Tag Archives: methylation

DNA Methylation Quantification – Acropora cervicornis (Staghorn coral) DNA from Javier Casariego (FIU)

Used the MethylFlash Methylated DNA Quantification Kit (Colorimetric) from Epigentek to quantify methylation in these coral DNA samples.

All samples were run in duplicate <em>except</em> 2h Block 1 due to insufficient DNA.

The following samples were used in a 1:10 dilution (2uL DNA : 18uL NanoPure H2O), due to their relatively high concentrations, to ensure accurate pipetting:

  • 72h Block 4
  • D14 Block 1
  • D14 Block 2
  • D14 Block 3
  • D14 Block 4
  • D14 Block 5
  • D14 Block 6
  • D14 Block 8
  • D14 Block 10

All samples were diluted to a final concentration of 9.645ng/uL (154.24ng total; 17.6uL) in NanoPure water, which is equal to 77.12ng of DNA per assay replicate. These numbers were chosen based off of the sample with the lowest concentration.

The following samples were used in their entirety:

  • 2h Block 8
  • D35 Block 8

Calculations were added to the spreadsheet provided by Javier (Google Sheet): A.cervicornis_DNA_Extractions(May_2017).xlsx

The spreadsheet became overly complicated because I initially forgot to account for the need to run each sample in duplicate.

The kit reagent dilutions were as follows:

  • Diluted ME1: 52mL of ME1 + 464mL of <em>distilled</em> water
  • Diluted ME4: 10uL of ME4 + 10uL of TE Buffer (pH=8.0; made by me on 20130408).
  • Standard curve: Prepped per instruction manual, with double volumes for two plates.
  • Diluted ME5: 50uL/well x 152well = 7600uL; 7600uL/1000 = 7.6uL; 7.6uL ME5 + 7592.4uL Diluted ME1
  • Diluted ME6: 50uL/well x 152well = 7600uL; 7600uL/2000 = 3.8uL; 3.8uL ME6 + 7596.2uL Diluted ME1
  • Diluted ME7: 50uL/well x 152well = 7600uL; 7600uL/5000 = 1.52uL; 1.52uL ME7 + 7598.48uL Diluted ME1

All diluted solutions were stored on ice for duration of procedure.

The remaining Diluted ME1 solution was stored at 4C (FTR 209), and is stable for 6 months, per the manufacturer’s instructions.

See the Results section below for plate layouts.

Plates were read at 450nm on the Seeb Lab Victor 1420 Plate Reader (Perkin Elmer) and the amount of DNA methylation was determined.

Results:

Individual sample methylation quantification (Google Sheet): A.cervicornis_DNA_Extractions(May_2017).xlsx

Plate Reader Output File Plate #1 (Google Sheet): 20170511_coral_DNA_methylation_plate01.xls

Plate Reader Output File Plate #2 (Google Sheet): 20170511_coral_DNA_methylation_plate02.xls

 

I’m not familiar with the experimental design, so I’m not going to spend time handling any of the in-depth analysis at this point in time. However, here’s the background on how methylation quantification and percent methylation were determined.

  1. Mean absorbance (450nm) was determined for all samples and standard curve samples. It’s important to note that the standard deviation between replicates was not evaluated and there appears to be consistent variability between samples, but I’m not certain how much variation is “acceptable” with and assay of this nature.

  2. The mean absorbance of the standard curve samples were plotted against their corresponding DNA amounts and a linear trendline was fitted to the points.

  3. Per the manufacturer’s recommendations, the four points (including the zero point) that yielded the best linear fit (i.e. best R^2 value) were used and the slope of best fit line for those four points was determined.

  4. This slope was then utilized in the equation provided by the manufacturer (see pg. 8 of the MethylFlash Kit manual).

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DNA Methylation Quantification – Coral DNA from Jose M. Eirin-Lopez (Florida International University)

Ran the coral DNA I quantified on 20160630 through the MethylFlash Methylated DNA Quantification Kit [Colorimetric] (Epigentek) kit to quantify global methylation.

Used 100ng of DNA per 8uL per replicate (x2 replicates = total 200ng in 16uL). Calcs are here (Google Sheet): 20160705_coral_DNA_methylation_calcs

Manufacturer’s protocol was followed.

Dilutions of kit reagents:

ME5 (1:1000) 2.6uL ME5 + 2597.4uL diluted ME1

ME6 (1:2000) 1.3uL ME6 + 2598.7uL diluted ME1

ME7 (1:5000) 0.52uL ME7 + 2599.48uL diluted ME1

Samples were quantified on the Seeb’s plate reader @ 450nm  (Wallac 1420 Victor 2  [Perkin Elmer])

Results:

Google Sheet: 20160707_coral_DNA_methylflash

sample treatment 5-mC(ng)
H1_1 nitrogen 0.8712248853
H1_10 nitrogen 0.6917168368
H1_12 control 0.2738478893
H1_5 nitrogen & phosphorous 0.9663585942
H1_6 control 0.6494783825
H1_8 nitrogen & phosphorous 0.4244913398
H24_1 nitrogen 0.372603297
H24_10 nitrogen 0.4237237786
H24_12 control 0.5350511937
H24_5 nitrogen & phosphorous 0.1495527697
H24_6 control 0.2291900804
H24_8 nitrogen & phosphorous 0.2213437801
H5_1 nitrogen -0.1233169902
H5_10 nitrogen 0.6997668774
H5_12 control 0.2307000493
H5_5 nitrogen & phosphorous -0.07790933048
H5_6 control 0.4562401662
H5_8 nitrogen & phosphorous 0.5949647121

 

Overall, it’s difficult to really interpret these results. I believe the data is a time course (e.g. H5 = hour 5, H24 = hour 24). Additionally, looking at treatments, there appear to be replicates, but it’s not clear what type of replicates they are (i.e. technical or biological). Generally, it seems like the control samples have lower quantities of methylated DNA than the treated samples. However, this doesn’t hold true for all three of the groups.

And, not that it really matters, but I don’t even know what species this is…

In any case, this was an attempt to gather some preliminary data for a grant that Steven is attempting to put together, so the original experiment and the subsequent data aren’t as robust as one would expect for a full-blown research project.

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Epigenetic variation of two populations grown at common site

In a different experiment compared to when Fidalgo siblings were outplanted at two sites, we also examined Hood Canal (HC) and Oyster Bay (SS/South Sound) grown at Clam Bay (Manchester). Descriptor.

These were the oysters Katherine Silliman spawned in the summer of 2015 and represent seed Jake outplanted years ago.

This was run against the BGI scaffolds >10k.
BSMAP-06-BGIv002_1CC19CB1.png

The results are quite interesting.
RStudio_1CC19CEC.png

The full notebook can be found at https://github.com/sr320/nb-2016/blob/master/O_lurida/BSMAP-06-BGIv001.ipynb.

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Fidalgo offspring at two locations

We carried out whole genome BS-Seq on siblings outplanted out at two sites: Fidalgo Bay (home) and Oyster Bay. Four individuals from each locale were examined.

A running description of the data is available @ https://github.com/RobertsLab/project-olympia.oyster-genomic/wiki/Whole-genome-BSseq-December-2015.

I need to look back to a genome to analyze this. We did some PacBio sequencing a while ago.
– http://nbviewer.jupyter.org/github/sr320/ipython_nb/blob/master/OlyO_PacBio.ipynb

In recap, the fastq file had 47,475 reads: http://owl.fish.washington.edu/halfshell/OlyO_Pat_PacBio_1.fastq

3058 of these reads were >10k bp: http://eagle.fish.washington.edu/cnidarian/OlyO_Pat_PacBio_10k.fa

Those 3058 reads were nicely assembled into 553 contigs: http://eagle.fish.washington.edu/cnidarian/OlyO_Pat_PacBio_10k_contigs.fa


Step forward a bit and all 47475 reads were assembled into the 5362 contigs known as OlyO_Pat_v02.fa http://owl.fish.washington.edu/halfshell/OlyO_Pat_v02.fa

The latter (v02) was used to map the 8 libraries. Roughly getting about 8% mapping
BSMAP-03b-Genomev2-10x_1CB41B65.png

About 15 fold average coverage
BSMAP-03b-Genomev2-10x_1CB41B7A.png

And with a little filtering
BSMAP-03b-Genomev2-10x_1CB41B9E.png

Note that awk script filtered for 10x coverage! this could be altered.

and R have an intriguing relationship
BSMAP-03b-Genomev2-10x_1CB41BC9.png

With BGI Draft Genome

Following the same workflow with the BGIv1 scaffolds >10k bp have about 16% or reads map.
BSMAP-05-BGIv001_1CB41C8D.png

3 fold coverage
BSMAP-05-BGIv001_1CB41CB3.png

again, making sure there is 10x coverage at a given CG loci
we get
RStudio_1CB41F50.png

Much weaker if we allow only 3x coverage at a given CG loci
RStudio_1CB421EC.png

and the bit of R code

setwd("/Volumes/web-1/halfshell/working-directory/16-04-05")

library(methylKit)

file.list ‘mkfmt_2_CGATGT.txt’,
‘mkfmt_3_TTAGGC.txt’,
‘mkfmt_4_TGACCA.txt’,
‘mkfmt_5_ACAGTG.txt’,
‘mkfmt_6_GCCAAT.txt’,
‘mkfmt_7_CAGATC.txt’,
‘mkfmt_8_ACTTGA.txt’
)

myobj=read(file.list,sample.id=list(“1″,”2″,”3″,”4″,”5″,”6″,”7″,”8″),assembly=”Pat10k”,treatment=c(0,0,0,0,1,1,1,1))

meth<-unite(myobj)
head(meth)
nrow(meth)
getCorrelation(meth,plot=F)
hc PCA<-PCASamples(meth)

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Since you’ve been gone

Soon after Ensenada I went to Chili, SICB, and PAG (in that order). The new year is often of time to let go of lingering projects, and likely I will be doing that soon. But to bring a few pending efforts to the forefront, so that I can analyze etc here is a bit of data that is (or soon will) be coming in.
Much of this is centered around the Ostrea lurida.


The first batch was 2bRAD data.
sheet

The full list of samples are here.

sc

These raw data are here.

A quick fastqc….


We also now have a fresh set of MBD-BS.. now out for sequencing.
Pregame here

pic


And just some plain old BS
pic

Details

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Alternative splicing and germline methylation

One of the premises related to germline methylation in oysters is that genes with limited methylation will have more alternatively spliced products. One of our most robust identification of alternatively spliced genes is from the heat shock experiment, however methylation was assessed with array to ID DMRs. In order to gauge the relationship of germline methylation and alternative splicing I looked at all of our data and made the calls.


I simply looked at the 42 areas determined to be alternatively spliced in the three individuals and qualitatively made the call. For example..

alt-sp_1B66A0F3.png

Of the 42 genes, only 11 were determined have limited methylation.

scaffold1391:350297-393525 - High
scaffold1501:189-2280 -  Low-Medium
scaffold1546:22946-41272 - High
scaffold157:93056-102166 - High
scaffold157:288396-298950 - High 
scaffold1583:636568-663717 - High
scaffold1630:57333-61806 - Low
scaffold1643:190932-200778 - Low 
scaffold1670:360106-365501 - High
scaffold1750:71251-77856 - High
scaffold1009:677703-719650 - High
scaffold1009:990592-1008075 - High 
scaffold193:111771-117728 - High 
scaffold198:1032767-1055090 - Medium-High
scaffold198:1084454-1102022 - High
scaffold211:954418-990421 - Medium-High
scaffold351:641567-648889 - Low 
scaffold102:1297353-1322657 - Low-High
scaffold383:99975-117650 - Low-High
scaffold38366:25577-54928 - Low 
scaffold1024:1037898-1043721 - Medium-High
scaffold395:85069-105025 - High
scaffold399:120007-128313 - High
scaffold41228:55316-64219 - Low 
scaffold41858:126164-135361 - High
scaffold42366:124115-157800- High
scaffold42892:55315-57225 - Low 
scaffold42904:154963-177485 - High 
scaffold43208:19552-46201 - High 
scaffold43940:65971-85144 - High
scaffold452:65648-77493 - Low 
scaffold527:16020-64639 - Low 
scaffold588:178668-183438 - High
scaffold616:53220-63262 - Low 
scaffold828:110697-114691 - High
scaffold942:369690-377892 - High
scaffold1160:333463-390372 - Low-High
scaffold1213:118721-121110 - Low 
scaffold128:428337-438047 - Low 
scaffold1282:23471-48121 - Low 
scaffold13:4222-16204 - Low
scaffold1322:265134-304245 - High

The caveat is (there always seems to be one) is this list of alternative spliced genes is based on consistency across individuals, something we would predict NOT to see if this was truly a stochastic phenomenon.

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First steps at an aggregated view of all DNA methylation data (updated)

Seems like I have gotten close (see here) but do not have a canonical IGV session that has all of our DNA methylation data. The goal here is to generate such a product (and publish, so I do not lose it).

All data is publicly available at

http://owl.fish.washington.edu/halfshell/index.php?dir=2015-05-comgenbro

see also data on Figshare


Updates

July 2, 2015 – added Heat Shock experiment alternative splice track
June 26, 2015 – add link to Figshare version
June 26, 2015 – updated Archive.zip
June 26, 2015 – added numerous array tracks from heat stress array experiment including 3+ tracks.
June 26, 2015 – added new track from heat stress – Heat-multi-individual-dmr.bed
June 22, 2015 – updated Archive.zip
June 22, 2015 – updated MBD-seq track gills (no bisulfite treatment) to use unique mapping (see also [this](MBD-seq track gills (no bisulfite treatment))
June 22, 2015 – Updated EE2 linkout to go to Github
June 22, 2015 – Corrected error in labelling EE2 experiment tracks
June 15, 2015 – added MBD-seq track gills (no bisulfite treatment)
June 15, 2015 – added larval pesticide treatment tracks (bisulfite treatment)
June 15, 2015 – new IGV screenshot
June 15, 2015 – added HS-Cuffdiff_geneexp.sig.gtf (differentially expressed genes from heat-shock)

 

 


Metadata

FileID Description Links
Crassostrea_gigas.GCA_000297895.1.26.gtf gtf ftp
MBD-Gill-meth MBD enriched DNA library alignment paper, info
BiGill_CpG_methylation gill methylation 5x (MBD-BS, hi output) paper
BiGill_exon_clc_rpkm Corresponding exon-specific gene expression paper
BiGo_CpG_methylation male gamete methylation 5x (hi output) paper
M1 male gamete methylation 5x preprint
M3 male gamete methylation 5x preprint
T1D3 72hpf larvae from M1 methylation 5x preprint
T1D5 120hpf larvae from M1 methylation 5x preprint
T3D3 72hpf larvae from M3 methylation 5x preprint
T3D5 120hpf larvae from M3 methylation 5x preprint
Heat-multi-individual-dmr.bed Heat Stress (13 locations) common signal notebook
2M_3plusmerge_Hyper.bed merging adj probes to single interval notebook
2M_3plusmerge_Hypo.bed merging adj probes to single interval notebook
4M_3plusmerge_Hyper.bed merging adj probes to single interval notebook
4M_3plusmerge_Hypo.bed merging adj probes to single interval notebook
6M_3plusmerge_Hyper.bed merging adj probes to single interval notebook
6M_3plusmerge_Hypo.bed merging adj probes to single interval notebook
2M_Hyper_3plusAdjactentProbes.gff 3+ adjacent probes notebook
2M_Hypo_3plusAdjactentProbes.gff 3+ adjacent probes notebook
4M_Hyper_3plusAdjactentProbes.gff 3+ adjacent probes notebook
4M_Hypo_3plusAdjactentProbes.gff 3+ adjacent probes notebook
6M_Hyper_3plusAdjactentProbes.gff 3+ adjacent probes notebook
6M_Hypo_3plusAdjactentProbes.gff 3+ adjacent probes notebook
2M_sig Heat stress DMRs (array), ind.#2 notebook, draft
4M_sig Heat stress DMRs (array), ind.#4 notebook, draft
6M_sig Heat stress DMRs (array), ind.#6 notebook, draft
HS-Cuffdiff_geneexp.sig.gtf Heat stress differentially expressed genes notebook
HS-Cuffdiff_altsplice.bed Heat stress alternatively spliced genes notebook
2M.bedgraph.tdf RNA-seq from ind.#2 above – pretreament notebook, draft
4M.bedgraph.tdf RNA-seq from ind.#4 above – pretreament notebook, draft
6M.bedgraph.tdf RNA-seq from ind.#6 above – pretreament notebook, draft
2M-HS.bedgraph.tdf RNA-seq from ind.#2 above – post-heatshock notebook, draft
4M-HS.bedgraph.tdf RNA-seq from ind.#4 above – post-heatshock notebook, draft
6M-HS.bedgraph.tdf RNA-seq from ind.#6 above – post-heatshock notebook, draft
mgaveryDMRs_112212.gff EE2 exposure DMRs (array) paper
A01.smoothed EE2 exposure array data – input versus input paper
A02.smoothed EE2 exposure array data – EE2 vs control paper
A03.smoothed EE2 exposure array data – EE2 vs control (dyeswap) paper
YE_mixHYPER.bed DMRs in pesticide exposed larvae (hypermethylated)
YE_mixHYPO.bed DMRs in pesticide exposed larvae (hypomethylated)
YE_mix_22smCG3x larvae (mix pesticide exposed) methylation
YE_control_22smCG3x larvae (control) methylation

screenshot

anyone should be able to render this in IGV with this session file:

http://owl.fish.washington.edu/halfshell/2015-05-comgenbro/igv_session.xml


This work was supported in part by the National Science Foundation (NSF) under Grant Number 1158119 awarded to SR Roberts

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MeDIP – SB/WB Fragmented gDNA EtOH precipitation (continued from 20100702)

Finished EtOH precipitation of MeDIP gDNA. Samples were pelleted 16,000g, 4C, 30mins. Supe was discarded. Washed with 1mL 70% EtOH, pelleted 16,000g, 4C, 15mins. Supe discarded. MeDIP DNA was resuspended in 100uL of TE (pH = 8.5). Wash samples, containing unmethylated DNA, were resuspended/combined in a total of 100uL TE (pH = 8.5). Samples were spec’d:

Results:

R37: MeDIP DNA = 1.393ug recovery. This is ~13% of the input total gDNA (11.25ug) and is ~28% of the total DNA recovered in the procedure (4.935ug). Unmethylated DNA = 3.542ug total recovery. This is ~31% of the input total gDNA (11.25ug) and is ~72% of the total DNA recovered in the procedure (4.935ug). Total DNA recovery = ~44%.

R51: MeDIP DNA = 1.256ug recovery. This is ~14% of the input total gDNA (8.75ug) and is ~23% of the total DNA recovered in the procedure (5.462ug). Unmethylated DNA = 4.206ug total recovery. This is ~48% of the input total gDNA (8.75ug) and is ~77% of the total DNA recovered in the procedure (5.462ug). Total DNA recovery = ~62%.

There definitely seemed to be a high degree of salt carryover from the procedure, despite the phenol:chloroform treatment and EtOH precipitation. As such, I believe this is the reason that the 260/230 ratios are so out of whack. Possibly explains why the 260/280 ratios for the MeDIP DNA are so high, too?

These results demonstrate what we can expect to recover from this procedure, as well as how much DNA gets lost during processing. MeDIP DNA and unmethylated DNA were stored @ -20C.

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DNA Methylation Test – Gigas site gDNA (BB & DH) from 20090515

Used BB & DH samples #11-17 for procedure. Followed Epigentek’s protocol. My calcs for dilutions/solutions needed are here. All solutions were made fresh before using, except for Diluted GU1 which was made at the beginning of the procedure and stored on ice in a 50mL conical wrapped in aluminum foil. Used 100ng total (50ng/uL) of each sample gDNA. No standards for a standard curve based on speaking with Mac.

WELL SAMPLE WELL SAMPLE
A01 BB11 A02 DH11
B01 BB12 B02 DH12
C01 BB13 C02 DH13
D01 BB14 D02 DH14
E01 BB15 E02 DH15
F01 BB16 F02 DH16
G01 BB17 G02 DH17
H01 Pos. Control H02 Blank

Results: Above is the graph of the results. Although it’s only a small difference between the two sites, it is statistically significant. The calcs for this graph can be found here (Excel file). It should be noted that this graph was generated using estimated values from the standard curve provided in the manufacturer’s protocol. This was done because 1) I did not run standards to generate my own curve and 2) calculating the “% methylation” not using the formula that utilizes the standard curve was giving ridiculously high values (e.g. 350%).

Here is the raw data generated by the plate reader for a 1s read (Excel file) and a 0.1s (Excel file) read. Both reads have nearly identical values.

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