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Exploring Monoallelic Methylation Using High-throughput Sequencing. Cristian Coarfa, Ronald Harris Ting Wang, Aleksandar Milosavljevic, Joe Costello. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications - PowerPoint PPT Presentation
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Exploring Monoallelic Methylation Using High-throughput Sequencing Cristian Coarfa, Ronald HarrisTing Wang, Aleksandar Milosavljevic, Joe Costello
Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications
Harris RA, Wang T, Coarfa C, Nagarajan RP, Hong C, Downey S, Johnson BE, Delaney A, Zhao Y, Olshen A, Ballinger T, Zhou X, Fosberg KJ, Gu J, Echipare L, OGeen H, Lister R, Pelizzola M, Xi Y, Epstein CB, Bernstein BE, Hawkins RD, Ren B, Chung WY, Gu H, Bock C, Gnirke A, Zhang MQ, Haussler D, Ecker JR, Li W, Farnham PJ, Waterland RA, Meissner A, Marra MA, Hirst M, Milosavljevic A, Costello JF.
In press, Nature Biotechnology
Imprinting
Non-imprinted monoallelic methylation
Cell type-specific methylation
Sites of inter-individual variation in methylation levelBiological importance of intermediate methylation levels
Methylated CpGsUnmethylated CpGsmethyl DNA immunoprecipitation(MeDIP)methylation-sensitive restriction digestion (MRE)~20 million reads/sample~100 million reads/sampleIGAII sequencingdata visualizationIllumina library constructioncombine parallel digests,ligate adapters,size-select 100-300 bpIP sonicated, adapter-ligatedDNA, size-select 100-300 bp
Unmethylated and Methylated patches within a CpG island
high MeDIP, no or low MREhigh MRE, no or low MeDIP12
Intermediate methylation levels at imprinted genes
Start Stop MRE MeDIP nearest gene GeneChr1. . . . . . . . . . . . . . . .... chr22 . . . . . . . . . . . . . . . .Initial catalogue of Intermediate methylation sites
Ting Wang, Washington University
Chr11153328115366671.034291.9069-205410HCCA2chr11194647519487870.776958.5443-18939LOC100133545chr11197514119774391.284587.55160H19chr11224568022505082.345199.4044-29211C11orf21chr11242074724232241.656529.51610KCNQ1
Using Genetic Variation to Detect Monoallelic Epigenomic and Transcription States
H1 cell line
Monoallelic DNA methylation (MRE and MeDIP)
Monoallelic expression (MethylC-seq and RNA-seq)
Monoallelic Histone H3K4me3 (MethylC-seq and Chip-seq)
MethylC-seq +ChIP-seqMethylC-seq + RNA-seqMRE-seq +MeDIP-seqMonoallelic Epigenomic Marks and Expression
Intermediate methylation levels in POTEB
Validation of monoallelic DNA methylation in POTEB
Searching for Monoallelic Methlylation Using Shotgun Bisulfite SequencingWe expect streaks of 50d% methylation ratiosUse 500bp windows tiling CpG IslandsCompute average CpG methylationCpG Islands1000 lociInfer distribution of methylation in 1000 lociSubselect 500bp windows tiling CpG IslandsIn the selected windows, search for allele specific methylation
Average methylation over 500 bp window in CpG Islands and 1000 loci
Chart1
0.04381694260.0055714741
0.04690808490.0039796243
0.04726356630.0049347342
0.04214772570.0071633238
0.03806741780.0084368036
0.03361617290.0079592486
0.03038592910.0074816937
0.02834577520.0101878383
0.02556374710.0062082139
0.02534736710.0087551735
0.02550192420.0105062082
0.02142161640.0084368036
0.01965966520.0081184336
0.01826865120.0093919134
0.0157030030.0066857689
0.0161357630.0078000637
0.01404924190.0078000637
0.01265822780.0073225088
0.01165360660.0074816937
0.01046351680.0070041388
0.01165360660.0079592486
0.00850064140.006049029
0.00877884420.0062082139
0.00710962740.0071633238
0.00571861330.0062082139
0.00737237450.0082776186
0.00633684180.0073225088
0.00625956320.0070041388
0.0056258790.0074816937
0.00499219490.0052531041
0.00644503180.0073225088
0.00468308060.0065265839
0.0048221820.0062082139
0.00451306780.0063673989
0.00443578920.0062082139
0.0042348650.0049347342
0.0041266750.0054122891
0.00403394070.005730659
0.00352390230.0054122891
0.00335388940.0049347342
0.00457489070.0078000637
0.00381756080.0054122891
0.00335388940.0070041388
0.00372482650.0055714741
0.00326115520.0068449538
0.00338480090.0074816937
0.00316842090.0071633238
0.00316842090.0065265839
0.00323024370.0062082139
0.00298295240.0052531041
0.0040184850.0071633238
0.00279748380.005889844
0.00361663650.0081184336
0.00315296520.0074816937
0.00346207940.0081184336
0.00333843370.0078000637
0.00306023090.0079592486
0.00316842090.0079592486
0.00289021810.006049029
0.00323024370.0066857689
0.00374028220.0093919134
0.00289021810.0101878383
0.00313750950.0098694683
0.00323024370.0095510984
0.00318387660.0133715377
0.00312205380.0100286533
0.00366300370.0085959885
0.00347753510.0120980579
0.00338480090.0113021331
0.00344662370.0114613181
0.00409576360.0128939828
0.00392575080.011620503
0.00449761210.0135307227
0.00462125780.0148042025
0.00386392790.0170327921
0.00547132190.017510347
0.00517766340.0167144222
0.0056258790.017510347
0.00590408190.0195797517
0.00649139890.0192613817
0.00874793280.0224450812
0.00834608430.0245144858
0.00913432560.0272206304
0.01103537810.0238777459
0.01163815090.0262655205
0.0142037990.0316778096
0.01540934450.0272206304
0.016939460.0289716651
0.01905689250.0248328558
0.01863958830.0245144858
0.01969057670.0194205667
0.01664580150.0122572429
0.01357011480.0093919134
0.00908795850.0049347342
0.00598136040.0033428844
0.00344662370.0028653295
0.00187014110.0007959249
0.00089643130.0003183699
0.00038639280.000159185
0.00142192550.0009551098
% of CpG Islands windows
% windows in 1000 loci
Percent methylation
% of windows
Average Methylation Scores over 500bp windows in CpG Islands and 1000 putative intermediate methylation loci
Methylation CpG Islands
Meth levelNumber of windows in CpG Islands% of CpG Islands windowsNumber of windows in 1000 loci% windows in 1000 loci
028354.38%350.56%
130354.69%250.40%
230584.73%310.49%
327274.21%450.72%
424633.81%530.84%
521753.36%500.80%
619663.04%470.75%
718342.83%641.02%
816542.56%390.62%
916402.53%550.88%
1016502.55%661.05%
1113862.14%530.84%
1212721.97%510.81%
1311821.83%590.94%
1410161.57%420.67%
1510441.61%490.78%
169091.40%490.78%
178191.27%460.73%
187541.17%470.75%
196771.05%440.70%
207541.17%500.80%
215500.85%380.60%
225680.88%390.62%
234600.71%450.72%
243700.57%390.62%
254770.74%520.83%
264100.63%460.73%
274050.63%440.70%
283640.56%470.75%
293230.50%330.53%
304170.64%460.73%
313030.47%410.65%
323120.48%390.62%
332920.45%400.64%Looking at 500 bp windows with 30-80% methylation
342870.44%390.62%Rediscover 950 of the 1000 loci
352740.42%310.49%
362670.41%340.54%
372610.40%360.57%
382280.35%340.54%
392170.34%310.49%
402960.46%490.78%
412470.38%340.54%
422170.34%440.70%
432410.37%350.56%
442110.33%430.68%
452190.34%470.75%
462050.32%450.72%
472050.32%410.65%
482090.32%390.62%
491930.30%330.53%
502600.40%450.72%
511810.28%370.59%
522340.36%510.81%
532040.32%470.75%
542240.35%510.81%
552160.33%490.78%
561980.31%500.80%
572050.32%500.80%
581870.29%380.60%
592090.32%420.67%
602420.37%590.94%
611870.29%641.02%
622030.31%620.99%
632090.32%600.96%
642060.32%841.34%
652020.31%631.00%
662370.37%540.86%
672250.35%761.21%
682190.34%711.13%
692230.34%721.15%
702650.41%811.29%
712540.39%731.16%
722910.45%851.35%
732990.46%931.48%
742500.39%1071.70%
753540.55%1101.75%
763350.52%1051.67%
773640.56%1101.75%
783820.59%1231.96%
794200.65%1211.93%
805660.87%1412.24%
815400.83%1542.45%
825910.91%1712.72%
837141.10%1502.39%
847531.16%1652.63%
859191.42%1993.17%
869971.54%1712.72%
8710961.69%1822.90%
8812331.91%1562.48%
8912061.86%1542.45%
9012741.97%1221.94%
9110771.66%771.23%
928781.36%590.94%
935880.91%310.49%
943870.60%210.33%
952230.34%180.29%
961210.19%50.08%
97580.09%20.03%
98250.04%10.02%
100920.14%60.10%
647016282
&C&A
&CPage &P
Methylation CpG Islands
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% of CpG Islands windows
% windows in 1000 loci
Percent methylation
% of windows
Average Methylation Scores over 500bp windows in CpG Islands and 1000 putative intermediate methylation loci
SBS-ASM loci
Imprinting TargetGenesChromosomeLocationMethylation Ratio Reference AlleleMethylation Ratio Alternative Allele
chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544531460.03958224240.2182743041
chr6:31383220-31385505HLA-B,HLA-Cchr6313842010.10620915030.2990757138
chr19:54250034-54253309RUVBL2,CGB8,CGB7,CGB5,KCNA7,CGB2,CGB1,CGB,LHB,SNRP70.2,SNRP70,NTF5chr19542511000.16843971630.2415816327
chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544529730.09942037760.2145667433
chr4:191171919-191174993TUBB4Q,DUX4C,FRG2chr41911733840.10714285710.225
chrY:11947004-11950328N/AchrY119493000.11085271320.22
chr9:66194587-66197092N/Achr9661954320.17111111110.2107142857
chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275753640.17801274990.2544820124
chr16:33870863-33874924LOC649159chr16338732810.08121468930.2108695652
chr20:31717472-31720650C20orf144,C20orf134,APBA2BP,PXMP4,CBFA2T2.3,PXMP4.2,CBFA2T2.2,E2F1,APBA2BP.2,CBFA2T2chr20317184920.17373737370.2343669251
chr16:33867738-33870988LOC649159chr16338689540.15299026590.2829022989
chr3:75802981-75805596N/Achr3758036280.13690255110.2073134022
chr10:46411453-46414301SYT15,SYT15.2,GPRIN2chr10464120670.14393939390.2083304853
chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275743840.16108084450.2547974307
chr20:26135638-26139348N/Achr20261372970.14018646040.2044407796
chr3:130246227-130249742RAB43,CCDC48,GP9chr31302483820.22916666670.0064935065
chr4:191097585-191100469FRG1,TUBB4Qchr41910986230.01158046960.3334500626
chr19:812930-815227C19orf22,PRG2.2,PRTN3,AZU1,PTBP1,PTBP1.4,CFD,THRAP5,PTBP1.2,ELA2,PTBP1.3chr198143880.26666666670.1265107212
chr6:108060856-108064386FLJ10159chr61080627880.0379629630.5191197691
chr16:33870863-33874924LOC649159chr16338723650.0468750.2941176471
chr3:75802981-75805596N/Achr37580449000.2941176471
chr4:191171919-191174993TUBB4Q,DUX4C,FRG2chr41911729570.05336134450.2712820513
chr17:28172533-28174999MYO1Dchr17281735890.16666666670.2222222222
chr3:75915941-75918448N/Achr3759171840.16203959970.2069444444
chr5:177344140-177346359,chr5:177342948-177345545LOC653316,PROP1,LOC653316,PROP1chr51773453410.1670292670.3183982684
chr4:4278000-4281740OTOP1,TMEM128,LYARchr442799560.09259259260.22237784
chr16:33870863-33874924LOC649159chr16338723580.10636363640.2026811789
chr3:75802981-75805596N/Achr3758045590.13043478260.2285714286
chr16:33867738-33870988LOC649159chr16338693110.10794905580.2075233467
chr16:33867738-33870988LOC649159chr16338699560.11957671960.2757554945
chr4:191175795-191178585TUBB4Q,DUX4C,FRG2chr41911774300.12747747750.2780509931
chr1:16845072-16848632NBPF1chr1168472240.17730923690.2102564103
chr19:60282717-60286792EPS8L1.3,PPP1R12C,TNNT1,EPS8L1.2,RDH13,GP6,EPS8L1chr19602854040.41666666670
chr20:26135638-26139348N/Achr20261379810.16471568350.255246592
chr16:33867738-33870988LOC649159chr16338687660.19797711210.2647193229
chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275745350.22048505370.1801493532
chr19:18839351-18843200UPF1,LASS1.2,LASS1,GDF1,COPE.3,COPE,COPE.2,DDX49chr19188415900.09615384620.2104166667
chr20:26135638-26139348N/Achr20261384430.13333333330.2864583333
chr3:75789310-75791885N/Achr3757901050.07501369540.2178603604
chr16:33870863-33874924LOC649159chr16338721860.24647495360.1625
chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275751690.18286384980.2408107898
chr4:42198-44744ZNF718,ZNF595chr4434280.14471544720.3188573655
chr17:28172533-28174999MYO1Dchr17281736200.43333333330.1145833333
chr9:67902472-67904732N/Achr9679035600.20256410260.1084002677
chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275750800.19893320760.4120905667
chr20:26135638-26139348N/Achr20261384360.250.1732098553
chr4:191171919-191174993TUBB4Q,DUX4C,FRG2chr41911731690.12105263160.2591489319
chr10:130397433-130399648N/Achr101303986530.23880597010.0454545455
chr16:33867738-33870988LOC649159chr16338699230.18103448670.3137566138
chr1:1086906-1091447TTLL10,TNFRSF18.3,C1orf159,TNFRSF4,TNFRSF18.2,TNFRSF18chr110899700.0370370370.2142857143
chr16:33870863-33874924LOC649159chr16338723160.2870370370.1429577465
chr1:991526-996181C1orf159,AGRINchr19929160.21505376340.1631205674
chr16:33867738-33870988LOC649159chr16338692030.22744708990.1696969697
chr13:24012604-24015429PARP4chr13240144680.21473063970.1599415205
chr4:191097585-191100469FRG1,TUBB4Qchr41910989890.16122802980.2071052989
chr12:131482773-131484987N/Achr121314828810.11694677870.2036458333
chr9:66194587-66197092N/Achr9661957720.13254707840.2039458217
chr19:60282717-60286792EPS8L1.3,PPP1R12C,TNNT1,EPS8L1.2,RDH13,GP6,EPS8L1chr19602852290.12916666670.2653333333
chrY:11947004-11950328N/AchrY119480220.21807044170.1744408369
chr19:46045291-46047525CYP2A7.2,CYP2A7,CYP2A6,EGLN2.2,EGLN2,EGLN2.3chr19460464460.01724137930.2162162162
chr20:26135638-26139348N/Achr20261373080.27083333330.1834654235
chr7:129916975-129921347MEST,COPG2,MEST.2,TSGA14,MEST.3chr71299200620.24619883040.1873308409
chr16:33870863-33874924LOC649159chr16338722240.18918918920.3263888889
chr9:66196371-66199460N/Achr9661981800.06232876710.242824875
chr9:34612512-34615081,chr9:34613586-34615793OPRS1.3,IL11RA.2,IL11RA,CNTFR,CCL27,GALT,CNTFR.2,OPRS1.2,DCTN3.2,C9orf23.2,C9orf23,ARID3C,OPRS1,DCTN3,OPRS1.3,IL11RA.2,IL11RA,CNTFR,CCL27,GALT,CNTFR.2,OPRS1.2,DCTN3.2,C9orf23.2,C9orf23,ARID3C,OPRS1,DCTN3chr9346137180.19357429720.2708333333
chr3:75799803-75802846N/Achr3758013150.18928284410.2144067797
chr16:33870863-33874924LOC649159chr16338717580.22580645160.0962803726
chr16:33870863-33874924LOC649159chr16338739020.16176470590.2222222222
chr16:33870863-33874924LOC649159chr16338722150.17647058820.2266666667
chr20:26135638-26139348N/Achr20261379300.14939100580.2029379347
chr20:26135638-26139348N/Achr20261382550.00568181820.2317997842
chr3:75802981-75805596N/Achr3758042790.06572769950.2496151996
chr14:103706827-103709413N/Achr141037081940.06060606060.2078651685
chr16:33867738-33870988LOC649159chr16338691080.17094017090.2272727273
chr2:91139907-91142695N/Achr2911408740.25870646770.161352657
chr20:26135638-26139348N/Achr20261381730.16913214990.4047619048
chr16:33870863-33874924LOC649159chr16338730340.20913461540.1516946803
chr16:33870863-33874924LOC649159chr16338736730.26121794870.1907078429
chr16:33867738-33870988LOC649159chr16338686390.18540669860.3181584088
chr19:669098-672953C19orf21,PALM,FSTL3,PRSSL1,PALM.2chr196703220.16170634920.31
chr16:33867738-33870988LOC649159chr16338684830.11342592590.2352941176
chr2:91139907-91142695N/Achr2911407930.24132007230.1624223602
chr16:33870863-33874924LOC649159chr16338738130.24862313170.1631763129
chrY:11947004-11950328N/AchrY119485630.20217917680.1696648966
chr4:1396446-1399014CRIPAK,KIAA1530chr413974720.1235104670.2711926962
chr9:99022031-99025780ZNF322B,KIAA1529chr9990238830.09670781890.2673076923
chr4:191175001-191177268,chr4:191175795-191178585TUBB4Q,DUX4C,FRG2,TUBB4Q,DUX4C,FRG2chr41911768860.0686274510.2083333333
chr16:33867738-33870988LOC649159chr16338686240.19175036810.2360421472
chr16:33867738-33870988LOC649159chr16338684730.18032006920.3076923077
chr8:145174273-145181015EXOSC4,CYC1,GRINA,PARP10,GPAA1,OPLAH,GRINA.2,SPATC1,SHARPINchr81451797830.10666666670.2076923077
chr20:26135638-26139348N/Achr20261371080.03098958330.2066381374
chr20:26135638-26139348N/Achr20261381580.07708333330.2397435897
chr16:33867738-33870988LOC649159chr16338690010.16216216220.2054263566
chr17:1423797-1426009SLC43A2,PITPNAchr1714254620.19675925930.2660493827
chr16:33867738-33870988LOC649159chr163386949100.2427606178
chr4:191178728-191182892DUX4.3,TUBB4Q,DUX4.4,DUX4C,FRG2chr41911800710.34482758620.1964512028
chr16:33870863-33874924LOC649159chr16338735630.31250.1593873099
chr8:145672846-145675739PPP1R16A,CYHR1,VPS28.2,NFKBIL2,MFSD3,MGC70857,GPT,FOXH1,LRRC14,LRRC24,KIAA1688,RECQL4,KIFC2,VPS28chr81456735080.21851851850.1568181818
chr4:191097585-191100469FRG1,TUBB4Qchr41910992530.05950812470.2038690476
chr5:178352831-178355943GRM6,ZNF454chr51783538750.156250.2072463768
chr15:19219795-19222683N/Achr15192215210.01315789470.249021164
chr9:66196371-66199460N/Achr9661981260.03282828280.2253278123
chr14:105014466-105017745CRIP1,C14orf80,CRIP2,MTA1,TMEM121chr141050150840.06724063240.3024680604
chr3:75802981-75805596N/Achr3758036870.07633477630.270339676
chr17:21159584-21162005MAP2K3,MAP2K3.2chr17211609780.07807296870.233531746
chr4:191178728-191182892DUX4.3,TUBB4Q,DUX4.4,DUX4C,FRG2chr41911808500.21800519050.1850461236
chr4:191097585-191100469FRG1,TUBB4Qchr41910991750.08101103690.2075831025
chr18:12766496-12769113PTPN2.2,PTPN2.3,PTPN2chr18127679480.02127659570.2173202614
chr2:132825786-132828789N/Achr21328269030.16331168830.25
chr3:75802981-75805596N/Achr37580421400.2359085359
chr17:28172533-28174999MYO1Dchr17281738770.05122865440.2465607957
chr16:33870863-33874924LOC649159chr16338720020.20265936510.1065667503
chrY:11947004-11950328N/AchrY119485100.20179195560.1829223889
chr16:33870863-33874924LOC649159chr16338736110.38271604940.0223880597
chr19:7838263-7841898LRRC8E,FLJ22184,EVI5L,MAP2K7,SNAPC2chr1978393050.2318840580.1553366174
chr15:19346666-19350003POTE15chr15193481120.25833333330.1798245614
chr16:33870863-33874924LOC649159chr16338724920.07499795920.228880074
chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544529930.22087301590.150615659
chr16:33870863-33874924LOC649159chr16338725630.20183841150.1737012987
chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275747680.17077625570.3514244874
chr3:75802981-75805596N/Achr3758037350.04344476210.3671643706
chr17:28172533-28174999MYO1Dchr17281739320.27516596530.1471453043
chr16:33870863-33874924LOC649159chr16338735370.27195910970.1737784416
chr1:154451861-154454271PAQR6.2,BGLAP,SEMA4A,PMF1,SMG5,PAQR6,SLC25A44chr11544530850.17348448210.2089833153
chrY:11947004-11950328N/AchrY119479680.22561904760.1986111111
chr16:33870863-33874924LOC649159chr16338727000.21362368140.1713352007
chr16:33867738-33870988LOC649159chr16338695270.19736842110.3588804053
chr10:127573239-127576528BCCIP.3,FANK1,DHX32chr101275754680.12711864410.2036931818
chr18:12766496-12769113PTPN2.2,PTPN2.3,PTPN2chr18127678410.23684210530.1541666667
chr22:18088081-18096311TBX1.2,TBX1,SEPT5,GP1BB,TBX1.3chr22180946060.36271929820.0782828283
chr6:144369609-144372540PLAGL1.2,PLAGL1chr61443706610.16282051280.2258907759
chr5:115325249-115328036,chr5:115324506-115326992AP3S1.2,AP3S1,FLJ90650,AP3S1.2,AP3S1,FLJ90650chr511532637400.2106481481
chr16:33867738-33870988LOC649159chr16338694450.22294372290.138996139
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SBS-ASM genes
AGRIN
AP3S1
AP3S1.2
APBA2BP
APBA2BP.2
ARID3C
AZU1
BCCIP.3
BGLAP
C14orf80
C19orf21
C19orf22
C1orf159
C20orf134
C20orf144
C9orf23
C9orf23.2
CBFA2T2
CBFA2T2.2
CBFA2T2.3
CCDC48
CCL27
CFD
CGB
CGB1
CGB2
CGB5
CGB7
CGB8
CNTFR
CNTFR.2
COPE
COPE.2
COPE.3
COPG2
CRIP1
CRIP2
CRIPAK
CYC1
CYHR1
CYP2A6
CYP2A7
CYP2A7.2
DCTN3
DCTN3.2
DDX49
DHX32
DUX4.3
DUX4.4
DUX4C
E2F1
EGLN2
EGLN2.2
EGLN2.3
ELA2
EPS8L1
EPS8L1.2
EPS8L1.3
EVI5L
EXOSC4
FANK1
FLJ10159
FLJ22184
FLJ90650
FOXH1
FRG1
FRG2
FSTL3
GALT
GDF1
GP1BB
GP6
GP9
GPAA1
GPRIN2
GPT
GRINA
GRINA.2
GRM6
HLA-B
HLA-C
IL11RA
IL11RA.2
KCNA7
KIAA1529
KIAA1530
KIAA1688
KIFC2
LASS1
LASS1.2
LHB
LOC649159
LOC653316
LRRC14
LRRC24
LRRC8E
LYAR
MAP2K3
MAP2K3.2
MAP2K7
MEST
MEST.2
MEST.3
MFSD3
MGC70857
MTA1
MYO1D
N/A
NBPF1
NFKBIL2
NTF5
OPLAH
OPRS1
OPRS1.2
OPRS1.3
OTOP1
PALM
PALM.2
PAQR6
PAQR6.2
PARP10
PARP4
PITPNA
PLAGL1
PLAGL1.2
PMF1
POTE15
PPP1R12C
PPP1R16A
PRG2.2
PROP1
PRSSL1
PRTN3
PTBP1
PTBP1.2
PTBP1.3
PTBP1.4
PTPN2
PTPN2.2
PTPN2.3
PXMP4
PXMP4.2
RAB43
RDH13
RECQL4
RUVBL2
SEMA4A
SEPT5
SHARPIN
SLC25A44
SLC43A2
SMG5
SNAPC2
SNRP70
SNRP70.2
SPATC1
SYT15
SYT15.2
TBX1
TBX1.2
TBX1.3
THRAP5
TMEM121
TMEM128
TNFRSF18
TNFRSF18.2
TNFRSF18.3
TNFRSF4
TNNT1
TSGA14
TTLL10
TUBB4Q
UPF1
VPS28
VPS28.2
ZNF322B
ZNF454
ZNF595
ZNF718
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Parameter SearchExperimented with various lower and upper bounds for methylationGuidelinesDiscover as many of the 1000 loci Reduce the overall number of 500bp windows30-80 rediscovers 958 of loci, at the highest specificity
ResultsFound 6295 heterozygous sites 586 sites have allele specific methylationOverlap with 62 of the 1000 loci37 of the loci discovered using pairs of assays25 new loci
MethylC-seq +ChIP-seqMethylC-seq + RNA-seqMRE-seq +MeDIP-seqMonoallelic Epigenomic Marks and ExpressionDistribution of the 62 SBS-ASM loci
Additional25 loci
Breast Tissue Allele specific methylationDetermine informative heterozygous SNPs Loci with monoallelic MRE-seq and MeDIP-seq
Breast Tissue Multiple cell typesDifferent epigenotypesSame genotypeIdentify monoallelic events ConstitutionalTissue specificCell types for four individualsConserved monoallelic marksIndividual specific monoallelic marks
Integrate Array-based and Seq-based methodsCollaboration with Leo Schalkwyk and Jonathan Mill, Kings College, UKInvestigate same breast tissue samples
InsightCostResults# of ASM lociDistribution of ASM loci identified by each methodSuggestions for designing future studies
Acknowledgements NIEHS/NIDA: Joni Rutter, Tanya Barrett, Fred Tyson, Christine Colvis EDACC: R. Alan Harris, Cristian Coarfa, Yuanxin Xi, Wei Li, Robert A. Waterland, Aleksandar Milosavljevic
UCSF/GSC REMC: Raman Nagarajan, Chibo Hong, Sara Downey, Brett E. Johnson, Allen Delaney, Yongjun Zhao, Marco Marra, Martin Hirst, Joseph Costello
UCSC: Tracy Ballinger, David Haussler
Washington University: Xin Zhou, Maximiliaan Schillebeeckx, Ting Wang
UCD: Lorigail Echipare, Henriette OGeen, Peggy J. Farnham
UCSD REMC: Ryan Lister, Mattia Pelizzola, Bing Ren, Joseph Ecker
Cold Spring Harbor: Wen-Yu Chung, Michael Q. Zhang
Broad REMC: Hongcang Gu, Christoph Bock, Andreas Gnirke, Chuck Epstein, Brad Bernstein, Alexander Meissner
*maybe 20 M reads per lane (optimal), range ~ 10 - 20 M reads/laneUSing all CpG islands genome wide here, correct?
Not sure I understand the cutoffs. Can you elaborate more about what the >20,