Shelly’s Notebook: Wed. Nov. 6, Geoduck DMR Summary and Feature Analysis

Summary of DMRs

  • Within-sample DMRs were called (mox script here: 20191024_DMRfindAllEPI.sh; DMR output files here: https://gannet.fish.washington.edu/metacarcinus/Pgenerosa/analyses/20191024/, see files ending in _DMR250bp_MCmax30_cov5x_rms_results_collapsed.tsv) and the total number of DMRs called for each of the following comparisons were:
    • All ambient samples over time: 249
    • All day 10 samples: 182
    • All day 135 samples: 109
    • All day 145 samples: 213
  • Filtered DMRs: Within-sample DMRs were filtered for those that show coverage in at least 3/4 individuals per experimental group (R script here: mcmax30_DMR_cov_in_0.75_SamplesPerCategory.R, Rproj here: DMR_cov_in_0.75_SamplesPerCategory.Rproj). After these steps the total number of DMRs that went into the ANOVA test for experimental group differences for each of the following comparisons were:
    • All ambient samples over time: 82
    • All day 10 samples: 99
    • All day 135 samples: 42
    • All day 145 samples: 26
  • Significant DMRs After running group statistics (post here: https://shellytrigg.github.io/209th-post/), the fraction of DMRs with an experimental group effect significant at a 1way ANOVA p.value of < 0.1 for each of the following comparisons were:
    • 38/82
    • 29/99
    • 13/42
    • 5/26

DMR Summary Table:

| comparison | num.exp.groups | num.within.sample.DMRs | num.DMRs.filtered.0.75X.indv.per.group | num.DMRs.AOV.sig.at0.1 | num.DMRs.AOV.sig.at0.05 | fraction.filtered.DMRs | fraction.total.DMR.sig.at0.1 | fraction.filtered.DMR.sig.at0.1 | fraction.total.DMR.sig.at0.05 | fraction.filtered.DMR.sig.at0.05 | |———————|—————-|————————|—————————————-|————————|————————-|————————|——————————|———————————|——————————-|———————————-| | all ambient samples | 4 | 249 | 82 | 38 | 33 | 0.33 | 0.15 | 0.46 | 0.13 | 0.40 | | all day 10 samples | 3 | 182 | 99 | 29 | 14 | 0.54 | 0.16 | 0.29 | 0.08 | 0.14 | | all day 135 samples | 3 | 109 | 42 | 13 | 9 | 0.39 | 0.12 | 0.31 | 0.08 | 0.21 | | all day 145 samples | 6 | 213 | 26 | 5 | 3 | 0.12 | 0.02 | 0.19 | 0.01 | 0.12 |

  • no DMR was significant at FDR corrected p.value < 0.05
  • 5 DMRs from all-ambient samples and 1 DMR from Day 10 samples were significant at FDR corrected p.value of < 0.1

Feature analysis including non-significant DMRs

My previous post looked into the number of different genomic features that significant DMRs overlap with. This analysis looks at the number of different genomic features that all DMRs (significant + non-significant) overlap with and compares to that of significant DMRs.

  1. I matched filtered DMRs to the most recent GFF Panopea-generosa-vv0.74.a4-merged-2019-10-07-4-46-46.gff3 using this jupyter notebook: 20191106_functional_analysis.ipynb and created files with genome feature annotations in this directory: 20191106_anno
  2. I added on the Rmarkdown script I previously started 20191102_anno.Rmd to plot the filtered DMRs (“All DMRs”, left side) next to the ANOVA significant DMRs (“significant DMRs”, right side). Each sample comparison (all ambient samples, day10 samples, day135 samples, and day145 samples) is plotted row-wise.

AllvSigDMRsxfeatures_PropBarplot_GroupFacet.jpg

  • There are no obvious differences between significant DMRs and all DMRs in the proportion of features they overlap with, except for the Day145 comparison where CDS and exon features are under-represented, and mRNA and repeat_region are over-represented.

Next steps

  • Look deeper into repeat regions (there are ~6 different catagories, so can see if any are particularly different)
  • For DMRs no in features, check the nearest features
  • Compare genes with DMRs to the ones identified by Hollie’s method
  • Continue GO analysis
    • generate appropriate GO background to use for each comparison
    • run TopGO
  • Go through manuscript methods section
    • update new stuff
    • add comments to areas of concern

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Grace’s Notebook: Bairdi RNA – Bioanalyzer and Nanodrop, Begin analyses for GSS – Kallisto

Today I tried doing several things, none of them super successfully. First, I tried using Kallisto to get some quick differential expression results from the new bairdi RNA-seq data. It didn’t work, but I just need to look a little more into it. I didn’t spend too much time on it. Secondly, I tried running 11 of the extracted RNA samples on the Bioanalyzer – things do not look good. Thirdly, I ran those same samples on the NanoDop. I am not sure what to make of the Nanodop results yet.

Kallisto

I made this GitHub Issue after meeting with Steven yesterday: #790

I tried using Kallisto. I downloaded it from here: https://ift.tt/2CmxB0C
I simply clicked the Mac link next to the most resent release date (10/4/2019).

I then tried using it in a jupyter noteboook: 2019-11-06-bairdi-kallisto.ipynb

Not entirely sure what there error message means, but I haven’t actually spent much time on it yet. Will look into tonight/tomorrow.

Bioanalyzer

These are the RNA samples I’ve chosen to create the 6 pooled transcriptomes:

FRP trtmnt_tank sample_day infection_status maturity tube_number total-yield_ng
6157 NA 9 0 M 169 603.2
6143 NA 9 0 M 10 380.9
6228 NA 9 0 I 29 335.4
6150 NA 9 0 M 119 323.7
6277 NA 9 0 I 177 296.4
6156 NA 9 0 I 24 275.6
6272 NA 9 0 I 111 260
6178 NA 9 0 M 124 215.8
6161 NA 9 0 M 53 201.5
6136 NA 9 0 M 91 192.4
6137 NA 9 1 I 81 403
6174 NA 9 1 I 20 360.1
6177 NA 9 1 I 54 327.6
6122 NA 9 1 I 133 305.5
6196 NA 9 1 I 170 300.3
6233 NA 9 1 I 117 300.3
6187 NA 9 1 I 158 241.8
6199 NA 9 1 I 151 241.8
6164 NA 9 1 I 71 236.6
6237 NA 9 1 I 17 226.2
6104 cold 12 0 I 259 412.1
6106 cold 12 0 M 241 169
6153 cold 12 0 M 209 315.9
6157 cold 12 0 M 224 249.6
6160 cold 12 0 M 238 494
6172 cold 12 0 M 316 373.1
6175 cold 12 0 I 246 269.1
6178 cold 12 0 M 218 162.5
6189 cold 12 0 I 216 577.2
6191 cold 12 0 I 227 234
6196 cold 12 1 I 203 546
6128 cold 12 1 I 231 492.7
6174 cold 12 1 I 233 457.6
6148 cold 12 1 I 213 442
6173 cold 12 1 I 210 432.9
6177 cold 12 1 I 250 410.8
6188 cold 12 1 I 245 409.5
6199 cold 12 1 I 254 369.2
6163 cold 12 1 I 228 364
6164 cold 12 1 I 257 353.6
6232 warm 12 0 M 286 663
6235 warm 12 0 I 297 531.7
6253 warm 12 0 I 275 387.4
6223 warm 12 0 M 270 380.9
6228 warm 12 0 I 290 370.5
6234 warm 12 0 I 263 345.8
6265 warm 12 0 I 282 317.2
6238 warm 12 0 M 375 301.6
6259 warm 12 0 M 281 300.3
6242 warm 12 0 M 377 289.9
6255 warm 12 1 I 262 536.9
6266 warm 12 1 I 264 526.5
6245 warm 12 1 I 365 481
6261 warm 12 1 I 273 469.3
6249 warm 12 1 I 279 461.5
6233 warm 12 1 I 371 370.5
6244 warm 12 1 I 284 358.8
6262 warm 12 1 I 289 352.3
6257 warm 12 1 I 363 269.1
6256 warm 12 1 I 283 247

I started out today by just working with the first 11 samples listed.

FRP trtmnt_tank sample_day infection_status maturity tube_number total-yield_ng
6157 NA 9 0 M 169 603.2
6143 NA 9 0 M 10 380.9
6228 NA 9 0 I 29 335.4
6150 NA 9 0 M 119 323.7
6277 NA 9 0 I 177 296.4
6156 NA 9 0 I 24 275.6
6272 NA 9 0 I 111 260
6178 NA 9 0 M 124 215.8
6161 NA 9 0 M 53 201.5
6136 NA 9 0 M 91 192.4
6137 NA 9 1 I 81 403

I made a chip all ready to go, but the computer and machine set-up was not great, so Sam helped fix it (connected laptop to monitor becuase laptop screen is too dark since it’s low on battery power).

I ended up losing that chip because it took 10+ minutes to fix the computer-machine situation, and you have to run a chip within 5 minutes.

So, I tried again:

I ran 1ul of each sample on a RNA Pico Chip. Gel:
img

Electropherogram:
img

After talking with Sam, he said that I should try running a blank plate to check that all the reagents work, but include the ladder.

Results from that:
Gel:
img

Electropherogram:
img

The ladder is not showing up (like it didn’t in the previous images with the samples), but the reagents are working correctly.

Then, since I had some gel-dye mixes I made that only last through today, we decided I should give those samples another try, but without the ladder. I don’t actually need a ladder for what I’m trying to do.

Results:
Gel:
img
Electropherogram:
img

I made a GitHub Issue with these results to get Sam’s input: GitHub Issue #792

Nanodrop

Per Sam’s suggestion, I ran those same samples on the Nanodrop (1ul each).

Results:

Sample ID User ID Date Time ng/ul A260 A280 260/280 260/230 Constant Cursor Pos. Cursor abs. 340 raw
10 Default 11/6/2019 4:47 PM 40.03 1.001 0.486 2.06 0.32 40.00 230 3.117 0.038
169 Default 11/6/2019 4:48 PM 51.50 1.288 0.599 2.15 1.92 40.00 230 0.671 0.020
29 Default 11/6/2019 4:49 PM 49.92 1.248 0.630 1.98 1.33 40.00 230 0.937 0.128
119 Default 11/6/2019 4:50 PM 33.50 0.838 0.486 1.72 0.22 40.00 230 3.826 5.695
119 Default 11/6/2019 4:52 PM 7.22 0.180 0.076 2.38 0.06 40.00 230 3.049 -1.480
177 Default 11/6/2019 4:53 PM 24.73 0.618 0.293 2.11 1.08 40.00 230 0.575 0.196
24 Default 11/6/2019 4:54 PM 26.51 0.663 0.319 2.08 1.13 40.00 230 0.588 0.025
111 Default 11/6/2019 4:55 PM 23.54 0.589 0.296 1.99 0.22 40.00 230 2.637 0.138
124 Default 11/6/2019 4:57 PM 15.28 0.382 0.170 2.24 0.57 40.00 230 0.675 0.016
53 Default 11/6/2019 4:59 PM 20.58 0.514 0.272 1.89 0.10 40.00 230 5.414 0.015
91 Default 11/6/2019 5:00 PM 15.70 0.392 0.201 1.96 0.19 40.00 230 2.022 0.621
81 Default 11/6/2019 5:01 PM 24.68 0.617 0.305 2.02 0.22 40.00 230 2.814 0.021
Sample ID Curve Type Ref conc Ref Abs Std 1 conc Std 1 Abs. Std 2 conc Std 2 Abs Std 3 conc Std 3 Abs Std 4 conc Std 4 Abs Std 5 conc Std 5 Abs Std 6 conc Std 6 Abs Std 7 conc Std 7 Abs
10 Interpolation
169 Interpolation
29 Interpolation
119 Interpolation
119 Interpolation
177 Interpolation
24 Interpolation
111 Interpolation
124 Interpolation
53 Interpolation
91 Interpolation
81 Interpolation

I also took screenshots of all the sample graphs: 20191106-notebook-images/nanodrop

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