Yaamini’s Notebook: MethCompare Part 8

Characterizing CpGs in 5x data

After confirming last week that mapping to a pan-genome wasn’t changing our output, I went ahead with the CpG characterization pipeline. We decided to move ahead with 5x data with the option to return to 10x data later. I decided to create a new Jupyter notebook for running 5x data through the pipeline.

Repo fiasco

Turns out we haven’t mastered how to have five people working on a repo simultaneously without having pull-and-push issues. When Sam reverted to an old repo version, I lost the checksum code I worked on earlier. The first thing I did was add the code back in to my 10x Jupyter notebook and to the new notebook I created for 5x data. This isn’t the first time a repo commit from someone else lost some of my code, so hopefully we figure out!

M. capitata

Using 5x files provided more CpG data to characterize, but the percentages of methylated CpGs total and in various genome features were mostly consistent between 5x and 10x data.

Table 1. Methylated, sparsely methylated, and unmethylated CpGs in M. capitata

Sample Method CpG with Data Methylated CpG Sparsely Methylated CpG Unmethylated CpG
10 WGBS 4571288 450582 (9.9%) 547868 (12.0%) 3572838 (78.2%)
11 WGBS 4661716 528902 (11.3%) 517805 (11.1%) 3615009 (77.5%)
12 WGBS 8791700 1059904 (12.1%) 1000337 (11.4%) 6731459 (76.6%)
13 RRBS 3173254 257741 (8.1%) 152042 (4.8%) 2763471 (87.1%)
14 RRBS 2648697 184742 (7.0%) 135052 (5.1%) 2328903 (87.9%)
15 RRBS 3176517 231347 (7.3%) 179454 (5.6%) 2765716 (87.1%)
16 MBD-BSSeq 583599 106695 (18.3%) 74839 (12.8%) 402065 (68.9%)
17 MBD-BSSeq 242390 45506 (18.8%) 28850 (11.9%) 168034 (69.3%)
18 MBD-BSSeq 153392 29468 (19.2%) 16793 (10.9%) 107131 (69.8%)

Table 2. Number and percent of CpGs that overlap with genes in M. capitata.

Sample Method CpGs with Data Methylated CpGs Sparsely Methylated CpGs Unmethylated CpGs
10 WGBS 1683069 230343 (13.7%) 196827 (11.7%) 1255899 (74.6%)
11 WGBS 1740149 267181 (15.3%) 188348 (10.8%) 1284620 (73.8%)
12 WGBS 3204885 533230 (16.6%) 348967 (10.9%) 2322688 (72.5%)
13 RRBS 1062577 123271 (11.6%) 53235 (5.0%) 886071 (83.4%)
14 RRBS 895852 89865 (10.0%) 48499 (5.4%) 757488 (84.6%)
15 RRBS 1064769 113217 (10.6%) 64106 (6.0%) 887446 (83.3%)
16 MBD-BSSeq 208503 48436 (23.2%) 25316 (12.1%) 134751 (64.6%)
17 MBD-BSSeq 83519 20458 (24.5%) 9441 (11.3%) 53620 (64.2%)
18 MBD-BSSeq 50302 12960 (25.8%) 4843 (9.6%) 32499 (64.6%)

Table 3. Number and percent of CpGs that overlap with CDS in M. capitata.

Sample Method CpGs with Data Methylated CpGs Sparsely Methylated CpGs Unmethylated CpGs
10 WGBS 476579 54412 (11.4%) 60266 (12.6%) 361901 (75.9%)
11 WGBS 496887 64070 (12.9%) 58258 (11.7%) 374559 (75.4%)
12 WGBS 791965 113396 (14.3%) 89455 (11.3%) 589114 (74.4%)
13 RRBS 200808 18158 (9.0%) 11383 (5.7%) 171267 (85.3%)
14 RRBS 172313 12833 (7.4%) 10486 (6.1%) 148994 (86.5%)
15 RRBS 203147 15130 (7.4%) 14015 (6.9%) 174002 (85.7%)
16 MBD-BSSeq 72490 13535 (18.7%) 9666 (13.3%) 49289 (68.0%)
17 MBD-BSSeq 29305 5649 (19.3%) 3690 (12.6%) 19966 (68.1%)
18 MBD-BSSeq 17619 3992 (22.7%) 1907 (10.8%) 11720 (66.5%)

Table 4. Number and percent of CpGs that overlap with introns in M. capitata.

Sample Method CpGs with Data Methylated CpGs Sparsely Methylated CpGs Unmethylated CpGs
10 WGBS 1207609 176102 (14.6%) 136671 (11.3%) 894836 (74.1%)
11 WGBS 1244403 203311 (16.3%) 130195 (10.5%) 910897 (73.2%)
12 WGBS 2415036 420249 (17.4%) 259702 (10.8%) 1735085 (71.8%)
13 RRBS 862191 105145 (12.2%) 41866 (4.9%) 715180 (82.9%)
14 RRBS 723933 77053 (10.6%) 38032 (5.3%) 608848 (84.1%)
15 RRBS 862091 98111 (11.4%) 50116 (5.8%) 713864 (82.8%)
16 MBD-BSSeq 136153 34929 (25.7%) 15666 (11.5%) 85558 (62.8%)
17 MBD-BSSeq 54275 14821 (27.3%) 5755 (10.6%) 33699 (62.1%)
18 MBD-BSSeq 32717 8973 (27.4%) 2942 (9.0%) 20802 (63.6%)

Table 5. Number and percent of CpGs that overlap with intergenic regions in M. capitata.

Sample Method CpGs with Data Methylated CpGs Sparsely Methylated CpGs Unmethylated CpGs
10 WGBS 2888219 220239 (7.6%) 351041 (12.2%) 2316939 (80.2%)
11 WGBS 2921567 261721 (9.0%) 329457 (11.3%) 2330389 (79.8%)
12 WGBS 5586815 526674 (9.4%) 651370 (11.7%) 4408771 (78.9%)
13 RRBS 2110677 134470 (6.4%) 98807 (4.7%) 1877400 (88.9%)
14 RRBS 1752845 94877 (5.4%) 86553 (4.9%) 1571415 (89.6%)
15 RRBS 2111748 118130 (5.6%) 115348 (5.5%) 1878270 (88.9%)
16 MBD-BSSeq 375096 58259 (15.5%) 49523 (13.2%) 267314 (71.3%)
17 MBD-BSSeq 158871 25048 (15.8%) 19409 (12.2%) 114414 (72.0%)
18 MBD-BSSeq 103090 16508 (16.0%) 11950 (11.6%) 74632 (72.4%)

P. acuta

Table 6. Methylated, sparsely methylated, and unmethylated CpGs in P. acuta

Sample Method CpG with Data Methylated CpG Sparsely Methylated CpG Unmethylated CpG
1 WGBS 5546051 110364 (2.0%) 367019 (6.6%) 5068668 (91.4%)
2 WGBS 6358722 126440 (2.0%) 345887 (5.4%) 5886395 (92.6%)
3 WGBS 5866786 124819 (2.1%) 385346 (6.6%) 5356621 (91.3%)
4 RRBS 1835561 31047 (1.7%) 137700 (7.5%) 1666814 (90.8%)
5 RRBS 1451229 30345 (2.1%) 64837 (4.5%) 1356047 (93.4%)
6 RRBS 1517358 26617 (1.8%) 89246 (5.9%) 1401495 (92.4%)
7 MBD-BSSeq 2640625 258222 (9.8%) 296059 (11.2%) 2086344 (79.0%)
8 MBD-BSSeq 539008 213342 (39.6%) 80086 (14.9%) 245580 (45.6%)
9 MBD-BSSeq 2732607 255370 (9.3%) 337855 (12.4%) 2139382 (78.3%)

Table 7. Number and percent of CpGs that overlap with genes in P. acuta.

Sample Method CpG with Data Methylated CpG Sparsely Methylated CpG Unmethylated CpG
1 WGBS 2466992 73959 (3.0%) 157337 (6.4%) 2235696 (90.6%)
2 WGBS 2761956 85861 (3.1%) 144292 (5.2%) 2531803 (91.7%)
3 WGBS 2588278 82377 (3.2%) 161791 (6.3%) 2344110 (90.6%)
4 RRBS 776123 13588 (1.8%) 56290 (7.3%) 706245 (91.0%)
5 RRBS 607225 12789 (2.1%) 25954 (4.3%) 568482 (93.6%)
6 RRBS 639570 11396 (1.8%) 35633 (5.6%) 592541 (92.6%)
7 MBD-BSSeq 1253805 118291 (9.4%) 120485 (9.6%) 1015029 (81.0%)
8 MBD-BSSeq 220096 86074 (39.1%) 27902 (12.7%) 106120 (48.2%)
9 MBD-BSSeq 1281644 125536 (9.8%) 139034 (10.8%) 1017074 (79.4%)

Table 8. Number and percent of CpGs that overlap with CDS in P. acuta.

Sample Method CpG with Data Methylated CpG Sparsely Methylated CpG Unmethylated CpG
1 WGBS 1494340 59188 (4.0%) 89863 (6.0%) 1345289 (90.0%)
2 WGBS 1620632 66365 (4.1%) 76868 (4.7%) 1477399 (91.2%)
3 WGBS 1552715 65245 (4.2%) 89654 (5.8%) 1397816 (90.0%)
4 RRBS 500779 9644 (1.9%) 36616 (7.3%) 454519 (90.8%)
5 RRBS 395869 8808 (2.2%) 17345 (4.4%) 369716 (93.4%)
6 RRBS 415529 7954 (1.9%) 23679 (5.7%) 383896 (92.4%)
7 MBD-BSSeq 931250 92559 (9.9%) 87369 (9.4%) 751322 (80.7%)
8 MBD-BSSeq 184606 70696 (38.3%) 21973 (11.9%) 91937 (49.8%)
9 MBD-BSSeq 924825 95614 (10.3%) 98699 (10.7%) 730512 (79.0%)

Table 9. Number and percent of CpGs that overlap with introns in P. acuta.

Sample Method CpG with Data Methylated CpG Sparsely Methylated CpG Unmethylated CpG
1 WGBS 1840138 41787 (2.3%) 122271 (6.6%) 1676080 (91.1%)
2 WGBS 2113295 51567 (2.4%) 117738 (5.6%) 1943990 (92.0%)
3 WGBS 1946150 47352 (2.4%) 128122 (6.6%) 1770676 (91.0%)
4 RRBS 539524 8446 (1.6%) 38589 (7.2%) 492489 (91.3%)
5 RRBS 414147 8375 (2.0%) 17239 (4.2%) 388533 (93.8%)
6 RRBS 439844 7162 (1.6%) 23825 (5.4%) 408857 (93.0%)
7 MBD-BSSeq 746710 64572 (8.6%) 71046 (9.5%) 611092 (81.8%)
8 MBD-BSSeq 101087 42068 (41.6%) 13365 (13.2%) 45654 (45.2%)
9 MBD-BSSeq 794295 72530 (9.1%) 85117 (10.7%) 636648 (80.2%)

Table 10. Number and percent of CpGs that overlap with intergenic regions in P. acuta.

Sample Method CpG with Data Methylated CpG Sparsely Methylated CpG Unmethylated CpG
1 WGBS 3080835 3080835 (1.2%) 209781 (6.8%) 2834593 (92.0%)
2 WGBS 3598848 40642 (1.1%) 201712 (5.6%) 3356494 (93.3%)
3 WGBS 3280357 42507 (1.3%) 223666 (6.8%) 3014184 (91.9%)
4 RRBS 1060062 17473 (1.6%) 81473 (7.7%) 961116 (90.7%)
5 RRBS 844509 17581 (2.1%) 38900 (4.6%) 788028 (93.3%)
6 RRBS 878266 15232 (1.7%) 53637 (6.1%) 809397 (92.2%)
7 MBD-BSSeq 1387623 140057 (10.1%) 175668 (12.7%) 1071898 (77.2%)
8 MBD-BSSeq 319125 127376 (39.9%) 52215 (16.4%) 139534 (43.7%)
9 MBD-BSSeq 1451853 129949 (9.0%) 198940 (13.7%) 1122964 (77.3%)

Going forward

  1. Create figures for CpG characterization in various genome features
  2. Update code for methylation frequency distribution figure
  3. Generate repeat tracks for each species
  4. Find a way to generate tables programmatically
  5. Figure out how to meaningfully concatenate data for each method
  6. Figure out methylation island analysis

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