Sam’s Notebook: Samples Received – Marinelli Shellfish Company C.gigas and C.sikamea Oysters

Steven was recently contacted by Marinelli Shellfish Company to see if we could help them determine if some oysters they had were Crassostrea gigas (Pacific oyster) or Crassostrea sikamea (Kumamoto). Steven knows of a paper with primer sequences to use with qPCR for this specific determination.

They sent ~12 of:

  • known Crassostrea gigas
  • known Crassostrea sikamea
  • unknown

We collected mantle tissue from 12 of each group. Samples were labeled in the following fashion:

  • C.gigas 1191-SS ## (known Crassostrea gigas)
  • C.sikamea CA5SS ## (known Crassostrea sikamea)
  • C.sikamea 1191-SS ## (unknown)

Samples were stored at -80oC in Rack 2, Column 4, Row 5:

NOTE: Only 11 samples were collected for C.sikamea CA5SS.

Sam’s Notebook: Data Wrangling – Create Panopea-generosa-vv0.74.a4 Intron and Intergenic BED Files

Since generating an updated Pgenerosa_v074 annotation, we also needed updated intergenic and intron bed files to put in the OSF repository for this project.

I generated intergenic and intron BED files by following along with Steven’s notebooks:

Steven intergenic BED file notebook (GitHub):

Steven intron BED file notebook (GitHub):

Here’s how I generated these two BED files.

Jupyter Notebook (GitHub):

Shelly’s Notebook: Wed. Oct. 30, Geoduck DMR filtering

Summary of DMR analysis so far:

  1. Call methylation state from bismark data (mox script here)
  2. Call DMRs within individual samples (mox script here)
  3. Filter DMRs for those in at least 3/4 samples/group (R script here, R proj here)
  4. Filter DMRs for those significant at ANOVA uncorrected p.value < 0.1 R markdown script here, Rproj here

Summary of Step 4 above

Filtering DMRs for those significant at ANOVA uncorrected p.value < 0.1 from all 4 comparisons (all ambient samples, day10 samples, day 135 samples, and day 145 samples)

amb_MCmax30DMR_aov0.1_heatmap.jpg

  • ANOVA significant all ambient MCmax30 DMR violinplots: amb_MCmax30DMR_aov0.1_boxplots.jpg
  • ANOVA significant day 10 MCmax30 DMR heatmap:
    • Heatmap key: Column color bar: cyan = ambient, light pink = low.pH, magenta = super.low.pH. heatmap cell color: Red = more methylation, blue = no methylation, black = no data. day10_MCmax30DMR_aov0.1_heatmap.jpg
  • ANOVA significant day 10 MCmax30 DMR violinplots: day10_MCmax30DMR_aov0.1_boxplots.jpg
  • ANOVA significant day 135 MCmax30 DMR heatmap:
    • Heatmap key: Column color bar: cyan = ambient, light pink = low.pH, magenta = super.low.pH. heatmap cell color: Red = more methylation, blue = no methylation, black = no data. day135_MCmax30DMR_aov0.1_heatmap.jpg
  • ANOVA significant day 135 MCmax30 DMR violinplots: day135_MCmax30DMR_aov0.1_boxplots.jpg
  • ANOVA significant day 145 MCmax30 DMR heatmap:
    • Heatmap key: Column color bar: cyan = ambient, light pink = low.pH, magenta = super.low.pH. heatmap cell color: Red = more methylation, blue = no methylation, black = no data. day145_MCmax30DMR_aov0.1_heatmap.jpg
  • ANOVA significant day 145 MCmax30 DMR violinplots: day145_MCmax30DMR_aov0.1_boxplots.jpg

Next steps:

  • visualize significant DMRs in IGV
  • Functional analysis of DMRs

from shellytrigg https://ift.tt/2Wp7oYu
via IFTTT

Shelly’s Notebook: Tues. Oct. 29, Geoduck DMR filtering

Performing group stats on DMRs

Comparing ANOVA vs. GLM significant DMRs

  • ANOVA and GLM each identify different DMRs so I plotted these using an uncorrected p-value < 0.1. Column color bar in heat maps below: cyan = ambient, light pink = low.pH, magenta = super.low.pH. heatmap color: Red = more methylation, blue = no methylation, black = no data.
  • ANOVA significant day 10 MCmax10 DMR heatmap: day10_MCmax10DMR_aov0.1_heatmap.jpg
  • GLM significant day 10 MCmax10 DMR heatmap: day10_MCmax10DMR_glm0.1_heatmap.jpg
  • ANOVA significant day 10 MCmax10 DMR violinplots: day10_MCmax10DMR_aov0.1_boxplots.jpg
  • GLM significant day 10 MCmax10 DMR violinplots: day10_MCmax10DMR_glm0.1_boxplots.jpg
  • CONCLUSIONS:
    • the GLM seems more likely to identify DMRs as significant when one group has zero % methylation and/or half of another group’s samples have zero % methylation.
    • I feel alittle more confident going with the ANOVA

Check ANOVA significant DMRs found by other DMRfind parameters

  • Next I ran ANOVA DMRs from DMR parameters MCmax = 25bp, 30bp, and 50bp for day10 samples to see if these DMRs show obvious group patterns in the heatmaps and violinplots
  • Rmarkdown files that generated figures below are here:
  • ANOVA significant day 10 MCmax25 DMR heatmap: day10_MCmax25DMR_aov0.1_heatmap.jpg
  • ANOVA significant day 10 MCmax25 DMR violinplots: day10_MCmax25DMR_aov0.1_boxplots.jpg
  • ANOVA significant day 10 MCmax30 DMR heatmap: day10_MCmax30DMR_aov0.1_heatmap.jpg
  • ANOVA significant day 10 MCmax30 DMR violinplots: day10_MCmax30DMR_aov0.1_boxplots.jpg
  • ANOVA significant day 10 MCmax50 DMR heatmap: day10_MCmax50DMR_aov0.1_heatmap.jpg
  • ANOVA significant day 10 MCmax50 DMR violinplots: day10_MCmax50DMR_aov0.1_boxplots.jpg
  • CONCLUSIONS: MCmax30 give the most number of DMRs while sill maintaining clear patterns in the heatmap and violinplots. I think the patterns are less obvious in the MCmax50 heatmap. Therefore, I think it’s safe to go with the MCmax30 parameter.
  • NEXT STEPS: Run ANOVA, generate heatmaps and violinplots for DMRs from all 4 comparisons

from shellytrigg https://ift.tt/330ac0J
via IFTTT