The addition of a methyl group to the fifth carbon of cytidine is the most prevalent and stable epigenetic modification of human DNA (Laurent et al., 2010). DNA methylation is involved in gene regulation and influences a vast array of biological mechanisms, including embryonic development and cell fate, genome imprinting, X-chromosome inactivation, and transposon silencing (Moore et al., 2013; Smith and Meissner, 2013). In mammals, there are two copies or alleles of a gene, one inherited from each parent. Most gene transcripts are expressed from both alleles. However, a subset of genes are only expressed from a single allele; this allele can be selected either randomly, as seen in X-chromosome inactivation in females, or based upon the parent-of-origin (PofO), referred to as imprinting (Chess, 2013; Khamlichi and Feil, 2018).

In imprinting, mono-allelic expression of a gene or cluster of genes is controlled by a cis-acting imprinting control region (ICR) (Bartolomei and Ferguson-Smith, 2011). The main mechanism by which this occurs is PofO-defined differential methylation at ICRs, also known as imprinted differentially methylated regions (DMRs) (Bartolomei and Ferguson-Smith, 2011; Maupetit-Méhouas et al., 2016). Imprinted DMRs are classified as germline (primary) or somatic (secondary), hereinafter referred to as gDMR and sDMR. gDMRs are established during the first wave of methylation reprogramming in germ cell development and escape the second methylation reprogramming after fertilization (Zink et al., 2018). sDMRs are established de novo after fertilization during somatic development, usually under the control of a nearby gDMR (Zink et al., 2018). Imprinted clusters of genes may span up to ~4 Mb, by acting through a CCCTC-binding factor (CTCF)-binding site or by allelic expression of a long non-coding RNA (Bartolomei and Ferguson-Smith, 2011; da Rocha and Gendrel, 2019). By contrast, individually imprinted genes are typically regulated by PofO-derived differential methylation at the gene promoter (Bartolomei and Ferguson-Smith, 2011).

Imprinting is implicated in various genetic disorders, either from aberrations in imprinted methylation or from deleterious variants affecting the ICR and imprinted genes. Aberrant imprinted methylation is also detected in several human cancers (Goovaerts et al., 2018; Jelinic and Shaw, 2007; Tomizawa and Sasaki, 2012). Thus, accurate mapping and characterization of imprinting in humans is key to the treatment and actionability of genetic disorders, and to personalized oncogenomonics.

To detect imprinted methylation, accurate assignment of methylation data to paternal and maternal alleles is required. Achieving this with traditional bisulfite sequencing or arrays is challenging. Several studies have used samples with large karyotypic abnormalities, such as uniparental disomies, teratomas, and hydatidiform moles, to infer regions of imprinting (Court et al., 2014; Hernandez Mora et al., 2018; Joshi et al., 2016). This approach relies not only on rare structural variants, but also on the assumption that both normal methylation and the imprinted state remain intact in spite of substantial genomic aberrations. A study by Zink et al., 2018, leveraged a genotyped, multi-generation pedigree spanning nearly half the population of Iceland (n=150,000), in combination with whole-genome oxidative bisulfite sequencing, to phase methylation and infer PofO (Zink et al., 2018). However, despite being able to phase nearly every SNP in that cohort, they were only able to phase 84% of the human autosomal methylome in over 200 samples due to the short length of reads. Furthermore, the study was based on a single, genetically isolated population, which may not be representative of the wider human population. A comprehensive mapping of imprinted methylation using a technology more suited to phasing reads, based on individuals more representative of the human population, could greatly advance our understanding of imprinting, with direct benefits for human health.

Previously, we have shown that nanopore sequencing can detect allelic methylation in a single sample and accurately determine PofO using only trio data. We also previously developed the software NanoMethPhase for this purpose (Akbari et al., 2021). Here, we applied NanoMethPhase to public nanopore data from a diverse set of 12 lymphoblastoid cell lines (LCLs) from the 1000 Genomes Project (1KGP) and Genome in a Bottle (GIAB) to investigate genome-wide allele-specific methylation (ASM) and detect novel imprinted DMRs (Figure 1A; Auton et al., 2015; De Coster et al., 2019; Jain et al., 2018; Shafin et al., 2020; Zook et al., 2016; Zook et al., 2019). Using trio data from GIAB and 1KGP for these cell lines, we phased nanopore long reads to their PofO and inferred allelic methylation (Akbari et al., 2021; Auton et al., 2015; Zook et al., 2019). We were able to detect haplotype and methylation status for 26.5 million autosomal CpGs comprising 95% of the human autosomal methylome (GRCh38 main chromosomes). We further used public whole-genome bisulfite sequencing (WGBS) data to confirm the presence of the detected DMRs in other tissues and to classify the novel DMRs as germline or somatic. We captured 94% of the well-characterized DMRs and detected 42 novel DMRs (16 gDMRs and 26 sDMRs). Of these novel DMRs, 40.5% show evidence of conservation. We also detected seven blocks of PofO methylation bias at seven imprinted clusters with mutual exclusive allelic H3K36me3 and H3K27me3 histone marks. Collectively, our results extend the set of known imprinted intervals in humans and demonstrate a major contribution in our ability to characterize imprinting by ASM, brought about by the capabilities of long-read nanopore sequencing.

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Here, we describe the first genome-wide map of human ASM intended to detect novel imprinted intervals using nanopore sequencing. Leveraging long reads and parental SNVs allowed us to phase methylation for ~26.5 million autosomal CpGs representing 95% of the CpGs in the human autosomal genome (GRCh38) across 12 LCLs. This effort achieves a much higher resolution than previous studies aimed at capturing allelic methylation using bisulfite sequencing or methylation arrays (Court et al., 2014; Hernandez Mora et al., 2018; Joshi et al., 2016; Zink et al., 2018). Fourteen of our novel DMRs did not have any phased CpG from previous WGBS or array studies (Supplementary file 4), illustrating the utility of longer reads for imprinted methylation calling. DMRs that are detected in only a single study displayed higher variations across individuals compared to those detected by at least two studies. Therefore, lack of phasing at some novel DMRs in previous studies and higher variation in imprinted methylation at novel DMRs could explain the reason they were not detected previously. We also demonstrated that germline DMRs with a greater number of ZFP57 motif tend to be more consistently imprinted across individuals suggesting motifs redundancy increases ZFP57 recruitment and tolerance to any DNA sequence variation. However, due to the availability of DNA sequence in a limited number of samples, we were not able to examine sequence variation at the DMRs and the ZFP57-binding motifs for any possible association with polymorphic imprinted methylation which will require further study.

Even though we detected methylation for all the CpGs in the human genome (GRCh38), we were not able to phase 5% of the human methylome (Kent et al., 2002). We used SNVs detected from short-reads data in the 1KGP and GIAB databases for phasing (Auton et al., 2015; Zook et al., 2019). Seventy-five percent of the unphased CpGs mapped to the ENCODE blacklist, regions with low mappability, indicative of lack of SNVs to phase reads (Amemiya et al., 2019). Improvement in base calling and variant calling from nanopore reads could enable the phasing of a complete genome-wide methylome using nanopore-detected SNVs.

We detected 16 novel gDMRs and 26 novel sDMRs. These novel DMRs were supported by several lines of evidence in our analyses. (1) They displayed significant PofO methylation bias in nanopore-sequenced cell line samples. (2) They were partially methylated in WGBS data. (3) gDMRs demonstrated establishment of methylation in sperm or oocyte and escape from the second de-methylation step. (4) Eighty-four percent of those for which H3K4me3 ChIP-seq data could be phased and examined showed significant allelic H3K4me3. (5) Forty percent showed evidence of conservation. (6) Eighty-three percent mapped to at least one regulatory region including CGI, CTCF-binding site, and enhancer (Supplementary file 4). These novel DMRs represent a substantial and well-confirmed expansion of known regions of imprinting, which may aid future research and diagnosis in the fields of genetic medicine and oncology.

We detected seven blocks of allelic methylation bias (Figure 8; Figure 8—figure supplements 1–6). All of the blocks represented several common features. (1) They were detected in imprinted genes that appeared in a cluster. (2) There was at least one well-characterized and conserved gDMR in each block (except ZNF597/NAA60 block with a conserved sDMR). (3) The well-characterized DMRs in these blocks displayed significant allelic H3K4me3 (except the DMR in the L3MBTL1 block, which could not be examined due to the lack of an SNV). (4) The well-characterized DMRs in these blocks overlapped with the promoters of genes with subtle PofO methylation bias at the gene body and DMR itself displayed opposite PofO methylation (except for GPR1-AS/ZDBF2 block where DMR did not map to the promoter and had the same PofO with the gene body). (5) All the blocks were accompanied by a strong allelic expression and H3K36me3 histone mark on the subtle hypermethylated allele and H3K27me3 on the hypomethylated allele. This represents a novel facet of imprinting biology and suggests a link between allelic expression and histone modifications with biased PofO methylation at these blocks. However, the mechanism regulating such blocks and the rule of these PofO-biased methylation remain to be determined. One possible explanation could be that the subtle parental methylation bias is used by cells to express important genes (genes that can regulate other genes in the cluster or have regulatory roles) in an imprinted cluster with higher fidelity through its gene body methylation on the active allele. For example, at the KCNQ1OT1 and GNAS/GNAS-AS1 clusters, the methylation blocks overlap with KCNQ1OT1 and GNAS-AS1 gene bodies, both of which encode antisense RNA transcripts that regulate other genes in the imprinted cluster (Chiesa et al., 2012; Turan and Bastepe, 2013).

Orthologous regions of ~40% of the detected DMRs demonstrated partial methylation in one or more of the three mammals including chimpanzee, rhesus macaque, and mouse, suggesting their conservation. There were a considerably higher number of orthologous sites and partially methylated orthologous DMRs in chimpanzee and rhesus macaque, in agreement with more similarities and less distance to these primates compared to the mouse in human evolution. Previously, Court et al., 2014, detected 14 novel DMRs, and did not detect any imprinted orthologs of their novel DMRs in mice (Court et al., 2014). All 14 DMRs also overlapped with our detected DMRs and six of them had orthologous regions in mm10 using the UCSC liftover file (Kent et al., 2002). Two of the orthologs displayed partial methylation in mouse; the first is MEG8 human DMR with its orthologous Rian gene in the mouse, which was not examined by Court et al., 2014, and the other is found in the Htr5a gene, which was previously reported as not conserved in mouse (Court et al., 2014). After reviewing their analysis, Court et al., 2014, seem to have examined different orthologous region (Appendix 2—figure 1). For Htr5a, they examined the CGI (CpG:_102) ~50 kb away from the gene, while we examined the region spanning the first or second exon (two transcripts) of Htr5a which was partially methylated while CpG:_102 was also unmethylated in our study.

Using reported imprinted genes, 36% of the novel DMRs mapped close to known imprinted genes (Babak et al., 2015; Baran et al., 2015; Geneimprint, 2021; Jadhav et al., 2019; Morison et al., 2001; Zink et al., 2018). Five of our novel DMRs could be potential ICRs for reported imprinted genes. Specifically, imprinted DMRs overlapping the promoters of ZNF714, PAX8-AS1, ACTL10, and SYCE1 genes (Figures 6 and 7). ZNF714 is a member of the zinc finger family of proteins which have several imprinted genes with developmental roles (Babak et al., 2015; Baran et al., 2015; Camargo et al., 2012; Jadhav et al., 2019; Zink et al., 2018). ZNF714 has been reported to be associated with non-syndromic cleft lip (Camargo et al., 2012). Aberrant methylation of multiple CpGs overlapping with the novel DMR at PAX8-AS1 has been implicated in thyroid disorders (Candler et al., 2021). SYCE1 and ACTL10 are also implicated in human diseases (Bak et al., 2016; Maor-Sagie et al., 2015). Thus, these imprinted DMRs could be of potential clinical value.

In addition to the aforementioned novel DMRs, two of the reported DMRs in PTCHD3 and FANCC are also interesting. Paternal expression of PTCHD3 and maternal expression for FANCC were previously detected by Zink et al., 2018, though they could not detect any associated DMR due to the lack of phased CpG (Zink et al., 2018). Hernandez Mora et al., 2018, detected three maternally methylated probes at the promoter of PTCHD3 and one maternally methylated probe in intron 1 of FANCC, but were unable to examine the parental expression (Hernandez Mora et al., 2018). We also detected two maternally methylated gDMRs overlapping with the promoter of PCTHD3 and intron 1 of FANCC (Appendix 3—figure 1; Appendix 3—figure 2). Therefore, these gDMRs could be the ICRs for these genes. The maternal gDMR at the PTCHD3 promoter can directly suppress the maternal allele and results in paternal expression. FANCC gDMR overlaps with a CGI and CTCF-binding site. CTCF is a methylation-sensitive DNA-binding protein and CpG methylation can inhibit CTCF binding (Hashimoto et al., 2017; Renda et al., 2007). Moreover, CTCF binding to the first intron of major immediate early (MIE) gene of the human cytomegalovirus (HCMV) in HCMV-infected cells resulted in repression of this gene (Puerta et al., 2014). Therefore, the maternally methylated DMR in intron 1 of maternally expressed FANCC suggests a mechanism through which the paternal allele is suppressed by CTCF binding at DMR while DNA methylation inhibits CTCF binding at the maternal allele.

Overall, our study demonstrates a near-complete genome-wide map of human ASM by leveraging long-read nanopore technology. The use of nanopore technology allowed us to expand the set of known imprinted DMRs using 12 LCLs with parental SNPs. Moreover, we detected seven large PofO bias methylation blocks with enriched allelic expression and histone modifications. We showed that nanopore sequencing has the ability to achieve a higher resolution of phased CpGs using a small sample size and allows for the calling of imprinted methylation in a single sample, potentially reducing the cost by reducing the sample size.

The current manuscript is a computational study, so no new datasets have been generated for this manuscript. The source of each dataset is provided under the ‘“Materials and methods’” section under the appropriate subsection. Genomic tracks generated in this study including DNA methylation and histone modification tracks are deposited in the Mendeley data repository (https://doi.org/10.17632/f4k2gytbh5.1). Codes are uploaded to GitHub https://github.com/vahidAK/NanoMethPhase/tree/master/scripts (copy archived at swh:1:rev:1657f7aed60604aa7c7f3e77d992d76bee6bf6d3): PartialMethylation_AtDMR.sh, CountReadsAtSNV.py and Binomial_test.py.

1. 1. Akbari V

   (2022) Mendeley Data

   Genome-Wide Detection of Imprinted Differentially Methylated Regions Using Nanopore Sequencing_Akbari-etal.

   https://doi.org/10.17632/f4k2gytbh5.1

1. 1. Shafin K
   2. Pesout T
   3. Lorig-Roach R
   4. Haukness M
   5. Olsen HE
   6. Bosworth C
   7. Armstrong J
   8. Tigyi K
   9. Maurer N
   10. Koren S
   11. Sedlazeck FJ
   12. Marschall T
   13. Mayes S
   14. Costa V
   15. Zook JM
   16. Liu KJ
   17. Kilburn D
   18. Sorensen M
   19. Munson KM
   20. Vollger MR
   21. Monlong J
   22. Garrison E
   23. Eichler EE
   24. Salama S
   25. Haussler D
   26. Green RE
   27. Akeson M
   28. Phillippy A
   29. Miga KH
   30. Carnevali P
   31. Jain M
   32. Paten B

   (2020) Human Pangenome Reference Consortium

   ID hpgp-data. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes.

   https://github.com/human-pangenomics/hpgp-data
2. 1. Jain M
   2. Koren S
   3. Miga KH
   4. Quick J
   5. Rand AC
   6. Sasani TA
   7. Tyson JR
   8. Beggs AD
   9. Dilthey AT
   10. Fiddes IT
   11. Malla S
   12. Marriott H
   13. Nieto T
   14. O'Grady J
   15. Olsen HE
   16. Pedersen BS
   17. Rhie A
   18. Richardson H
   19. Quinlan AR
   20. Snutch TP
   21. Tee L
   22. Paten B
   23. Phillippy AM
   24. Simpson JT
   25. Loman NJ
   26. Loose M

   (2018) Nanopore WGS Consortium

   ID NA12878. Nanopore sequencing and assembly of a human genome with ultra-long reads.

   https://github.com/nanopore-wgs-consortium/NA12878/blob/master/Genome.md
3. 1. De Coster W
   2. De Rijk P
   3. De Roeck A
   4. De Pooter T
   5. D'Hert S
   6. Strazisar M
   7. Sleegers K
   8. Van Broeckhoven C

   (2019) European Nucleotide Archive

   ID PRJEB26791. Structural variants identified by Oxford Nanopore PromethION sequencing of the human genome.

   https://www.ebi.ac.uk/ena/browser/view/PRJEB26791
4. 1. Zook JM
   2. Catoe D
   3. McDaniel J
   4. Vang L
   5. Spies N
   6. Sidow A
   7. Weng Z
   8. Liu Y
   9. Mason CE
   10. Alexander N
   11. Henaff E
   12. McIntyre ABR
   13. Chandramohan D
   14. Chen F
   15. Jaeger E
   16. Moshrefi A
   17. Pham K
   18. Stedman W
   19. Liang T
   20. Saghbini M
   21. Dzakula Z
   22. Hastie A
   23. Cao H
   24. Deikus G
   25. Schadt E
   26. Sebra R
   27. Bashir A
   28. Truty RM
   29. Chang CC
   30. Gulbahce N
   31. Zhao K
   32. Ghosh S
   33. Hyland F
   34. Fu Y
   35. Chaisson M
   36. Xiao C
   37. Trow J
   38. Sherry ST
   39. Zaranek AW
   40. Ball M
   41. Bobe J
   42. Estep P
   43. Church GM
   44. Marks P
   45. Kyriazopoulou-Panagiotopoulou S
   46. Zheng GXY
   47. Schnall-Levin M
   48. Ordonez HS
   49. Mudivarti PA
   50. Giorda K
   51. Sheng Y
   52. Rypdal KB
   53. Salit M

   (2016) NCBI Genome in a Bottle FTP

   ID FTP. Extensive sequencing of seven human genomes to characterize benchmark reference materials.

   ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/
5. 1. The 1000 Genomes Project Consortium

   (2015) The International Genome Sample Resource

   ID 30x-grch38. A global reference for human genetic variation.

   https://www.internationalgenome.org/data-portal/data-collection/30x-grch38
6. 1. Stunnenberg HG
   2. Abrignani S
   3. Adams D
   4. de Almeida M
   5. Altucci L
   6. Amin V
   7. Amit I
   8. Antonarakis SE
   9. Aparicio S
   10. Arima T
   11. Arrigoni L
   12. Arts R
   13. Asnafi V
   14. Badosa ME
   15. Bae JB
   16. Bassler K
   17. Beck S
   18. Berkman B
   19. Bernstein BE
   20. Hirst M

   (2016) Blueprint Epigenome

   ID blueprint. The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery.

   https://www.blueprint-epigenome.eu/
7. 1. Bernstein BE
   2. Stamatoyannopoulos JA
   3. Costello JF
   4. Ren B
   5. Milosavljevic A
   6. Meissner A
   7. Kellis M
   8. Marra MA
   9. Beaudet AL
   10. Ecker JR
   11. Farnham PJ
   12. Hirst M
   13. Lander ES
   14. Mikkelsen TS
   15. Thomson JA

   (2010) NCBI Gene Expression Omnibus

   ID epigenomics. The NIH Roadmap Epigenomics Mapping Consortium.

   https://www.ncbi.nlm.nih.gov/geo/roadmap/epigenomics/
8. 1. ENCODE Project Consortium

   (2012) ENCODE

   ID encodeproject. An Integrated Encyclopedia of DNA Elements in the Human Genome.

   https://www.encodeproject.org/
9. 1. Okae H
   2. Chiba H
   3. Hiura H
   4. Hamada H
   5. Sato A
   6. Utsunomiya T
   7. Kikuchi H
   8. Yoshida H
   9. Tanaka A
   10. Suyama M
   11. Arima T

   (2014) DNA Data Bank of Japan

   ID DRA003802. Genome-wide analysis of DNA methylation dynamics during early human development.

   ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/DRA003/DRA003802
10. 1. Steinmetz LM
    2. Hogenesch JB
    3. Kellis M
    4. Batzoglou S
    5. Snyder M

    (2013) NCBI Sequence Read Archive

    ID SRP030041. Extensive Variation in Chromatin States Across Humans.

    https://www.ncbi.nlm.nih.gov/sra/?term=SRP030041
11. 1. Hon GC
    2. Rajagopal N
    3. Shen Y
    4. McCleary DF
    5. Yue F
    6. Dang MD
    7. Ren B

    (2013) NCBI Gene Expression Omnibus

    ID GSE42836. Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42836
12. 1. Tung J
    2. Barreiro LB
    3. Johnson ZP
    4. Hansen KD
    5. Michopoulos V
    6. Toufexis D
    7. Michelini K
    8. Wilson ME
    9. Gilad Y

    (2012) NCBI Gene Expression Omnibus

    ID GSE34128. Social environment is associated with gene regulatory variation in the rhesus macaque immune system.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE34128
13. 1. Jeong H
    2. Mendizabal I
    3. Berto S
    4. Chatterjee P
    5. Layman T
    6. Usui N
    7. Toriumi K
    8. Douglas C
    9. Singh D
    10. Huh I
    11. Preuss TM
    12. Konopka G
    13. Yi S V

    (2021) NCBI Gene Expression Omnibus

    ID GSE151768. Evolution of DNA methylation in the human brain.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151768
14. 1. Dahlet T
    2. Argüeso Lleida A
    3. Al Adhami H
    4. Dumas M
    5. Bender A
    6. Ngondo RP
    7. Tanguy M
    8. Vallet J
    9. Auclair G
    10. Bardet AF
    11. Weber M

    (2020) NCBI Gene Expression Omnibus

    ID GSE130735. Genome-wide analysis in the mouse embryo reveals the importance of DNA methylation for transcription integrity.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130735
15. 1. Jung YH
    2. Sauria MEG
    3. Lyu X
    4. Cheema MS
    5. Ausio J
    6. Taylor J
    7. Corces VG

    (2017) NCBI Gene Expression Omnibus

    ID GSE79226. Chromatin States in Mouse Sperm Correlate with Embryonic and Adult Regulatory Landscapes.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79226
16. 1. Saenz-de-Juano MD
    2. Ivanova E
    3. Billooye K
    4. Herta A-C
    5. Smitz J
    6. Kelsey G
    7. Anckaert E

    (2019) NCBI Gene Expression Omnibus

    ID GSE128656. Genome-wide assessment of DNA methylation in mouse oocytes reveals effects associated with in vitro growth, superovulation, and sexual maturity.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128656
17. 1. Gao F
    2. Niu Y
    3. Sun YE
    4. Lu H
    5. Chen Y
    6. Li S
    7. Kang Y
    8. Luo Y
    9. Si C
    10. Yu J
    11. Li C
    12. Sun N
    13. Si W
    14. Wang H
    15. Ji W
    16. Tan T

    (2017) NCBI Gene Expression Omnibus

    ID GSE60166. De novo DNA methylation during monkey pre-implantation embryogenesis.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60166
18. 1. Florian Z
    2. Droplaug NM
    3. Olafur TM
    4. Nicolas JW
    5. Tiffany JM
    6. Asgeir S
    7. Gisli HH
    8. Sigurjon AG
    9. Pall M
    10. Helga I
    11. Snædis K
    12. Kristjan
    13. Kristjan FA
    14. Anna H
    15. Julius G
    16. Thorunn R
    17. Ingileif J
    18. Hilma H
    19. Gudmundur IE
    20. Olof S
    21. Isleifur O
    22. Gisli M
    23. Daniel FG
    24. Unnur T
    25. Bjarni VH
    26. Simon NS
    27. Kari S

    (2018) figshare

    Methylation and expression data for whole-genome human imprinting study.

    https://doi.org/10.6084/m9.figshare.6816917
19. 1. Imbeault M
    2. Helleboid P
    3. Trono D

    (2017) NCBI Gene Expression Omnibus

    ID GSE78099. ChIP-exo of human KRAB-ZNFs transduced in HEK 293T cells and KAP1 in hES H1 cells.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78099
20. 1. Takahashi N
    2. Coluccio A
    3. Thorball C. W
    4. Planet E
    5. Shi H
    6. Offner S
    7. Turelli P
    8. Imbeault M
    9. Ferguson-Smith A. C
    10. Trono D

    (2019) NCBI Gene Expression Omnibus

    ID GSE115387. ZNF445 is a primary regulator of genomic imprinting.

    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115387

1. 1. Akbari V
   2. Garant J-M
   3. O’Neill K
   4. Pandoh P
   5. Moore R
   6. Marra MA
   7. Hirst M
   8. Jones SJM

   (2021) Megabase-scale methylation phasing using nanopore long reads and NanoMethPhase

   Genome Biology 22:68.

   https://doi.org/10.1186/s13059-021-02283-5

   • PubMed
   • Google Scholar
2. Software

   1. Akbari V

   (2022) NanoMethPhase, version swh:1:rev:1657f7aed60604aa7c7f3e77d992d76bee6bf6d3

   Software Heritage.

   https://archive.softwareheritage.org/swh:1:dir:544b123669c23565347328349f18f04a7fe06c41;origin=https://github.com/vahidAK/NanoMethPhase;visit=swh:1:snp:1f3158491f82b8a966e5344319e883a3742bee01;anchor=swh:1:rev:1657f7aed60604aa7c7f3e77d992d76bee6bf6d3
3. 1. Amemiya HM
   2. Kundaje A
   3. Boyle AP

   (2019) The ENCODE Blacklist: Identification of Problematic Regions of the Genome

   Scientific Reports 9:9354.

   https://doi.org/10.1038/s41598-019-45839-z

   • PubMed
   • Google Scholar
4. 1. Auton A
   2. Brooks LD
   3. Durbin RM
   4. Garrison EP
   5. Kang HM
   6. Korbel JO
   7. Marchini JL
   8. McCarthy S
   9. McVean GA
   10. Abecasis GR
   11. 1000 Genomes Project Consortium

   (2015) A global reference for human genetic variation

   Nature 526:68–74.

   https://doi.org/10.1038/nature15393

   • PubMed
   • Google Scholar
5. 1. Babak T
   2. DeVeale B
   3. Tsang EK
   4. Zhou Y
   5. Li X
   6. Smith KS
   7. Kukurba KR
   8. Zhang R
   9. Li JB
   10. van der Kooy D
   11. Montgomery SB
   12. Fraser HB

   (2015) Genetic conflict reflected in tissue-specific maps of genomic imprinting in human and mouse

   Nature Genetics 47:544–549.

   https://doi.org/10.1038/ng.3274

   • PubMed
   • Google Scholar
6. 1. Bak M
   2. Boonen SE
   3. Dahl C
   4. Hahnemann JMD
   5. Mackay DJDG
   6. Tümer Z
   7. Grønskov K
   8. Temple IK
   9. Guldberg P
   10. Tommerup N

   (2016) Genome-wide DNA methylation analysis of transient neonatal diabetes type 1 patients with mutations in ZFP57

   BMC Medical Genetics 17:29.

   https://doi.org/10.1186/s12881-016-0292-4

   • PubMed
   • Google Scholar
7. 1. Baran Y
   2. Subramaniam M
   3. Biton A
   4. Tukiainen T
   5. Tsang EK
   6. Rivas MA
   7. Pirinen M
   8. Gutierrez-Arcelus M
   9. Smith KS
   10. Kukurba KR
   11. Zhang R
   12. Eng C
   13. Torgerson DG
   14. Urbanek C
   15. GTEx Consortium
   16. Li JB
   17. Rodriguez-Santana JR
   18. Burchard EG
   19. Seibold MA
   20. MacArthur DG
   21. Montgomery SB
   22. Zaitlen NA
   23. Lappalainen T

   (2015) The landscape of genomic imprinting across diverse adult human tissues

   Genome Research 25:927–936.

   https://doi.org/10.1101/gr.192278.115

   • PubMed
   • Google Scholar
8. 1. Bartolomei MS
   2. Ferguson-Smith AC

   (2011) Mammalian genomic imprinting

   Cold Spring Harbor Perspectives in Biology 3:a002592.

   https://doi.org/10.1101/cshperspect.a002592

   • PubMed
   • Google Scholar
9. 1. Bernstein BE
   2. Stamatoyannopoulos JA
   3. Costello JF
   4. Ren B
   5. Milosavljevic A
   6. Meissner A
   7. Kellis M
   8. Marra MA
   9. Beaudet AL
   10. Ecker JR
   11. Farnham PJ
   12. Hirst M
   13. Lander ES
   14. Mikkelsen TS
   15. Thomson JA

   (2010) The NIH Roadmap Epigenomics Mapping Consortium

   Nature Biotechnology 28:1045–1048.

   https://doi.org/10.1038/nbt1010-1045

   • PubMed
   • Google Scholar
10. 1. Brinkman AB
    2. Gu H
    3. Bartels SJJ
    4. Zhang Y
    5. Matarese F
    6. Simmer F
    7. Marks H
    8. Bock C
    9. Gnirke A
    10. Meissner A
    11. Stunnenberg HG

    (2012) Sequential ChIP-bisulfite sequencing enables direct genome-scale investigation of chromatin and DNA methylation cross-talk

    Genome Research 22:1128–1138.

    https://doi.org/10.1101/gr.133728.111

    • PubMed
    • Google Scholar
11. 1. Camargo M
    2. Rivera D
    3. Moreno L
    4. Lidral AC
    5. Harper U
    6. Jones M
    7. Solomon BD
    8. Roessler E
    9. Vélez JI
    10. Martinez AF
    11. Chandrasekharappa SC
    12. Arcos-Burgos M

    (2012) GWAS reveals new recessive loci associated with non-syndromic facial clefting

    European Journal of Medical Genetics 55:510–514.

    https://doi.org/10.1016/j.ejmg.2012.06.005

    • PubMed
    • Google Scholar
12. 1. Candler T
    2. Kessler NJ
    3. Gunasekara CJ
    4. Ward KA
    5. James P
    6. Laritsky E
    7. Baker MS
    8. Dyer R
    9. Elango R
    10. Jeffries D
    11. Waterland RA
    12. Moore SE
    13. Ludgate M
    14. Prentice AM
    15. Silver MJ

    (2021) DNA methylation at a nutritionally sensitive region of the PAX8 gene is associated with thyroid volume and function in Gambian children

    Science Advances 7:eabj1561.

    https://doi.org/10.1126/sciadv.abj1561

    • PubMed
    • Google Scholar
13. 1. Chen Z
    2. Zhang Y

    (2020) Maternal H3K27me3-dependent autosomal and X chromosome imprinting

    Nature Reviews. Genetics 21:555–571.

    https://doi.org/10.1038/s41576-020-0245-9

    • PubMed
    • Google Scholar
14. 1. Cheong CY
    2. Chng K
    3. Ng S
    4. Chew SB
    5. Chan L
    6. Ferguson-Smith AC

    (2015) Germline and somatic imprinting in the nonhuman primate highlights species differences in oocyte methylation

    Genome Research 25:611–623.

    https://doi.org/10.1101/gr.183301.114

    • PubMed
    • Google Scholar
15. 1. Chess A

    (2013) Random and non-random monoallelic expression

    Neuropsychopharmacology 38:55–61.

    https://doi.org/10.1038/npp.2012.85

    • PubMed
    • Google Scholar
16. 1. Chiesa N
    2. De Crescenzo A
    3. Mishra K
    4. Perone L
    5. Carella M
    6. Palumbo O
    7. Mussa A
    8. Sparago A
    9. Cerrato F
    10. Russo S
    11. Lapi E
    12. Cubellis MV
    13. Kanduri C
    14. Cirillo Silengo M
    15. Riccio A
    16. Ferrero GB

    (2012) The KCNQ1OT1 imprinting control region and non-coding RNA: new properties derived from the study of Beckwith-Wiedemann syndrome and Silver-Russell syndrome cases

    Human Molecular Genetics 21:10–25.

    https://doi.org/10.1093/hmg/ddr419

    • PubMed
    • Google Scholar
17. 1. Court F
    2. Tayama C
    3. Romanelli V
    4. Martin-Trujillo A
    5. Iglesias-Platas I
    6. Okamura K
    7. Sugahara N
    8. Simón C
    9. Moore H
    10. Harness JV
    11. Keirstead H
    12. Sanchez-Mut JV
    13. Kaneki E
    14. Lapunzina P
    15. Soejima H
    16. Wake N
    17. Esteller M
    18. Ogata T
    19. Hata K
    20. Nakabayashi K
    21. Monk D

    (2014) Genome-wide parent-of-origin DNA methylation analysis reveals the intricacies of human imprinting and suggests a germline methylation-independent mechanism of establishment

    Genome Research 24:554–569.

    https://doi.org/10.1101/gr.164913.113

    • PubMed
    • Google Scholar
18. 1. Cuellar Partida G
    2. Laurin C
    3. Ring SM
    4. Gaunt TR
    5. McRae AF
    6. Visscher PM
    7. Montgomery GW
    8. Martin NG
    9. Hemani G
    10. Suderman M
    11. Relton CL
    12. Davey Smith G
    13. Evans DM

    (2018) Genome-wide survey of parent-of-origin effects on DNA methylation identifies candidate imprinted loci in humans

    Human Molecular Genetics 27:2927–2939.

    https://doi.org/10.1093/hmg/ddy206

    • PubMed
    • Google Scholar
19. 1. da Rocha ST
    2. Gendrel A-V

    (2019) The influence of DNA methylation on monoallelic expression

    Essays in Biochemistry 63:663–676.

    https://doi.org/10.1042/EBC20190034

    • PubMed
    • Google Scholar
20. 1. Dahlet T
    2. Argüeso Lleida A
    3. Al Adhami H
    4. Dumas M
    5. Bender A
    6. Ngondo RP
    7. Tanguy M
    8. Vallet J
    9. Auclair G
    10. Bardet AF
    11. Weber M

    (2020) Genome-wide analysis in the mouse embryo reveals the importance of DNA methylation for transcription integrity

    Nature Communications 11:3153.

    https://doi.org/10.1038/s41467-020-16919-w

    • PubMed
    • Google Scholar
21. 1. De Coster W
    2. De Rijk P
    3. De Roeck A
    4. De Pooter T
    5. D’Hert S
    6. Strazisar M
    7. Sleegers K
    8. Van Broeckhoven C

    (2019) Structural variants identified by Oxford Nanopore PromethION sequencing of the human genome

    Genome Research 29:1178–1187.

    https://doi.org/10.1101/gr.244939.118

    • PubMed
    • Google Scholar
22. 1. ENCODE Project Consortium

    (2012) An integrated encyclopedia of DNA elements in the human genome

    Nature 489:57–74.

    https://doi.org/10.1038/nature11247

    • PubMed
    • Google Scholar
23. 1. Gao F
    2. Niu Y
    3. Sun YE
    4. Lu H
    5. Chen Y
    6. Li S
    7. Kang Y
    8. Luo Y
    9. Si C
    10. Yu J
    11. Li C
    12. Sun N
    13. Si W
    14. Wang H
    15. Ji W
    16. Tan T

    (2017) De novo DNA methylation during monkey pre-implantation embryogenesis

    Cell Research 27:526–539.

    https://doi.org/10.1038/cr.2017.25

    • PubMed
    • Google Scholar
24. Website

    1. Geneimprint

    (2021) Geneimprint

    Accessed March 3, 2021.

    http://www.geneimprint.com
25. 1. Gigante S
    2. Gouil Q
    3. Lucattini A
    4. Keniry A
    5. Beck T
    6. Tinning M
    7. Gordon L
    8. Woodruff C
    9. Speed TP
    10. Blewitt ME
    11. Ritchie ME

    (2019) Using long-read sequencing to detect imprinted DNA methylation

    Nucleic Acids Research 47:e46.

    https://doi.org/10.1093/nar/gkz107

    • PubMed
    • Google Scholar
26. 1. Goovaerts T
    2. Steyaert S
    3. Vandenbussche CA
    4. Galle J
    5. Thas O
    6. Van Criekinge W
    7. De Meyer T

    (2018) A comprehensive overview of genomic imprinting in breast and its deregulation in cancer

    Nature Communications 9:1–14.

    https://doi.org/10.1038/s41467-018-06566-7

    • PubMed
    • Google Scholar
27. 1. Hanna CW
    2. Kelsey G

    (2014) The specification of imprints in mammals

    Heredity 113:176–183.

    https://doi.org/10.1038/hdy.2014.54

    • PubMed
    • Google Scholar
28. 1. Hashimoto H
    2. Wang D
    3. Horton JR
    4. Zhang X
    5. Corces VG
    6. Cheng X

    (2017) Structural Basis for the Versatile and Methylation-Dependent Binding of CTCF to DNA

    Molecular Cell 66:711–720.

    https://doi.org/10.1016/j.molcel.2017.05.004

    • PubMed
    • Google Scholar
29. 1. Hernandez Mora JR
    2. Tayama C
    3. Sánchez-Delgado M
    4. Monteagudo-Sánchez A
    5. Hata K
    6. Ogata T
    7. Medrano J
    8. Poo-Llanillo ME
    9. Simón C
    10. Moran S
    11. Esteller M
    12. Tenorio J
    13. Lapunzina P
    14. Kagami M
    15. Monk D
    16. Nakabayashi K

    (2018) Characterization of parent-of-origin methylation using the Illumina Infinium MethylationEPIC array platform

    Epigenomics 10:941–954.

    https://doi.org/10.2217/epi-2017-0172

    • PubMed
    • Google Scholar
30. 1. Hon GC
    2. Rajagopal N
    3. Shen Y
    4. McCleary DF
    5. Yue F
    6. Dang MD
    7. Ren B

    (2013) Epigenetic memory at embryonic enhancers identified in DNA methylation maps from adult mouse tissues

    Nature Genetics 45:1198–1206.

    https://doi.org/10.1038/ng.2746

    • PubMed
    • Google Scholar
31. 1. Imbeault M
    2. Helleboid PY
    3. Trono D

    (2017) KRAB zinc-finger proteins contribute to the evolution of gene regulatory networks

    Nature 543:550–554.

    https://doi.org/10.1038/nature21683

    • PubMed
    • Google Scholar
32. 1. Jadhav B
    2. Monajemi R
    3. Gagalova KK
    4. Ho D
    5. Draisma HHM
    6. van de Wiel MA
    7. Franke L
    8. Heijmans BT
    9. van Meurs J
    10. Jansen R
    11. GoNL Consortium
    12. BIOS Consortium
    13. ’t Hoen PAC
    14. Sharp AJ
    15. Kiełbasa SM

    (2019) RNA-Seq in 296 phased trios provides a high-resolution map of genomic imprinting

    BMC Biology 17:50.

    https://doi.org/10.1186/s12915-019-0674-0

    • PubMed
    • Google Scholar
33. 1. Jain M
    2. Koren S
    3. Miga KH
    4. Quick J
    5. Rand AC
    6. Sasani TA
    7. Tyson JR
    8. Beggs AD
    9. Dilthey AT
    10. Fiddes IT
    11. Malla S
    12. Marriott H
    13. Nieto T
    14. O’Grady J
    15. Olsen HE
    16. Pedersen BS
    17. Rhie A
    18. Richardson H
    19. Quinlan AR
    20. Snutch TP
    21. Tee L
    22. Paten B
    23. Phillippy AM
    24. Simpson JT
    25. Loman NJ
    26. Loose M

    (2018) Nanopore sequencing and assembly of a human genome with ultra-long reads

    Nature Biotechnology 36:338–345.

    https://doi.org/10.1038/nbt.4060

    • PubMed
    • Google Scholar
34. 1. Jelinic P
    2. Shaw P

    (2007) Loss of imprinting and cancer

    The Journal of Pathology 211:261–268.

    https://doi.org/10.1002/path.2116

    • PubMed
    • Google Scholar
35. 1. Jeong H
    2. Mendizabal I
    3. Berto S
    4. Chatterjee P
    5. Layman T
    6. Usui N
    7. Toriumi K
    8. Douglas C
    9. Singh D
    10. Huh I
    11. Preuss TM
    12. Konopka G
    13. Yi SV

    (2021) Evolution of DNA methylation in the human brain

    Nature Communications 12:2021.

    https://doi.org/10.1038/s41467-021-21917-7

    • PubMed
    • Google Scholar
36. 1. John RM
    2. Lefebvre L

    (2011) Developmental regulation of somatic imprints

    Differentiation 81:270–280.

    https://doi.org/10.1016/j.diff.2011.01.007

    • PubMed
    • Google Scholar
37. 1. Joshi RS
    2. Garg P
    3. Zaitlen N
    4. Lappalainen T
    5. Watson CT
    6. Azam N
    7. Ho D
    8. Li X
    9. Antonarakis SE
    10. Brunner HG
    11. Buiting K
    12. Cheung SW
    13. Coffee B
    14. Eggermann T
    15. Francis D
    16. Geraedts JP
    17. Gimelli G
    18. Jacobson SG
    19. Le Caignec C
    20. de Leeuw N
    21. Liehr T
    22. Mackay DJ
    23. Montgomery SB
    24. Pagnamenta AT
    25. Papenhausen P
    26. Robinson DO
    27. Ruivenkamp C
    28. Schwartz C
    29. Steiner B
    30. Stevenson DA
    31. Surti U
    32. Wassink T
    33. Sharp AJ

    (2016) DNA Methylation Profiling of Uniparental Disomy Subjects Provides a Map of Parental Epigenetic Bias in the Human Genome

    American Journal of Human Genetics 99:555–566.

    https://doi.org/10.1016/j.ajhg.2016.06.032

    • PubMed
    • Google Scholar
38. 1. Jung YH
    2. Sauria MEG
    3. Lyu X
    4. Cheema MS
    5. Ausio J
    6. Taylor J
    7. Corces VG

    (2017) Chromatin States in Mouse Sperm Correlate with Embryonic and Adult Regulatory Landscapes

    Cell Reports 18:1366–1382.

    https://doi.org/10.1016/j.celrep.2017.01.034

    • PubMed
    • Google Scholar
39. 1. Kasowski M
    2. Kyriazopoulou-Panagiotopoulou S
    3. Grubert F
    4. Zaugg JB
    5. Kundaje A
    6. Liu Y
    7. Boyle AP
    8. Zhang QC
    9. Zakharia F
    10. Spacek DV
    11. Li J
    12. Xie D
    13. Olarerin-George A
    14. Steinmetz LM
    15. Hogenesch JB
    16. Kellis M
    17. Batzoglou S
    18. Snyder M

    (2013) Extensive variation in chromatin states across humans

    Science (New York, N.Y.) 342:750–752.

    https://doi.org/10.1126/science.1242510

    • PubMed
    • Google Scholar
40. 1. Kent WJ
    2. Sugnet CW
    3. Furey TS
    4. Roskin KM
    5. Pringle TH
    6. Zahler AM
    7. Haussler D

    (2002) The human genome browser at UCSC

    Genome Research 12:996–1006.

    https://doi.org/10.1101/gr.229102

    • PubMed
    • Google Scholar
41. 1. Kent W J
    2. Zweig AS
    3. Barber G
    4. Hinrichs AS
    5. Karolchik D

    (2010) BigWig and BigBed: enabling browsing of large distributed datasets

    Bioinformatics (Oxford, England) 26:2204–2207.

    https://doi.org/10.1093/bioinformatics/btq351

    • PubMed
    • Google Scholar
42. 1. Khamlichi AA
    2. Feil R

    (2018) Parallels between Mammalian Mechanisms of Monoallelic Gene Expression

    Trends in Genetics 34:954–971.

    https://doi.org/10.1016/j.tig.2018.08.005

    • PubMed
    • Google Scholar
43. 1. Kim S
    2. Scheffler K
    3. Halpern AL
    4. Bekritsky MA
    5. Noh E
    6. Källberg M
    7. Chen X
    8. Kim Y
    9. Beyter D
    10. Krusche P
    11. Saunders CT

    (2018) Strelka2: fast and accurate calling of germline and somatic variants

    Nature Methods 15:591–594.

    https://doi.org/10.1038/s41592-018-0051-x

    • PubMed
    • Google Scholar
44. 1. Laurent L
    2. Wong E
    3. Li G
    4. Huynh T
    5. Tsirigos A
    6. Ong CT
    7. Low HM
    8. Kin Sung KW
    9. Rigoutsos I
    10. Loring J
    11. Wei CL

    (2010) Dynamic changes in the human methylome during differentiation

    Genome Research 20:320–331.

    https://doi.org/10.1101/gr.101907.109

    • PubMed
    • Google Scholar
45. 1. Li H
    2. Durbin R

    (2009) Fast and accurate short read alignment with Burrows-Wheeler transform

    Bioinformatics (Oxford, England) 25:1754–1760.

    https://doi.org/10.1093/bioinformatics/btp324

    • PubMed
    • Google Scholar
46. 1. Li H

    (2018) Minimap2: pairwise alignment for nucleotide sequences

    Bioinformatics (Oxford, England) 34:3094–3100.

    https://doi.org/10.1093/bioinformatics/bty191

    • PubMed
    • Google Scholar
47. 1. Maor-Sagie E
    2. Cinnamon Y
    3. Yaacov B
    4. Shaag A
    5. Goldsmidt H
    6. Zenvirt S
    7. Laufer N
    8. Richler C
    9. Frumkin A

    (2015) Deleterious mutation in SYCE1 is associated with non-obstructive azoospermia

    Journal of Assisted Reproduction and Genetics 32:887–891.

    https://doi.org/10.1007/s10815-015-0445-y

    • PubMed
    • Google Scholar
48. 1. Matsubara K
    2. Itoh M
    3. Shimizu K
    4. Saito S
    5. Enomoto K
    6. Nakabayashi K
    7. Hata K
    8. Kurosawa K
    9. Ogata T
    10. Fukami M
    11. Kagami M

    (2019) Exploring the unique function of imprinting control centers in the PWS/AS-responsible region: finding from array-based methylation analysis in cases with variously sized microdeletions

    Clinical Epigenetics 11:36.

    https://doi.org/10.1186/s13148-019-0633-1

    • PubMed
    • Google Scholar
49. 1. Maupetit-Méhouas S
    2. Montibus B
    3. Nury D
    4. Tayama C
    5. Wassef M
    6. Kota SK
    7. Fogli A
    8. Cerqueira Campos F
    9. Hata K
    10. Feil R
    11. Margueron R
    12. Nakabayashi K
    13. Court F
    14. Arnaud P

    (2016) Imprinting control regions (ICRs) are marked by mono-allelic bivalent chromatin when transcriptionally inactive

    Nucleic Acids Research 44:621–635.

    https://doi.org/10.1093/nar/gkv960

    • PubMed
    • Google Scholar
50. 1. Moore LD
    2. Le T
    3. Fan G

    (2013) DNA methylation and its basic function

    Neuropsychopharmacology 38:23–38.

    https://doi.org/10.1038/npp.2012.112

    • PubMed
    • Google Scholar
51. 1. Morison IM
    2. Paton CJ
    3. Cleverley SD

    (2001) The imprinted gene and parent-of-origin effect database

    Nucleic Acids Research 29:275–276.

    https://doi.org/10.1093/nar/29.1.275

    • PubMed
    • Google Scholar
52. 1. Nakabayashi K
    2. Trujillo AM
    3. Tayama C
    4. Camprubi C
    5. Yoshida W
    6. Lapunzina P
    7. Sanchez A
    8. Soejima H
    9. Aburatani H
    10. Nagae G
    11. Ogata T
    12. Hata K
    13. Monk D

    (2011) Methylation screening of reciprocal genome-wide UPDs identifies novel human-specific imprinted genes

    Human Molecular Genetics 20:3188–3197.

    https://doi.org/10.1093/hmg/ddr224

    • PubMed
    • Google Scholar
53. 1. Okae H
    2. Chiba H
    3. Hiura H
    4. Hamada H
    5. Sato A
    6. Utsunomiya T
    7. Kikuchi H
    8. Yoshida H
    9. Tanaka A
    10. Suyama M
    11. Arima T

    (2014) Genome-wide analysis of DNA methylation dynamics during early human development

    PLOS Genetics 10:e1004868.

    https://doi.org/10.1371/journal.pgen.1004868

    • PubMed
    • Google Scholar
54. 1. Park Y
    2. Wu H

    (2016) Differential methylation analysis for BS-seq data under general experimental design

    Bioinformatics (Oxford, England) 32:1446–1453.

    https://doi.org/10.1093/bioinformatics/btw026

    • PubMed
    • Google Scholar
55. 1. Puerta MF
    2. Ruth C
    3. Fang L
    4. Robert P
    5. Zhong D
    6. R.-M. Y. A
    7. B. M. S
    8. L. P. M
    9. Qiyi T
    10. H.-F. L

    (2014) CTCF Binding to the First Intron of the Major Immediate Early (MIE) Gene of Human Cytomegalovirus (HCMV

    Negatively Regulates MIE Gene Expression and HCMV Replication. Journal of Virology 88:7389–7401.

    https://doi.org/10.1128/JVI.00845-14

    • Google Scholar
56. 1. Quenneville S
    2. Verde G
    3. Corsinotti A
    4. Kapopoulou A
    5. Jakobsson J
    6. Offner S
    7. Baglivo I
    8. Pedone PV
    9. Grimaldi G
    10. Riccio A
    11. Trono D

    (2011) In embryonic stem cells, ZFP57/KAP1 recognize a methylated hexanucleotide to affect chromatin and DNA methylation of imprinting control regions

    Molecular Cell 44:361–372.

    https://doi.org/10.1016/j.molcel.2011.08.032

    • PubMed
    • Google Scholar
57. 1. Renda M
    2. Baglivo I
    3. Burgess-Beusse B
    4. Esposito S
    5. Fattorusso R
    6. Felsenfeld G
    7. Pedone PV

    (2007) Critical DNA Binding Interactions of the Insulator Protein CTCF

    Journal OF Biological Chemistry 282:33336–33345.

    https://doi.org/10.1074/jbc.M706213200

    • PubMed
    • Google Scholar
58. 1. Saenz-de-Juano MD
    2. Ivanova E
    3. Billooye K
    4. Herta AC
    5. Smitz J
    6. Kelsey G
    7. Anckaert E

    (2019) Genome-wide assessment of DNA methylation in mouse oocytes reveals effects associated with in vitro growth, superovulation, and sexual maturity

    Clinical Epigenetics 11:197.

    https://doi.org/10.1186/s13148-019-0794-y

    • PubMed
    • Google Scholar
59. 1. Shafin K
    2. Pesout T
    3. Lorig-Roach R
    4. Haukness M
    5. Olsen HE
    6. Bosworth C
    7. Armstrong J
    8. Tigyi K
    9. Maurer N
    10. Koren S
    11. Sedlazeck FJ
    12. Marschall T
    13. Mayes S
    14. Costa V
    15. Zook JM
    16. Liu KJ
    17. Kilburn D
    18. Sorensen M
    19. Munson KM
    20. Vollger MR
    21. Monlong J
    22. Garrison E
    23. Eichler EE
    24. Salama S
    25. Haussler D
    26. Green RE
    27. Akeson M
    28. Phillippy A
    29. Miga KH
    30. Carnevali P
    31. Jain M
    32. Paten B

    (2020) Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes

    Nature Biotechnology 38:1044–1053.

    https://doi.org/10.1038/s41587-020-0503-6

    • PubMed
    • Google Scholar
60. 1. Sharp AJ
    2. Migliavacca E
    3. Dupre Y
    4. Stathaki E
    5. Sailani MR
    6. Baumer A
    7. Schinzel A
    8. Mackay DJ
    9. Robinson DO
    10. Cobellis G
    11. Cobellis L
    12. Brunner HG
    13. Steiner B
    14. Antonarakis SE

    (2010) Methylation profiling in individuals with uniparental disomy identifies novel differentially methylated regions on chromosome 15

    Genome Research 20:1271–1278.

    https://doi.org/10.1101/gr.108597.110

    • PubMed
    • Google Scholar
61. 1. Simpson JT
    2. Workman RE
    3. Zuzarte PC
    4. David M
    5. Dursi LJ
    6. Timp W

    (2017) Detecting DNA cytosine methylation using nanopore sequencing

    Nature Methods 14:407–410.

    https://doi.org/10.1038/nmeth.4184

    • PubMed
    • Google Scholar
62. 1. Smith ZD
    2. Meissner A

    (2013) DNA methylation: roles in mammalian development

    Nature Reviews. Genetics 14:204–220.

    https://doi.org/10.1038/nrg3354

    • PubMed
    • Google Scholar
63. 1. Stunnenberg HG
    2. International Human Epigenome Consortium
    3. Hirst M

    (2016) The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

    Cell 167:1145–1149.

    https://doi.org/10.1016/j.cell.2016.11.007

    • PubMed
    • Google Scholar
64. 1. Takahashi N
    2. Coluccio A
    3. Thorball CW
    4. Planet E
    5. Shi H
    6. Offner S
    7. Turelli P
    8. Imbeault M
    9. Ferguson-Smith AC
    10. Trono D

    (2019) ZNF445 is a primary regulator of genomic imprinting

    Genes & Development 33:49–54.

    https://doi.org/10.1101/gad.320069.118

    • PubMed
    • Google Scholar
65. 1. Tomizawa S
    2. Sasaki H

    (2012) Genomic imprinting and its relevance to congenital disease, infertility, molar pregnancy and induced pluripotent stem cell

    Journal of Human Genetics 57:84–91.

    https://doi.org/10.1038/jhg.2011.151

    • PubMed
    • Google Scholar
66. 1. Tung J
    2. Barreiro LB
    3. Johnson ZP
    4. Hansen KD
    5. Michopoulos V
    6. Toufexis D
    7. Michelini K
    8. Wilson ME
    9. Gilad Y

    (2012) Social environment is associated with gene regulatory variation in the rhesus macaque immune system

    PNAS 109:6490–6495.

    https://doi.org/10.1073/pnas.1202734109

    • PubMed
    • Google Scholar
67. 1. Turan S
    2. Bastepe M

    (2013) The GNAS complex locus and human diseases associated with loss-of-function mutations or epimutations within this imprinted gene

    Hormone Research in Paediatrics 80:229–241.

    https://doi.org/10.1159/000355384

    • PubMed
    • Google Scholar
68. 1. Virtanen P
    2. Gommers R
    3. Oliphant TE
    4. Haberland M
    5. Reddy T
    6. Cournapeau D
    7. Burovski E
    8. Peterson P
    9. Weckesser W
    10. Bright J
    11. van der Walt SJ
    12. Brett M
    13. Wilson J
    14. Millman KJ
    15. Mayorov N
    16. Nelson ARJ
    17. Jones E
    18. Kern R
    19. Larson E
    20. Carey CJ
    21. Polat İ
    22. Feng Y
    23. Moore EW
    24. VanderPlas J
    25. Laxalde D
    26. Perktold J
    27. Cimrman R
    28. Henriksen I
    29. Quintero EA
    30. Harris CR
    31. Archibald AM
    32. Ribeiro AH
    33. Pedregosa F
    34. van Mulbregt P
    35. SciPy 1.0 Contributors

    (2020) SciPy 1.0: fundamental algorithms for scientific computing in Python

    Nature Methods 17:261–272.

    https://doi.org/10.1038/s41592-019-0686-2

    • PubMed
    • Google Scholar
69. 1. Wagner EJ
    2. Carpenter PB

    (2012) Understanding the language of Lys36 methylation at histone H3

    Nature Reviews. Molecular Cell Biology 13:115–126.

    https://doi.org/10.1038/nrm3274

    • PubMed
    • Google Scholar
70. 1. Xie W
    2. Barr CL
    3. Kim A
    4. Yue F
    5. Lee AY
    6. Eubanks J
    7. Dempster EL
    8. Ren B

    (2012) Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome

    Cell 148:816–831.

    https://doi.org/10.1016/j.cell.2011.12.035

    • PubMed
    • Google Scholar
71. 1. Yuan W
    2. Xu M
    3. Huang C
    4. Liu N
    5. Chen S
    6. Zhu B

    (2011) H3K36 methylation antagonizes PRC2-mediated H3K27 methylation

    The Journal of Biological Chemistry 286:7983–7989.

    https://doi.org/10.1074/jbc.M110.194027

    • PubMed
    • Google Scholar
72. 1. Zhao H
    2. Sun Z
    3. Wang J
    4. Huang H
    5. Kocher JP
    6. Wang L

    (2014) CrossMap: a versatile tool for coordinate conversion between genome assemblies

    Bioinformatics (Oxford, England) 30:1006–1007.

    https://doi.org/10.1093/bioinformatics/btt730

    • PubMed
    • Google Scholar
73. 1. Zink F
    2. Magnusdottir DN
    3. Magnusson OT
    4. Walker NJ
    5. Morris TJ
    6. Sigurdsson A
    7. Halldorsson GH
    8. Gudjonsson SA
    9. Melsted P
    10. Ingimundardottir H
    11. Kristmundsdottir S
    12. Alexandersson KF
    13. Helgadottir A
    14. Gudmundsson J
    15. Rafnar T
    16. Jonsdottir I
    17. Holm H
    18. Eyjolfsson GI
    19. Sigurdardottir O
    20. Olafsson I
    21. Masson G
    22. Gudbjartsson DF
    23. Thorsteinsdottir U
    24. Halldorsson BV
    25. Stacey SN
    26. Stefansson K

    (2018) Insights into imprinting from parent-of-origin phased methylomes and transcriptomes

    Nature Genetics 50:1542–1552.

    https://doi.org/10.1038/s41588-018-0232-7

    • PubMed
    • Google Scholar
74. 1. Zook JM
    2. Catoe D
    3. McDaniel J
    4. Vang L
    5. Spies N
    6. Sidow A
    7. Weng Z
    8. Liu Y
    9. Mason CE
    10. Alexander N
    11. Henaff E
    12. McIntyre ABR
    13. Chandramohan D
    14. Chen F
    15. Jaeger E
    16. Moshrefi A
    17. Pham K
    18. Stedman W
    19. Liang T
    20. Saghbini M
    21. Dzakula Z
    22. Hastie A
    23. Cao H
    24. Deikus G
    25. Schadt E
    26. Sebra R
    27. Bashir A
    28. Truty RM
    29. Chang CC
    30. Gulbahce N
    31. Zhao K
    32. Ghosh S
    33. Hyland F
    34. Fu Y
    35. Chaisson M
    36. Xiao C
    37. Trow J
    38. Sherry ST
    39. Zaranek AW
    40. Ball M
    41. Bobe J
    42. Estep P
    43. Church GM
    44. Marks P
    45. Kyriazopoulou-Panagiotopoulou S
    46. Zheng GXY
    47. Schnall-Levin M
    48. Ordonez HS
    49. Mudivarti PA
    50. Giorda K
    51. Sheng Y
    52. Rypdal KB
    53. Salit M

    (2016) Extensive sequencing of seven human genomes to characterize benchmark reference materials

    Scientific Data 3:160025.

    https://doi.org/10.1038/sdata.2016.25

    • PubMed
    • Google Scholar
75. 1. Zook JM
    2. McDaniel J
    3. Olson ND
    4. Wagner J
    5. Parikh H
    6. Heaton H
    7. Irvine SA
    8. Trigg L
    9. Truty R
    10. McLean CY
    11. De La Vega FM
    12. Xiao C
    13. Sherry S
    14. Salit M

    (2019) An open resource for accurately benchmarking small variant and reference calls

    Nature Biotechnology 37:561–566.

    https://doi.org/10.1038/s41587-019-0074-6

    • PubMed
    • Google Scholar

Author details

1. Vahid Akbari

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Medical Genetics, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8005-7776

2. Jean-Michel Garant

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Data curation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

3. Kieran O'Neill

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7609-5905

4. Pawan Pandoh

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Richard Moore

   Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Marco A Marra

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Medical Genetics, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7146-7175

7. Martin Hirst

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Microbiology and Immunology, Michael Smith Laboratories, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Formal analysis, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Steven JM Jones

   1. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, Canada
   2. Department of Medical Genetics, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review and editing

   For correspondence

   sjones@bcgsc.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3394-2208

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