bedford, M. T., & Clarke, S. G. (2009) · Molecular Cell · doi:10.1016/j.molcel.2008.12.013 · Cited by 1,810 · see citing works
Acetylation of Histones and Transcription-Related Factors
Sterner, D. E., & Berger, S. L. (2000) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.64.2.435-459.2000 · Cited by 1,766 · see citing works
Conserved role of intragenic DNA methylation in regulating alternative promoters
Maunakea, A. K., Nagarajan, R. P., Bilenky, M., Ballinger, T. J., D’Souza, C., Fouse, S. D., Johnson, B. E., Hong, C., Nielsen, C., Zhao, Y., et al. (2010) · Nature · doi:10.1038/nature09165 · Cited by 1,743 · see citing works
Missing heritability and strategies for finding the underlying causes of complex disease
Eichler, E. E., Flint, J., Gibson, G. C., Kong, A., Leal, S. M., Moore, J. H., & Nadeau, J. H. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2809 · Cited by 1,671 · see citing works
Saxonov, S., Berg, P., & Brutlag, D. L. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0510310103 · Cited by 1,401 · see citing works
Epigenome-wide association studies for common human diseases
Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3000 · Cited by 1,272 · see citing works
Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis
Meissner, A. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki901 · Cited by 1,203 · see citing works
Single-cell sequencing-based technologies will revolutionize whole-organism science
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3542 · Cited by 1,177 · see citing works
microRNA target predictions in animals
Rajewsky, N. (2006) · Nature Genetics · doi:10.1038/ng1798 · Cited by 1,086 · see citing works
Dynamic changes in the human methylome during differentiationPreprint
Laurent, L. C., Wong, E., Li, G., Huynh, T., Tsirigos, A., Ong, C. T., Low, H. M., Sung, K. W. K., Rigoutsos, I., Loring, J. F., et al. (2010) · Genome Research · doi:10.1101/gr.101907.109 · Cited by 1,037 · see citing works
Molecular Signals of Epigenetic States
Bonasio, R., Tu, S., & Reinberg, D. (2010) · Science · doi:10.1126/science.1191078 · Cited by 912 · see citing works
miRNPs: a novel class of ribonucleoproteins containing numerous microRNAs
Mourelatos, Z., Dostie, J., Paushkin, S., Sharma, A., Charroux, B., Abel, L., Rappsilber, J., Mann, M., & Dreyfuss, G. (2002) · Genes & Development · doi:10.1101/gad.974702 · Cited by 886 · see citing works
Reactome pathway analysis: a high-performance in-memory approach
Fabregat, A., Sidiropoulos, K., Viteri, G., Forner, O., Marín-García, P., Arnau, V., D’Eustachio, P., Stein, L. D., & Hermjakob, H. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1559-2 · Cited by 859 · see citing works
DNA Methylation: Superior or Subordinate in the Epigenetic Hierarchy?
Jin, B., Li, Y., & Robertson, K. D. (2011) · Genes & Cancer · doi:10.1177/1947601910393957 · Cited by 818 · see citing works
Transitions in Distinct Histone H3 Methylation Patterns at the Heterochromatin Domain Boundaries
Noma, K.-I., Allis, C. D., & Grewal, S. I. S. (2001) · Science · doi:10.1126/science.1064150 · Cited by 761 · see citing works
Gu, H., Smith, Z. D., Bock, C., Boyle, P., Gnirke, A., & Meissner, A. (2011) · Nature Protocols · doi:10.1038/nprot.2010.190 · Cited by 750 · see citing works
Harris, R. A., Wang, T., Coarfa, C., Nagarajan, R. P., Hong, C., Downey, S. L., Johnson, B., Fouse, S. D., Delaney, A. D., Zhao, Y., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1682 · Cited by 713 · see citing works
Quantitative comparison of genome-wide DNA methylation mapping technologies
Bock, C., Tomazou, E. M., Brinkman, A. B., Müller, F., Simmer, F., Gu, H., Jäger, N., Gnirke, A., Stunnenberg, H. G., & Meissner, A. (2010) · Nature Biotechnology · doi:10.1038/nbt.1681 · Cited by 587 · see citing works
Non-coding RNAs as direct and indirect modulators of epigenetic regulation
Peschansky, V. J., & Wahlestedt, C. (2013) · Epigenetics · doi:10.4161/epi.27473 · Cited by 516 · see citing works
Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing
Hafner, M., Landgraf, P., Ludwig, J., Rice, A., Ojo, T., Lin, C. P., Holoch, D., Lim, C., & Tuschl, T. (2007) · Methods · doi:10.1016/j.ymeth.2007.09.009 · Cited by 480 · see citing works
Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering
Gasch, A. P., & Eisen, M. B. (2002) · Genome biology · doi:10.1186/gb-2002-3-11-research0059 · Cited by 475 · see citing works
Integrating the MAP kinase signal into the G1 phase cell cycle machinery
Roovers, K., & Assoian, R. K. (2000) · BioEssays · doi:10.1002/1521-1878(200009)22:9 · Cited by 469 · see citing works
NONCODING RNAS IN THE MAMMALIAN CENTRAL NERVOUS SYSTEM
Cao, X., Yeo, G. W.-M., Muotri, A. R., Kuwabara, T., & Gage, F. H. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112839 · Cited by 432 · see citing works
Basic Concepts of Microarrays and Potential Applications in Clinical Microbiology
Miller, M. B., & Tang, Y. (2009) · Clinical Microbiology Reviews · doi:10.1128/cmr.00019-09 · Cited by 418 · see citing works
Serre, D., Lee, B. H., & Ting, A. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp992 · Cited by 404 · see citing works
Tissue type is a major modifier of the 5-hydroxymethylcytosine content of human genesPreprint
Nestor, C. E., Ottaviano, R., Reddington, J. P., Sproul, D., Reinhardt, D., Dunican, D. S., Katz, E., Dixon, J. M., Harrison, D. J., & Meehan, R. R. (2011) · Genome Research · doi:10.1101/gr.126417.111 · Cited by 403 · see citing works
Comparative isoschizomer profiling of cytosine methylation: The HELP assayPreprint
Khulan, B., Thompson, R. F., Ye, K. Q., Fazzari, M. J., Suzuki, M., Stasiek, E., Figueroa, M. E., Glass, J. L., Chen, Q., Montagna, C., et al. (2006) · Genome Research · doi:10.1101/gr.5273806 · Cited by 396 · see citing works
Dimension reduction techniques for the integrative analysis of multi-omics data
Meng, C., Zeleznik, O. A., Thallinger, G., Küster, B., Gholami, A. M., & Culhane, A. C. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbv108 · Cited by 383 · see citing works
Genomic mapping of 5-hydroxymethylcytosine in the human brain
Jin, S.-G., Wu, X., Li, A. X., & Pfeifer, G. P. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr120 · Cited by 375 · see citing works
Profiling genome-wide DNA methylation
Yong, W.-S., Hsu, F.-M., & Chen, P.-Y. (2016) · Epigenetics & Chromatin · doi:10.1186/s13072-016-0075-3 · Cited by 374 · see citing works
Ruvkun, G. (2001) · Science · doi:10.1126/science.1066315 · Cited by 357 · see citing works
Reconstructing the population genetic history of the Caribbean
Moreno-Estrada, A., Gravel, S., Zakharia, F., McCauley, J. L., Byrnes, J. K., Gignoux, C. R., Ortiz-Tello, P. A., Martínez, R. J., Hedges, D. J., Morris, R. W., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003925 · Cited by 327 · see citing works
Non-coding RNAs, epigenetics and complexity
Costa, F. F. (2007) · Gene · doi:10.1016/j.gene.2007.12.008 · Cited by 319 · see citing works
A multivariate approach to the integration of multi-omics datasets
Meng, C., Küster, B., Culhane, A. C., & Gholami, A. M. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-162 · Cited by 319 · see citing works
DNA methylation and silencing of gene expression.
Newell-Price, J., Clark, A., & King, P. (2000) · Trends in endocrinology and metabolism · doi:10.1016/s1043-2760(00)00248-4 · Cited by 316 · see citing works
Next-generation tag sequencing for cancer gene expression profilingPreprint
Morrissy, A. S., Morin, R. D., Delaney, A. D., Zeng, T., McDonald, H., Jones, S. J. M., Zhao, Y., Hirst, M., & Marra, M. A. (2009) · Genome Research · doi:10.1101/gr.094482.109 · Cited by 309 · see citing works
Advantages and limitations of microarray technology in human cancer
Russo, G. M., Zegar, C., & Giordano, A. (2003) · Oncogene · doi:10.1038/sj.onc.1206865 · Cited by 289 · see citing works
Methylated DNA Immunoprecipitation (MeDIP)
Mohn, F., Weber, M., Schübeler, D., & Roloff, T. C. (2008) · Methods in molecular biology · doi:10.1007/978-1-59745-522-0_5 · Cited by 239 · see citing works
Dynamic protein methylation in chromatin biology
Ng, S. S., Yue, W. W., Oppermann, U. C. T., & Klose, R. J. (2008) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-008-8303-z · Cited by 226 · see citing works
Nair, S. S., Coolen, M. W., Stirzaker, C., Song, J. Z., Statham, A. L., Strbenac, D., Robinson, M. D., & Clark, S. J. (2011) · Epigenetics · doi:10.4161/epi.6.1.13313 · Cited by 210 · see citing works
Kundu, T. K., Palhan, V. B., Wang, Z., An, W., Cole, P. A., & Roeder, R. G. (2000) · Molecular Cell · doi:10.1016/s1097-2765(00)00054-x · Cited by 201 · see citing works
Levin, J. Z., Berger, M. F., Adiconis, X., Rogov, P., Melnikov, A., Fennell, T., Nusbaum, C., Garraway, L. A., & Gnirke, A. (2009) · Genome biology · doi:10.1186/gb-2009-10-10-r115 · Cited by 193 · see citing works
Xu, W., Xu, M., Wang, L., Zhou, W., Xiang, R., Shi, Y., Zhang, Y., & Piao, Y. (2019) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-019-0081-6 · Cited by 162 · see citing works
Krishnan, K. C., Kurt, Z., Barrere‐Cain, R., Sabir, S., Das, A., Floyd, R. R., Vergnes, L., Zhao, Y., Che, N., Charugundla, S., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.006 · Cited by 149 · see citing works
Epigenetic regulation: methylation of histone and non-histone proteins
Lan, F., & Shi, Y. (2009) · Science in China Series C Life Sciences · doi:10.1007/s11427-009-0054-z · Cited by 120 · see citing works
Integration of multi-omics data for prediction of phenotypic traits using random forest
Acharjee, A., Kloosterman, B., Visser, R. G. F., & Maliepaard, C. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1043-4 · Cited by 109 · see citing works
Non-coding RNAs: Epigenetic regulators of bone development and homeostasis
Hassan, M. Q., Tye, C. E., Stein, G. S., & Lian, J. B. (2015) · Bone · doi:10.1016/j.bone.2015.05.026 · Cited by 102 · see citing works
Sadakierska−Chudy, A., Kostrzewa, R. M., & Filip, M. (2014) · Neurotoxicity Research · doi:10.1007/s12640-014-9497-5 · Cited by 100 · see citing works
Optimized design and data analysis of tag-based cytosine methylation assays
Suzuki, M., Jing, Q., Lia, D., Pascual, M., McLellan, A. S., & Greally, J. M. (2010) · Genome biology · doi:10.1186/gb-2010-11-4-r36 · Cited by 89 · see citing works
Grasping Trimethylation of Histone H3 at Lysine 4
Vermeulen, M., & Timmers, H. T. M. (2010) · Epigenomics · doi:10.2217/epi.10.11 · Cited by 79 · see citing works
Histone H3 Lysine 4 di-methylation: A novel mark for transcriptional fidelity?
Pinskaya, M., & Morillon, A. (2009) · Epigenetics · doi:10.4161/epi.4.5.9369 · Cited by 74 · see citing works
Histone Deacetylase Inhibitors: The Epigenetic Therapeutics That Repress Hypoxia‐Inducible Factors
Chen, S., & Sang, N. (2010) · BioMed Research International · doi:10.1155/2011/197946 · Cited by 73 · see citing works
Histone acetylation and methylation: combinatorial players for transcriptional regulation.
An, W. (2007) · PubMed · Cited by 72 · see citing works
Zhang, M., Yan, F., Li, F., Jiang, K., Li, D., Han, R., Li, Z.-J., Jiang, R., Liu, X., Kang, X., et al. (2017) · Scientific Reports · doi:10.1038/srep45564 · Cited by 69 · see citing works
DNA unmethylome profiling by covalent capture of CpG sites
Kriukienė, E., Labrie, V., Khare, T., Urbanavičiūtė, G., Lapinaitė, A., Koncevičius, K., Li, D., Wang, T., Pai, S., Ptak, C., et al. (2013) · Nature Communications · doi:10.1038/ncomms3190 · Cited by 62 · see citing works
Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules
Silverbush, D., Cristea, S., Arad, G., Geiger, T., Beerenwinkel, N., & Sharan, R. (2019) · Cell Systems · doi:10.1016/j.cels.2019.04.005 · Cited by 56 · see citing works
Guo, K., Eid, S., Elzinga, S. E., Pacut, C. M., Feldman, E. L., & Hur, J. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00913-6 · Cited by 56 · see citing works
Comparative transcriptomic and metabolomic analysis of fenofibrate and fish oil treatments in mice
Lu, Y., Boekschoten, M. V., Wopereis, S., Müller, M. M., & Kersten, S. (2011) · Physiological Genomics · doi:10.1152/physiolgenomics.00100.2011 · Cited by 51 · see citing works
Zhang, W., Tang, G., Zhou, S., & Niu, Y. (2019) · BMC Genomics · doi:10.1186/s12864-019-6284-y · Cited by 49 · see citing works
Transcriptomic Analysis of mRNA-lncRNA-miRNA Interactions in Hepatocellular Carcinoma
Tang, X., Feng, D., Li, M., Zhou, J., Li, X., Zhao, D., Hao, B., Li, D., & Ding, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-52559-x · Cited by 43 · see citing works
Wilson, S. P., & Filipp, F. V. (2018) · npj Systems Biology and Applications · doi:10.1038/s41540-018-0061-4 · Cited by 40 · see citing works
Kim, J., Park, J., Yoo, I., Han, J., Kim, N., Lim, W.-J., Cho, E.-S., Choi, B., Choi, S.-H., Kim, T.-H., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-23655-1 · Cited by 40 · see citing works
Self-citationHu, Q., Ao, Q., Tan, Y., Xi, G., Luo, Y., & Zhu, J. (2020) · The Journal of Immunology · doi:10.4049/jimmunol.1901496 · Cited by 37 · see citing works
Lim, B., Kim, S.-W., Lim, K.-S., Jeong, C., Kim, S.-C., Lee, S.-M., Park, C.-K., PAS, M. F. W. T., Gho, H., Kim, T. H., et al. (2020) · Veterinary Research · doi:10.1186/s13567-020-00850-5 · Cited by 32 · see citing works
Self-citationDNA Methylation and Complex Human Disease
(2016) · Elsevier eBooks · doi:10.1016/c2013-0-13028-0 · Cited by 31 · see citing works
Accessing epigenetic variation in the plant methylome
Kim, K. D., Baidouri, M. E., & Jackson, S. (2014) · Briefings in Functional Genomics · doi:10.1093/bfgp/elu003 · Cited by 28 · see citing works
DNA methylation profiling using HpaII tiny fragment enrichment by ligation-mediated PCR (HELP)
Suzuki, M., & Greally, J. M. (2010) · Methods · doi:10.1016/j.ymeth.2010.04.013 · Cited by 27 · see citing works
Integrating Multi-Omics for Uncovering the Architecture of Cross-Talking Pathways in Breast Cancer
Wang, L., Xiao, Y., Ping, Y., Li, J., Zhao, H., Li, F., Hu, J. R., Zhang, H., Deng, Y., Tian, J., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104282 · Cited by 25 · see citing works
Shen, J., & Zhu, B. (2018) · Molecular Medicine Reports · doi:10.3892/mmr.2018.8804 · Cited by 24 · see citing works
Presence of 5-methylcytosine in CpNpG trinucleotides in the human genome
Lee, J., Jang, S. J., Benoit, N. E., Hoque, M. O., Califano, J. A., Trink, B., Sidransky, D., Mao, L., & Moon, C. (2010) · Genomics · doi:10.1016/j.ygeno.2010.03.013 · Cited by 21 · see citing works
RNA Sequencing in Schizophrenia
Li, X., & Teng, S. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28992 · Cited by 18 · see citing works
Histone Modifications, Stem Cells and Prostate Cancer
Crea, F., Clermont, P., Mai, A., & Helgason, C. D. (2014) · Current Pharmaceutical Design · doi:10.2174/13816128113199990522 · Cited by 16 · see citing works
Histone Acetylation And Methylation
An, W. (2007) · Springer eBooks · doi:10.1007/1-4020-5466-1_16 · Cited by 15 · see citing works
Assessment of weighted topological overlap (wTO) to improve fidelity of gene co-expression networks
Voigt, A., & Almaas, E. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2596-9 · Cited by 11 · see citing works
Stein, G. S., van Wijnen, A. J., Stein, J. L., Lian, J. B., McNeil, S. M., & Pockwinse, S. M. (1999) · Journal of Cellular Biochemistry · doi:10.1002/(sici)1097-4644(1999)75:32+ · Cited by 9 · see citing works
The HELP-Based DNA Methylation Assays
Greally, J. M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7481-8_11 · Cited by 7 · see citing works
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Shi, C., Cheng, L., Yu, Y., Chen, S., Dai, Y., Yang, J., Zhang, H., Chen, J., & Geng, N. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.124675 · Cited by 82 · see citing works
Piferrer, F., & Wang, H. (2023) · Wiley eBooks · doi:10.1002/9781119821946 · Cited by 50 · see citing works
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Naya-Català, F., Belenguer, Á., Montero, D., Torrecillas, S., Soriano, B., Calduch-Giner, J. À., Lloréns, C., Fontanillas, R., Sarih, S., Zamorano, M. J., et al. (2023) · BMC Genomics · doi:10.1186/s12864-023-09759-7 · Cited by 18 · see citing works
Revisiting Epigenetics Fundamentals and Its Biomedical Implications
Meza-Menchaca, T., Albores-Medina, A., Heredia-Mendez, A. J., Ruíz-May, E., Ricaño-Rodríguez, J., Gallegos-García, V., Esquivel, A., Vettoretti-Maldonado, G., & Campos-Parra, A. D. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25147927 · Cited by 15 · see citing works
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Holtby, A. R., Hall, T. J., McGivney, B. A., Han, H., Murphy, K. J., MacHugh, D. E., Katz, L. M., & Hill, E. W. (2023) · Animal Genetics · doi:10.1111/age.13320 · Cited by 13 · see citing works
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Wolde, T., Huang, J. H., Huang, P., Pandey, V., & Qin, P. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4010019 · Cited by 11 · see citing works
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Sun, Y., Sun, Y., He, X., Li, S., Xu, X., Feng, Y., Yang, J., Xie, R., & Sun, G. (2024) · BMC Genomics · doi:10.1186/s12864-024-10972-1 · Cited by 11 · see citing works
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Additional file 1 of Genomic data integration tutorial, a plant case study
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Additional file 2 of Genomic data integration tutorial, a plant case study
Mardoc, E., Sow, M. D., Dejean, S., & Salse, J. (2024) · Figshare · doi:10.6084/m9.figshare.25017915
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Naya-Català, F., Belenguer, A., Montero, D., Torrecillas, S., Soriano, B., Calduch-Giner, J. À., Lloréns, C., Fontanillas, R., Sarih, S., Zamorano, M. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26636871
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- Authors
- Kim, D.-Y., & Kim, J.-M.
- Affiliations
- Chung-Ang University (KR)
- Corresponding author
- Kim, Jun-Mo
- Venue
- Animal Bioscience
- Published
- 2021-08-01
- Volume
- 34
- Issue
- 8
- Pages
- 1271-1282
- ISSN
- 2765-0189 (ISSN-L)
- 2765-0235
- PMC
- PMC8255897
- OpenAlex
- W3157995343
- Cited by
- 47
- References
- 107
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 16 · 2023: 8 · 2022: 2 · 2021: 2
- FWCI
- 0.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Animal Bioscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (2018R1A6A1A03025159)
- Ministry of Education
- Chung-Ang University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- multi-omics integration, epigenetic mechanisms, gene-environment interaction, heritable traits, gene expression regulation, next-generation sequencing data analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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