The document describes the agenda for a hands-on training workshop on methylation sequencing analysis. The training will cover topics such as WGBS dataset quality control, mapping, and basic analysis using tools like methpipe, BEDtools, and the UCSC Genome Browser. The first session in the morning focuses on WGBS dataset QC, mapping with methpipe, and calculating metrics like bisulfite conversion rate and methylation levels in different genomic regions. The second session in the afternoon involves more advanced analysis using BEDtools to analyze methylation differences between genomic regions and between male and female samples, as well as using the UCSC Genome Browser.

1. Hands-On Training in Methylation Sequencing Analysis
Time Topic Speaker
09:00~10:30 WGBS Dataset QC & Mapping 張益峰 博士
l 測試資料簡介（Human IMR90 以及 H1 的
WGBS Dataset）
l 執行 short read QC
l meth-pipe 簡介（網站、下載、安裝）
l 執行 meth-pipe 進行 short read mapping 和
mapped raw data 處理
l 計算 bisulfide conversion rate
l 計算 HMR / DMR / PMD
l 產生 BED 格式的檔案
10:45~11:00 Q & A / 休息
11:00~12:30
Basic WGBS Analysis and UCSC Genome
Browser
林依璿 博士
l BEDtools 的簡介
l BEDtools 實做（一）：HMR / DMR /
PMD 在Chromosome X 上的分佈
l BEDtools 實做（二）：CpG Island 的 DNA
methylation 計算
l BEDtools 實做（三）：比較 male (H1) &
female (IMR90) 在 Chromosome X 上的
methylation 差異
l 用 R 以及 ggplot2 package 呈現以上的
methylation 差異
l UCSC Genome Browser 介紹和使用操作
l UCSC Genome Browser 裡面的 MethBase
Public TrackHub 介紹和使用操作
12:30~13:00 Q & A / 午餐
This slide is available in http://www.slideshare.net/YiFengChang
1

2. Required Software in Your Laptop
• Linux console
• Putty:
http://the.earth.li/~sgtatham/putty/lat
est/x86/putty.exe
• SCP/SFTP/FTP client
• Winscp:
http://winscp.net/download/winscp55
6.zip
• PDF viewer
• http://get.adobe.com/tw/reader/
2
http://120.126.44.231/ycl6/20141219_Ian.pptx
http://120.126.44.231/ycl6/20141219_NRPB2014.pdf

3. NCHC ALPS1 for NRPB Users• Login node: alps1.nchc.org.tw
• Computing nodes
• 48 (we can use 4) x 48 cores 128GB RAM
• 1 x 64 cores 1TB RAM
• Storages
• Users home: 200GB (Temp Account: 1GB)
• /work3: 42TB
• /work5: 200TB
This slide is available in http://www.slideshare.net/YiFengChang
Queue Name Test[註一] 4G 16G 48G 128G 192core 384core 1T
使用記憶體上限 (GB) 2 4 16 48 100 100/48core 100/48core 1024
使用核心數上限 1 2 8 24 48 192 384 64
建議核心使用上限 1 1 6 20 40 192 384 60
工作優先權[註二] 90 85 80 50 30 20 20 10
什麼是 queue?
Queue 就是針對工作所設立的虛擬的運算單元，一個 queue 可以負擔一個運算
工作。如果一臺機器被指定兩個 queue，意味這台機器可能同時運行兩個工作。
Queue 的設計是用來管理計算資源 。
•所有的工作必須透過 queuing system 執行
•目前提供Test, 4G, 16G, 48G, 128G,192core,384core, 1T 共有 五 種 queue
•工作優先順序以Test> 4G>16G>48G>128G>192core>384core> 1T
•記憶體使用大於上限者，將會被中斷運算
•可先使用1T queue試運算，決定記憶體用量後再選擇使用何種 queue
•使用queue時的限制與規則如下:
註一 ，test queue一人只能送一個job，執行時間為10分鐘
註二 ，數字愈大優先權愈高
網中心提供之Queuing system
國網中心提供 IBM Load Sharing Facility (LSF)
目前可以使用的 Queue
3

4. Submit a Job
s00yao25@alps1:~> cd
s00yao25@alps1:~> bsub -q 4G -o stdout -e stderr "ls"
Job <422673> is submitted to queue <4G>.
s00yao25@alps1:~> cat stdout
s00yao25@alps1:~> bjobs
JOBID USER STAT QUEUE FROM_HOST EXEC_HOST JOB_NAME SUBMIT_TIME
422673 s00yao2 RUN 4G alps1 2*alps1-25 ls Dec 18 23:16
s00yao25@alps1:~> bjobs
No unfinished job found
4

5. Check Job Status
5

6. Kill a Job
>bkill JOBID
# chang queue priority of pending jobs
> btop
6

7. Effect and Problems of Bisulfite
Treatment of DNA
7
Krueger, F., Kreck, B., Franke, A. & Andrews, S.R. DNA methylome analysis using short bisulfite sequencing data. Nat
Methods 9, 145-51 (2012).
Mapping bisulfite reads to 4 possible bisulfite strands (OT/CTOT/OB/CTOB) is
equivalent to mapping the bisulfite read and its reverse complementary
read to both Top/Bottom strands of the original reference sequence.

8. How to Align BS Reads Against Reference Genome?
8
Krueger, F. & Andrews, S.R. Bismark: A flexible aligner and methylation caller for Bisulfite-Seq
applications. Bioinformatics (2011).
Bock, C. Analysing and interpreting DNA
methylation data. Nat Rev Genet 13, 705-19 (2012)
Y=C or T
TCGA TCGT ACGT ATGA
Multiple hits
TTGT ATGT
Multiple hits
TCGA ATGA

9. Why RMAPBS/RMAPBS-PE
9
http://smithlabresearch.org/manuals/rmap_manual.pdf

10. Analysis Pipeline
10
Allele-specific Methylated Regions
amrfinder allelicmeth
Differential Methylation Region
dmr
Large Hypo/Hyper-Methylation Domains
pmd
Hypo/Hyper-Methylation Regions
hmr hyperhmr pmr
Methylation Calling
methcounts + error correction
Bisulfite Conversion Rate
bsrate
Remove Duplicate Reads
duplicate-remover
Mapping
rmapbs rmapbs-pe
Quality Trimming
fastq_masker
Cross-species Comparison of Methylomes
liftOver
Calculating Methylation Ratio for Regions
bigWigAverageOverBed roimethstat Bwtools
Generate Methylation BED file
Bedtools bedGraphToBigWig
fastx toolkit: http://hannonlab.cshl.edu/fastx_toolkit/
MethPipe: http://smithlabresearch.org/software/methpipe/
Bedtools: https://github.com/arq5x/bedtools2
Programs from UCSC Genome Browser:
http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64
bwtool: https://github.com/CRG-Barcelona/bwtool/wiki

11. H1 (male): human embryonic stem cells (107GB)
IMR90 (female): fetal lung fibroblasts (154GB)
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16256
11

12. Convert SRA to Fastq (DO NOT RUN)
sra-toolkit: https://github.com/ncbi/sratoolkit
> fastq-dump --split-3 SRR018975.sra
> ls
SRR018975.fastq
12

13. Quality Trimming (DO NOT RUN)
#e.g. SRR018975.fastq.gz
> for f in *.gz; # read all gzip files one by one
do
b=`basename $f .gz`; # SRR018975.fastq
echo $f
bsub -q 4G -o $f.stdout -e $f.stderr "
gzip -dc $f| # read gzip file
fastq_masker -q 30 -Q33| # mask low quality reads as Ns
split -dl 6000000 - $b- "; # split fastq file into smaller ones
done
> ls
SRR018975.fastq-00
SRR018975.fastq-01
SRR018975.fastq-02
…
13

14. Mapping
bsub -q 4G -o rmapbs.stdout -e rmapbs.stderr "
/work3/NRPB1219/bin/rmapbs-pe
-c /work3/NRPB1219/methpipe-data/data/genome
-o /home/s00yao00/Output/test.mr
-m 3 -L 400 -C AGATCGGAAGAGC:GCTCTTCCGATCT
/work3/NRPB1219/methpipe-data/data/snippet_1.fq
/work3/NRPB1219/methpipe-data/data/snippet_2.fq"
#head /home/s00yao00/Output/test.mr
chr22 379487 379588 FREDDYKRUEGER_0001:1:1:160:969#0/2 2 +
AAGTAATATATATGTTTTGGGATTAGTGAATTTAAGTTAGTATTAAGAATTTTATTATTATTTTTTTATTATATTTTAGAGAGTTATTTTTTTATTTTTAA
B_c^cbbPfbfbLc]][[Xcbcc`cbd`bbcbdZUdbfOffffdffdSSTTQbZbbb`df[bbbbbdffYffffffbbbZbZX^^afffffcff
chr22 379487 379588 FREDDYKRUEGER_0001:1:1:160:969#0/1 3 +
AAGTAATATTTATGTTTTGGGATTAGTGAATTTAAGTTAGTATTATGAATTTTATTATTATTTTTTTATTATATTTTAGAGAGTTATTTTTTTCTTTTTAA
`dd``addddbcUcccdcfffaf_ZdddOVM_[bOZdb`dbffZ`ObObffaeff^ffdfffffffaffdWf_bdebdaU[^[ffa_f_b`OUK^`UZT
chr22 568970 569071 FREDDYKRUEGER_0001:1:1:249:1215#0/2 0 -
TTTTATTTGATGGATAATATTAAGAAATTTGTAGTATTGTTTTGGAATTTTTTGTGAGGGATAAATAAATAGAATATAGTAGTATTGTTTTATAATTTTTT
BcXe^cecccgbabfadadcf^cgggegTggefgeggggggggggggggggggggggggegaggg^gggbggggggggggggegggggggggfggfcdggg
chr22 310957 311058 FREDDYKRUEGER_0001:1:1:303:856#0/2 4 +
TTGTAATTATGTTGATTTTATGTGTAGTTATTGATGTTGTTGTATGGTAGTTTATGGTTTTTTAGGAATTTAGAATTTGAGTTTTATTTTTGTTTTATAGT
Z]]`W[J`]fdPcefbf^fgggggWcggaeaedSSOQTcggdgcgggcdgffdeaccaddcadfbacfaffcaaecadggbdggggcgcgggggccg
chr22 568970 569071 FREDDYKRUEGER_0001:1:1:249:1215#0/1 2 +
AAAGGATTGTAGAATAGTGTTATTGTGTTTTGTTTGTTTGTTTTTTATAAAGGATTTTAGAGTAGTGTTGTAGATTTTTTAGTGTTGTTTATTAGATAGGT
ggagcfggdgggbegffgfgggggggggfgggggggggggggfggggfcf^d^Pfggggebafbbgfgfgge^^ggggggccggfegefggfggeP^ZW[
chr22 581983 582084 FREDDYKRUEGER_0001:1:1:359:1280#0/2 2 +
GAGGTAATTTAGAGTGTTGTTTTTGGTTTTTGAGGGTTTGTTTTTTAAATAGGATATTATTATATTGTTACGATAGTTTGAATGTTTGTTCGTGATAAATG
Bffeeegg_fgeegggeeeeagcggggfgaggggggggggggggggggggfgffdeggggggfgfgggffgggffgggggfggfgggggbgbggggggcf
chr22 310918 311019 FREDDYKRUEGER_0001:1:1:303:856#0/1 0 +
TATATAGAGTTAGGTTTTATAGTTTATTTTTTTATTATTTTGTAATTTTGTTAATTTTATGTTTAGTTATTGATGTTTTTGTATGGTAGTTTATGGTTTTT
faf_f_ggeggcg^ggggbgdggggcgggggggdggdggggggg^ggggggdg[ggggdggcgdccggagggNggcdgggcbYddTcKRcde[ddYgcggg
chr22 581975 582076 FREDDYKRUEGER_0001:1:1:359:1280#0/1 1 +
TATTTTATAAGGTAATTTAGAGTGTTGTTTTTGGTTTTTGAGGGTTTGTTTTTTAAATAGGATATTATTATATTGTTACGATAGTTTGAATGTTTGTTCGT
hggggfggghggegggggcg]gggggggggggggggggggefgeeggggggfggggfgggceggggggegggggggfeaeaggdgggWgegegecgegfg
chr22 578161 578262 FREDDYKRUEGER_0001:1:1:871:393#0/2 3 +
TTAGAATAGTGTTGTTTGTATTCGAGTGTTTGTTTTTTATATAGGATTATAGAATATTTTTACGAGTGTTCGAATGTTTGTTTTTTAGATAGGATTTTGGA
^ac]eL]]R_JeeeLegggVbggeeeeVeefceegaggggggggefgdgggggfggggdgggggggggggggggggggegegggggggfgggggggggg
chr22 578102 578203 FREDDYKRUEGER_0001:1:1:871:393#0/1 0 +
AAGAAAGGATTTTAGAATAGTGTTGTTGTGTTTTGAGTGTTTGTTATTTTTGAATGATTTTAGATTATTGTTGTTGGTATTCGAGTGTTTGTTTTTTATAT
cce^aVeaaecdeefOadfegcgg_gdggagggghgggggdggfbgggggbg_fgWgeggWgLgggggeggcgdePa`fffafOe`_egggdg_g_gae
14
http://120.126.44.231/ycl6/20141219_Ian.pptx
http://120.126.44.231/ycl6/20141219_NRPB2014.pdf

15. Sorting mr file
bsub -q 16G -o stdout -e stderr "
LC_ALL=C sort -S 14G -k 1,1 -k 2,2n -k 3,3n -k 6,6
-o /work3/s00yao25/h1.chrX.mr.sorted_start
/work3/NRPB1219/h1.chrX.mr"
15

16. Remove Duplicates
export PATH=$PATH:/pkg/biology/methpipe/methpipe-3.3.1/bin/
bsub -q 16G -o stdout -e stderr "
duplicate-remover -S /work3/s00yao25/h1.chrX_dremove_stat.txt
-o /work3/s00yao25/h1.chrX.mr.dremove
/work3/s00yao25/h1.chrX.mr.sorted_start "
Successfully completed.
Resource usage summary:
CPU time : 167.80 sec.
Max Processes : 3
Max Threads : 4
16
TOTAL READS IN: 24350707
GOOD BASES IN: 1987943796
TOTAL READS OUT: 22884736
GOOD BASES OUT: 1867152730
DUPLICATES REMOVED: 1465971
READS WITH DUPLICATES: 1219174

17. Estimating bisulfite conversion rate
bsub -q 16G -o stdout -e stderr "
bsrate -c /work3/NRPB1219/hg18
-o /home/s00yao25/Output/h1.chrX.bsrate
/work3/s00yao25/h1.chrX.mr.dremove"
17
# head –n 16 /home/s00yao25/Output/h1.chrX.bsrate
OVERALL CONVERSION RATE = 0.980192
POS CONVERSION RATE = 0.980204 96942555
NEG CONVERSION RATE = 0.980179 96821402
BASE PTOT PCONV PRATE NTOT NCONV NRATE BTHTOT BTHCONV BTHRATE ERR ALL ERRRATE
1 1798190 1762518 0.98016 1796291 1760655 0.98016 3594481 3523173 0.98016 36327 3630808 0.01001
2 1654252 1617801 0.97797 1649805 1613025 0.97771 3304057 3230826 0.97784 41299 3345356 0.01235
3 1646403 1615036 0.98095 1644710 1613525 0.98104 3291113 3228561 0.98099 48231 3339344 0.01444
4 1699787 1666286 0.98029 1695105 1662078 0.98052 3394892 3328364 0.98040 50697 3445589 0.01471
5 1663363 1631006 0.98055 1658397 1626045 0.98049 3321760 3257051 0.98052 52464 3374224 0.01555
6 1720978 1687130 0.98033 1716036 1682351 0.98037 3437014 3369481 0.98035 45366 3482380 0.01303
7 1677561 1644979 0.98058 1677119 1644343 0.98046 3354680 3289322 0.98052 53873 3408553 0.01581
8 1714426 1681206 0.98062 1714378 1681339 0.98073 3428804 3362545 0.98068 34491 3463295 0.00996
9 1702891 1668424 0.97976 1700092 1665742 0.97980 3402983 3334166 0.97978 34861 3437844 0.01014
10 1681522 1648092 0.98012 1680471 1647068 0.98012 3361993 3295160 0.98012 45776 3407769 0.01343
11 1664207 1631036 0.98007 1664386 1631083 0.97999 3328593 3262119 0.98003 46055 3374648 0.01365
12 1651326 1618334 0.98002 1649370 1616514 0.98008 3300696 3234848 0.98005 44139 3344835 0.01320

18. Computing single-site methylation levels
# sorting… again
bsub -q 16G -o stdout -e stderr "
LC_ALL=C sort -S 14G -k 1,1 -k 3,3n -k 2,2n -k 6,6
-o /work3/s00yao25/h1.chrX.mr.sorted_end_first
/work3/s00yao25/h1.chrX.mr.dremove"
# methylation calling
bsub -q 16G -o stdout -e stderr "
methcounts -c /work3/NRPB1219/hg18
-o /work3/s00yao25/h1.chrX.meth
/work3/s00yao25/h1.chrX.mr.sorted_end_first"
#extract CpG sites
bsub -q 16G -o stdout -e stderr "
symmetric-cpgs
-o /work3/s00yao25/h1.chrX_CpG.meth h1.chrX.meth"
18
chrX 0 + CHH 0 0
chrX 4 + CHH 0 0
chrX 5 + CHH 0 0
chrX 6 + CHH 0 0
chrX 10 + CHH 0 0
chrX 11 + CHH 0 0
chrX 12 + CHH 0 0
chrX 16 + CHH 0 0
chrX 17 + CHH 0 0
chrX 18 + CHH 0 0
chrX 152 + CpG 0 0
chrX 232 + CpG 0 0
chrX 330 + CpG 0 0
chrX 334 + CpG 0 0
chrX 336 + CpG 0 0
chrX 364 + CpG 0 0
chrX 366 + CpG 0 0
chrX 374 + CpG 0 0
chrX 376 + CpG 0 0
meth ratio read count

19. Computation of methylation level statistics
bsub -q 16G -o stdout -e stderr "
levels -o /home/s00yao25/Output/h1.chrX.levels
/work3/s00yao25/h1.chrX.meth"
19

20. Hypomethylated (hmr), hypermethylated (hypermr), and
partial methylated (pmr) regions
bsub -q 16G -o stdout -e stderr "
hmr -o /work3/s00yao25/h1.chrX.hmr /work3/s00yao25/h1.chrX_CpG.meth"
bsub -q 16G -o stdout -e stderr "
hmr -partial -o /work3/s00yao25/h1.chrX.pmr /work3/s00yao25/h1.chrX_CpG.meth"
bsub -q 16G -o stdout -e stderr "
pmd -o /work3/s00yao25/h1.chrX.pmd /work3/s00yao25/h1.chrX_CpG.meth"
20
chrX 2727656 2728600 HYPO0 18 +
chrX 2731108 2731952 HYPO1 14 +
chrX 2732390 2733303 HYPO2 23 +
chrX 2740632 2740962 HYPO3 9 +
chrX 2756524 2758153 HYPO4 139 +
chrX 2817685 2817980 HYPO5 8 +
chrX 2855757 2857708 HYPO6 127 +
chrX 2890571 2890884 HYPO7 9 +
chrX 3004371 3004626 HYPO8 9 +
chrX 3238227 3238677 HYPO9 9 +
# of CpG

21. Differential Methylation Analysis
bsub -q 16G -o stdout -e stderr "
methdiff -o /work3/s00yao25/h1.imr90.chrX.methdiff
/work3/NRPB1219/h1.chrX_CpG.meth /work3/NRPB1219/imr90.chrX_CpG.meth"
21
chrX 2709681 + CpG 0.749276 7 2 12 7
chrX 2709727 + CpG 0.917633 4 1 9 12
chrX 2709774 + CpG 0.894737 3 1 6 10
chrX 2709871 + CpG 0.742424 0 16 0 48
chrX 2709890 + CpG 0.857575 3 20 3 47
chrX 2709982 + CpG 0.999354 10 2 7 19
chrX 2710014 + CpG 0.704043 3 6 3 10
chrX 2710023 + CpG 0.600782 4 3 4 4
chrX 2710146 + CpG 0.523077 1 2 8 14
chrX 2710155 + CpG 0.234026 3 3 17 9
Probability
Sample A
Un-meth
Sample A
Meth
Sample B
Un-meth
Sample B
Meth

22. Differential methylated region
(DMR)
bsub -q 16G -o stdout -e stderr "
dmr /work3/s00yao25/h1.imr90.chrX.methdiff /work3/NRPB1219/h1.chrX.hmr
/work3/NRPB1219/imr90.chrX.hmr DMR_h1_lt_imr90 DMR_imr90_lt_h1"
22
==> DMR_h1_lt_imr90 <==
chrX 2727656 2728600 X:18 10 +
chrX 2731108 2731952 X:15 4 +
chrX 2732390 2733303 X:37 8 +
chrX 2740632 2740962 X:9 0 +
chrX 2758131 2758153 X:3 0 +
chrX 2817685 2817980 X:9 0 +
chrX 2855757 2855890 X:1 1 +
chrX 2890571 2890884 X:9 4 +
chrX 3004371 3004626 X:9 0 +
chrX 3238227 3238677 X:24 0 +
==> DMR_imr90_lt_h1 <==
chrX 2825454 2826947 X:37 17 +
chrX 2857708 2857760 X:2 0 +
chrX 3272822 3273033 X:13 3 +
chrX 3275527 3275594 X:1 0 +
chrX 3287038 3289160 X:36 9 +
chrX 3643168 3643374 X:7 0 +
chrX 4016033 4022054 X:47 29 +
chrX 4028369 4042000 X:79 54 +
chrX 4051286 4059878 X:52 39 +
chrX 4079778 4087714 X:45 26 +
Number of significant differential methylated CpG
Meth. level lower in H1 than IMR90 Meth. level lower in IMR90 than H1