Grace’s Notebook: May 8, 2018 Bioanalyzer and Pubathon

Bioanalyzer

Last week I tried the bioanalyzer a couple times on tubes numbered 274 and 401.
Things didn’t look so great.

Gel:
img

Electropherogram:
img

GitHub issue

Sam suggested I just try again and also use tubes with very high RNA concentration.

So today I tried with tubes 14 (123.0 ng/mL) and 348 (144.0 ng/mL) (FRP 6144; infected; ambient).

Looks weird again.
Gel:
img

Electropherogram:
img

Looking at it now, I think I maybe messed it up by selecting “mRNA pico” instead of “Eukaryote_total-RNA”. Will ask Sam to see what’s up.

Will re-do tomorrow.

Pub-a-thon

Points – get points by commenting and reviewing others’ papers and repos!

DIA paper – get Emma over for the next Pubathon meeting (once date and time determined) to talk about next steps and moving forward with the paper.

Crab paper – methods and introduction stuff of whatever I can add

DecaPod

Published S1 Ep7 (Crab Meeting #3)

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Yaamini’s Notebook: Gonad Methylation Analysis Part 11

I am Jerry Gergich

jerry

Today I’m really riding the struggle bus! In this issue, I found out that I forgot an important argument in my bismark alignment. I did not indicate that I had paired read data. I now have to invoke my inner Jerry Gergich and redo the work. I specified the -1 and -2 “mates” files and reran the code.

For my subset, I will also re-extract the methylated data and remake my reports. All of the file names will stay the same, so they should still be easy to find. While I wait for the alignment to finish on the subset, I’ll start working with methylKit. Switching out the correct data will be simple!

P.S. Here’s some evidence I’m visibly on that struggle bus ft. Steven’s classic sass. Is the “Yep” referring to me figuring out the problem, or acknowledging that I’m not thinking clearly? I think it’s both:

screen shot 2018-05-08 at 10 51 34 am

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Yaamini’s Notebook: Gonad Methylation Analysis Part 9

The whole enchilada

In this Jupyter notebook, I ran the bismark alignment on the full range of my samples. The .txt file output can be found here. The .bam files produced were several GBs! I saved them in this OWL folder. I also uploaded all .bam output from my file subsets here.

My next step is to run these samples through methylKit in R. I’m going to test this first with the subset data I have, then start plugging along on these large files.

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Yaamini’s Notebook: Gonad Methylation Analysis Part 10

Visualizing bismark outputs

I started my IGV trials and tribulations yesterday, and today I’m (maybe) struggling a little less.

My goal was to use IGV to visualize 1) my alignment output and 2) bedGraphs from methylation extraction.

Alignment output

To visualize the alignment output, I first needed to sort and index the .bam output. I downloaded igvtools. In this lab notebook, I applied the sort and index command to one of my .bam files. I then uploaded this file to my IGV file.

untitled

Figure 1. IGV with .bam file.

I had to zoom to the single nucleotide level to view anything!

untitled5

Figure 2. Alignment at single nucleotide level.

I wanted to add in my other alignment files, but I didn’t want to go through the pain of writing individual lines of code to convert the files (Sam tried to help me, but right now there doesn’t seem to be a solution. Steven also mentioned that I should have used deduplicate_bismark after the alignment, which would have sorted and indexed all my files. You can see the issue here).

bedGraphs

I uploaded all of the bedGraphs from the methylation extractor (found here) into my IGV file.

untitled6

Figure 3. bedGraphs showing methylation levels for each file.

There isn’t any apparent difference between the two treatments. I looked at Steven’s lab notebook and he noticed the same thing. He also said that going to 100k made a difference. I have no idea what this means so I’ll have to ask him.

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Sam’s Notebook:BS-seq Mapping – Olympia oyster bisulfite sequencing: TrimGalore > FastQC > Bismark

0000-0002-2747-368X

Steven asked me to evaluate our methylation sequencing data sets for Olympia oyster.

According to our Olympia oyster genome wiki, we have the following two sets of BS-seq data:

All computing was conducted on our Apple Xserve: roadrunner.

All steps were documented in this Jupyter Notebook (GitHub): 20180503_emu_oly_methylation_mapping.ipynb

NOTE: The Jupyter Notebook linked above is very large in size. As such it will not render on GitHub. It will need to be downloaded to a computer that can run Jupyter Notebooks and viewed that way.

Here’s a brief overview of what was done.

Samples were trimmed with TrimGalore and then evaluated with FastQC. MultiQC was used to generate a nice visual summary report of all samples.

The Olympia oyster genome assembly, pbjelly_sjw_01, was used as the reference genome and was prepared for use in Bismark:

  /home/shared/Bismark-0.19.1/bismark_genome_preparation \ --path_to_bowtie /home/shared/bowtie2-2.3.4.1-linux-x86_64/ \ --verbose /home/sam/data/oly_methylseq/oly_genome/ \ 2> 20180507_bismark_genome_prep.err  

Bismark was run on trimmed samples with the following command:

  /home/shared/Bismark-0.19.1/bismark \ --path_to_bowtie /home/shared/bowtie2-2.3.4.1-linux-x86_64/ \ --genome /home/sam/data/oly_methylseq/oly_genome/ \ -u 1000000 \ -p 16 \ --non_directional \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/1_ATCACG_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/2_CGATGT_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/3_TTAGGC_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/4_TGACCA_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/5_ACAGTG_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/6_GCCAAT_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/7_CAGATC_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/8_ACTTGA_L001_R1_001_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_10_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_11_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_12_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_13_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_14_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_15_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_16_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_17_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_18_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_1_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_2_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_3_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_4_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_5_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_6_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_7_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_8_s456_trimmed.fq.gz \ /home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_9_s456_trimmed.fq.gz \ 2> 20180507_bismark_02.err  

Results:

TrimGalore output folder:

FastQC output folder:

MultiQC output folder:

MultiQC Report (HTML):

Bismark genome folder: 20180503_oly_genome_pbjelly_sjw_01_bismark/

Bismark output folder:

Yaamini’s Notebook: Gonad Methylation Analysis Part 9

The whole enchilada

In this Jupyter notebook, I ran the bismark alignment on the full range of my samples. The .txt file output can be found here. The .bam files produced were several GBs! I saved them in this OWL folder. I also uploaded all .bam output from my file subsets here.

My next step is to run these samples through methylKit in R. I’m going to test this first with the subset data I have, then start plugging along on these large files.

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Laura’s Notebook: Where are they now?

9 months later … where are they now?

I measured four of my Olympia oyster seed batches from the 2017 experiment. This is seed I produced from broodstock that were exposed to two pH treatments (7.3, 7.8) prior to reproductive conditioning (check out this repo README). Measured thus far are 385 oysters from each of the following groups:

  1. North Sound, 6-degree low pH
  2. North Sound, 6-degree ambient pH
  3. Hood Canal, 6-degree low pH
  4. Hood Canal, 6-degree ambient pH

Takeaway – adult oysters exposed to low pH prior to reproductive conditioning produced less viable larvae (measured via survival to post-set), and carry-over effect persists to 9-month juvenile stage as size is significantly lower.

### As a reminder here’s surival for the North Sound and Hood Canal groups:

North Sound Survival Chart

Hood Canal Survival Chart

Here’s the new data, which is size (mm) at ~9 months:

2018-05-04_ns-length

2018-05-04_hc-length

Mean length and anova results:

image

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