Sam’s Notebook: Transcriptome Assessment – BUSCO Metazoa on Hematodinium MEGAN Transcriptome

I previously created a Hematodinium de novo transcriptome assembly with Trinity from the MEGAN6 taxonomic-specific reads for Alveolata on 20200122 and decided to assess its “completeness” using BUSCO and the metazoa_odb9 database.

BUSCO was run with the --mode transcriptome option on Mox.

SBATCH script (GitHub):

#!/bin/bash ## Job Name #SBATCH --job-name=cbai_busco_megan_transcriptome ## Allocation Definition #SBATCH --account=srlab #SBATCH --partition=srlab ## Resources ## Nodes #SBATCH --nodes=1 ## Walltime (days-hours:minutes:seconds format) #SBATCH --time=3-00:00:00 ## Memory per node #SBATCH --mem=120G ##turn on e-mail notification #SBATCH --mail-type=ALL #SBATCH --mail-user=samwhite@uw.edu ## Specify the working directory for this job #SBATCH --chdir=/gscratch/scrubbed/samwhite/outputs/20200207_hemat_busco_megan # Load Python Mox module for Python module availability module load intel-python3_2017 # Load Open MPI module for parallel, multi-node processing module load icc_19-ompi_3.1.2 # SegFault fix? export THREADS_DAEMON_MODEL=1 # Document programs in PATH (primarily for program version ID) { date echo "" echo "System PATH for $SLURM_JOB_ID" echo "" printf "%0.s-" {1..10} echo "${PATH}" | tr : \\n } >> system_path.log # Establish variables for more readable code timestamp=$(date +%Y%m%d) species="hemat" prefix="${timestamp}.${species}" ## Input files and settings base_name="${prefix}.megan" busco_db=/gscratch/srlab/sam/data/databases/BUSCO/metazoa_odb9 transcriptome_fasta=/gscratch/srlab/sam/data/Hematodinium/transcriptomes/20200122.hemat.megan.Trinity.fasta augustus_species=fly threads=28 ## Save working directory wd=$(pwd) ## Set program paths augustus_bin=/gscratch/srlab/programs/Augustus-3.3.2/bin augustus_scripts=/gscratch/srlab/programs/Augustus-3.3.2/scripts blast_dir=/gscratch/srlab/programs/ncbi-blast-2.8.1+/bin/ busco=/gscratch/srlab/programs/busco-v3/scripts/run_BUSCO.py hmm_dir=/gscratch/srlab/programs/hmmer-3.2.1/src/ ## Augustus configs augustus_dir=${wd}/augustus augustus_config_dir=${augustus_dir}/config augustus_orig_config_dir=/gscratch/srlab/programs/Augustus-3.3.2/config ## BUSCO configs busco_config_default=/gscratch/srlab/programs/busco-v3/config/config.ini.default busco_config_ini=${wd}/config.ini # Export BUSCO config file location export BUSCO_CONFIG_FILE="${busco_config_ini}" # Export Augustus variable export PATH="${augustus_bin}:$PATH" export PATH="${augustus_scripts}:$PATH" export AUGUSTUS_CONFIG_PATH="${augustus_config_dir}" # Copy BUSCO config file cp ${busco_config_default} "${busco_config_ini}" # Make Augustus directory if it doesn't exist if [ ! -d "${augustus_dir}" ]; then mkdir --parents "${augustus_dir}" fi # Copy Augustus config directory cp --preserve -r ${augustus_orig_config_dir} "${augustus_dir}" # Edit BUSCO config file ## Set paths to various programs ### The use of the % symbol sets the delimiter sed uses for arguments. ### Normally, the delimiter that most examples use is a slash "/". ### But, we need to expand the variables into a full path with slashes, which screws up sed. ### Thus, the use of % symbol instead (it could be any character that is NOT present in the expanded variable; doesn't have to be "%"). sed -i "/^;cpu/ s/1/${threads}/" "${busco_config_ini}" sed -i "/^tblastn_path/ s%tblastn_path = /usr/bin/%path = ${blast_dir}%" "${busco_config_ini}" sed -i "/^makeblastdb_path/ s%makeblastdb_path = /usr/bin/%path = ${blast_dir}%" "${busco_config_ini}" sed -i "/^augustus_path/ s%augustus_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/bin/%path = ${augustus_bin}%" "${busco_config_ini}" sed -i "/^etraining_path/ s%etraining_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/bin/%path = ${augustus_bin}%" "${busco_config_ini}" sed -i "/^gff2gbSmallDNA_path/ s%gff2gbSmallDNA_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/scripts/%path = ${augustus_scripts}%" "${busco_config_ini}" sed -i "/^new_species_path/ s%new_species_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/scripts/%path = ${augustus_scripts}%" "${busco_config_ini}" sed -i "/^optimize_augustus_path/ s%optimize_augustus_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/scripts/%path = ${augustus_scripts}%" "${busco_config_ini}" sed -i "/^hmmsearch_path/ s%hmmsearch_path = /home/osboxes/BUSCOVM/hmmer/hmmer-3.1b2-linux-intel-ia32/binaries/%path = ${hmm_dir}%" "${busco_config_ini}" # Run BUSCO/Augustus training ${busco} \ --in ${transcriptome_fasta} \ --out ${base_name} \ --lineage_path ${busco_db} \ --mode transcriptome \ --cpu ${threads} \ --long \ --species ${augustus_species} \ --tarzip \ --augustus_parameters='--progress=true' 

Sam’s Notebook: Transcriptome Assessment – BUSCO Metazoa on C.bairdi MEGAN Transcriptome

I previously created a C.bairdi de novo transcriptome assembly with Trinity from the MEGAN6 taxonomic-specific reads for Arthropoda on 20200122 and decided to assess its “completeness” using BUSCO and the metazoa_odb9 database.

BUSCO was run with the --mode transcriptome option on Mox.

SBATCH script (GitHub):

#!/bin/bash ## Job Name #SBATCH --job-name=cbai_busco_megan_transcriptome ## Allocation Definition #SBATCH --account=srlab #SBATCH --partition=srlab ## Resources ## Nodes #SBATCH --nodes=1 ## Walltime (days-hours:minutes:seconds format) #SBATCH --time=3-00:00:00 ## Memory per node #SBATCH --mem=120G ##turn on e-mail notification #SBATCH --mail-type=ALL #SBATCH --mail-user=samwhite@uw.edu ## Specify the working directory for this job #SBATCH --chdir=/gscratch/scrubbed/samwhite/outputs/20200207_cbai_busco_megan # Load Python Mox module for Python module availability module load intel-python3_2017 # Load Open MPI module for parallel, multi-node processing module load icc_19-ompi_3.1.2 # SegFault fix? export THREADS_DAEMON_MODEL=1 # Document programs in PATH (primarily for program version ID) { date echo "" echo "System PATH for $SLURM_JOB_ID" echo "" printf "%0.s-" {1..10} echo "${PATH}" | tr : \\n } >> system_path.log # Establish variables for more readable code timestamp=$(date +%Y%m%d) species="cbai" prefix="${timestamp}.${species}" ## Input files and settings base_name="${prefix}.megan" busco_db=/gscratch/srlab/sam/data/databases/BUSCO/metazoa_odb9 transcriptome_fasta=/gscratch/srlab/sam/data/C_bairdi/transcriptomes/20200122.C_bairdi.megan.Trinity.fasta augustus_species=fly threads=28 ## Save working directory wd=$(pwd) ## Set program paths augustus_bin=/gscratch/srlab/programs/Augustus-3.3.2/bin augustus_scripts=/gscratch/srlab/programs/Augustus-3.3.2/scripts blast_dir=/gscratch/srlab/programs/ncbi-blast-2.8.1+/bin/ busco=/gscratch/srlab/programs/busco-v3/scripts/run_BUSCO.py hmm_dir=/gscratch/srlab/programs/hmmer-3.2.1/src/ ## Augustus configs augustus_dir=${wd}/augustus augustus_config_dir=${augustus_dir}/config augustus_orig_config_dir=/gscratch/srlab/programs/Augustus-3.3.2/config ## BUSCO configs busco_config_default=/gscratch/srlab/programs/busco-v3/config/config.ini.default busco_config_ini=${wd}/config.ini # Export BUSCO config file location export BUSCO_CONFIG_FILE="${busco_config_ini}" # Export Augustus variable export PATH="${augustus_bin}:$PATH" export PATH="${augustus_scripts}:$PATH" export AUGUSTUS_CONFIG_PATH="${augustus_config_dir}" # Copy BUSCO config file cp ${busco_config_default} "${busco_config_ini}" # Make Augustus directory if it doesn't exist if [ ! -d "${augustus_dir}" ]; then mkdir --parents "${augustus_dir}" fi # Copy Augustus config directory cp --preserve -r ${augustus_orig_config_dir} "${augustus_dir}" # Edit BUSCO config file ## Set paths to various programs ### The use of the % symbol sets the delimiter sed uses for arguments. ### Normally, the delimiter that most examples use is a slash "/". ### But, we need to expand the variables into a full path with slashes, which screws up sed. ### Thus, the use of % symbol instead (it could be any character that is NOT present in the expanded variable; doesn't have to be "%"). sed -i "/^;cpu/ s/1/${threads}/" "${busco_config_ini}" sed -i "/^tblastn_path/ s%tblastn_path = /usr/bin/%path = ${blast_dir}%" "${busco_config_ini}" sed -i "/^makeblastdb_path/ s%makeblastdb_path = /usr/bin/%path = ${blast_dir}%" "${busco_config_ini}" sed -i "/^augustus_path/ s%augustus_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/bin/%path = ${augustus_bin}%" "${busco_config_ini}" sed -i "/^etraining_path/ s%etraining_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/bin/%path = ${augustus_bin}%" "${busco_config_ini}" sed -i "/^gff2gbSmallDNA_path/ s%gff2gbSmallDNA_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/scripts/%path = ${augustus_scripts}%" "${busco_config_ini}" sed -i "/^new_species_path/ s%new_species_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/scripts/%path = ${augustus_scripts}%" "${busco_config_ini}" sed -i "/^optimize_augustus_path/ s%optimize_augustus_path = /home/osboxes/BUSCOVM/augustus/augustus-3.2.2/scripts/%path = ${augustus_scripts}%" "${busco_config_ini}" sed -i "/^hmmsearch_path/ s%hmmsearch_path = /home/osboxes/BUSCOVM/hmmer/hmmer-3.1b2-linux-intel-ia32/binaries/%path = ${hmm_dir}%" "${busco_config_ini}" # Run BUSCO/Augustus training ${busco} \ --in ${transcriptome_fasta} \ --out ${base_name} \ --lineage_path ${busco_db} \ --mode transcriptome \ --cpu ${threads} \ --long \ --species ${augustus_species} \ --tarzip \ --augustus_parameters='--progress=true' 

Yaamini’s Notebook: Gigas and Virginica Comparison

Comparing Crassostrea spp. methylation patterns

Guess I got to put together a poster for ASLO! My goal is to showcase some preliminary comparisons of methylation patterns in C. gigas and C. virginica gonad tissue, and DML in reponse to experimental ocean acidification.

Method comparison

Although the experiments are similar, there are some key differences in the experimental design that I need to consider. I made some summary tables to easily compare the experiments.

The first takeaway is that the C. gigas experimental duration was longer (7 weeks vs. 4 weeks), and potentially more extreme. Although the treatment pH conditions were the same for both species, pCO2 was higher for C. gigas in both experimental conditions.

Table 1. Carbonate chemistry parameters for both experiments. The C. gigas experiment took place over 7 weeks and had ambient inflow, while the C. virginica experiment was 4 weeks long and had controlled, not ambient, inflow.

Species pH pCO2 (µatm) DIC AT Ωcalcite
C. gigas (ambient) 7.82 ± 0.02 863 ± 42 2533 ± 35 2611 ± 31 2.13 ± 0.06
C. gigas (treatment) 7.29 ± 0.01 3344 ± 50 2920 ± 15 2808 ± 12 0.68 ± 0.01
C. virginica (control) 7.95 ± 0.01 492 ± 50 1960 ± 32 2140 ± 15 3.01 ± 0.25
C. virginica (treatment) 7.29 ± 0.01 2550 ± 211 2173 ± 37 2132 ± 42 0.72 ± 0.06

The C. gigas analysis had a smaller sample size. Pooled samples were used since I had lower DNA yield from histology blocks than from the tissue Katie and Alan sent. To compensate for the smaller sample size, I used more stringent analysis parameters (minimum sequencing depth used in downstream analyses and bismark alignment score).

Table 2. Sequencing and analysis parameters. For C. gigas, one pooled sample with two individuals of the same sex and reproductive sstage was created for each treatment. Alignment score refers to the score_min parameter used by bismark.

Species Sample size Sequencing type Minimum Sequencing Depth Alignment Score
C. gigas 2 (pooled; 1/treatment) WGBS 10x 0,-0.9
C. virginica 10 (5/treatment) MBD-BSSeq 5x 0,-1.2

Methylation island analysis

I want to replicate this perl script from this paper to identify methylation islands (MI) in the C. gigas and C. virginica genomes. The goal is to create a MI track for each species that I can then compare with DML in response to ocean acidification.

My perl comprehension is pretty low…but from what I can tell, all I need to do is:

  • Choose a window size: Probably going to start with 200 bp since that’s what the paper I’m replicating used, but I’ll try 300 bp too
  • Define mCpG fraction: Again, starting with 0.02 since that’s what the authors used, but I want to play around with this since insect genomes are less methylated than C. virginica.
  • Choose a step size: 50 bp (again from the paper)
  • Provide a list of mCpG in the genome (column 1: scaffold, column 2: bp): The paper used Bis-class to identify mCpG, but I already identified CpG as methylated using a 50% methylation cut-off for C. virginica, and Claire and Mac have done this previously with C. gigas

I’m going to start with C. virginica. Since my BEDfile of methylated loci has three columns instead of two, I started by creating a new file with only two columns: chromosome and mCpG position in this Jupyter notebook.

awk '{print $1"\t"$2}' 2019-04-09-All-5x-CpG-Loci-Methylated.bed > 2019-04-09-All-5x-CpG-Loci-Methylated-Reduced.bed #Create new tab-delimited file with only chromosome and mCpG position 

Then, I cloned the script to my repo and saved it here. Finally, I ran the script in my Jupyter notebook based on the README.md instructions! I ran several iterations of the script with different mCpG fractions.

#Run script with 200 bp window, 0.02 mCpG fraction, and 50 bp step size ./methyl_island_sliding_window.pl 200 0.02 50 2019-04-09-All-5x-CpG-Loci-Methylated-Reduced.bed > 2020-02-06-Methylation-Islands-200_0.02_50.tab 

I tried this with various mCpG fractions and initial widow sizes and saved the output in this folder. I really should have found a way to generate all these files programmatically…but here we are!

Table 3. MI statistics for various parameters. In general, MI number decreases as mCpG fraction and window size increase.

Initial Window Size (bp) mCpG Fraction Number of MI Max mCpG in MI Min mCpG in MI
200 0.02 119705 24777 4
200 0.03 129006 8305 6
200 0.04 113806 1682 8
200 0.05 93229 1452 10
200 0.10 18719 1167 20
200 0.15 2453 177 30
200 0.20 320 94 40
200 0.25 37 63 50
200 0.27 8 57 54
200 0.30 0 0 0
300 0.02 91756 24777 6
300 0.03 91833 8305 9
300 0.04 74497 1682 12
300 0.05 53510 1452 15
300 0.10 6629 1167 30
300 0.15 546 177 45
300 0.20 20 94 60
300 0.25 0 0 0

Now that I have all of these different MI tracks, I need to visualize them in IGV to 1) make sure methylation islands make sense in general and 2) see which set of parameters best fits my data. I created a new IGV session and added in bedgraphs with the locations of all CpGs from the concatenated 5x data, as well as just the methylated CpGs, using gannet links so someone else could view my session easily. My tab-delimited files have an extra column that counts the number of mCpG in each MI. Unfortunately IGV wasn’t too happy about this extra column when I tried to import and view it. I used the following loop to create BEDfiles from my tab-delimited output:

%%bash for f in *.tab do awk '{print $1"\t"$2"\t"$3}' ${f} > ${f}.bed done 

I loaded all my BEDfiles in IGV! And looked at the MI tracks at various resolutions.

Screen Shot 2020-02-06 at 7 34 32 PM

Screen Shot 2020-02-06 at 7 34 53 PM

Screen Shot 2020-02-06 at 7 35 25 PM

Screen Shot 2020-02-06 at 7 36 05 PM

Screen Shot 2020-02-06 at 7 36 44 PM

Figures 1-5. MI tracks generated using various parameters. Blue MI tracks are those from 200 bp windows, and purple MI tracks are those from 300 bp windows. The mCpG fraction increases down the screen.

After creating BEDfiles and putting them on gannet, I added them to IGV. Based on my IGV session, it seems like the 0.02 mCpG fraction actually does a pretty good job of capturing the methylation islands. I just don’t know if I should choose the 200 or 300 bp windows. I’m leaning towards 200 bp just because that’s the original method and I don’t really have a better way to choose. I posted this issue to get feedback.

Going forward

  1. Generate C. gigas methylation islands
  2. Charaterize C. gigas and C. virginica DML in relation to methylation islands
  3. Compare general methylation landscapes
  4. Complete a GO-MWU and DMG analysis with C. gigas
  5. Compare C. gigas and C. virginica DML
  6. Draft poster for ASLO and get feedback
  7. Finalize ASLO poster and print!

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Kaitlyn’s notebook: Geoduck hemolymph next steps and RNA extractions

Recap

The overall goal is to identify whether the geoduck are reproductively developed (ready to spawn), and potentially whether the geoduck are male or female, using hemolymph (which can be non-lethally collected).

So far we have…

1.Identified biomarkers for reproductive development/sex

  1. Developed primers for biomarkers

Next Steps:

I want to test the newly developed primers, but first I need to ensure I have adequate amounts of RNA and cDNA because I now have 17 primer pairs to test.

Identified 9 female and 7 male hemolymph samples across stages for qPCR:

Sample Sex Stage
19 F 2
21 F 2
23 F 2
55 F 3
31 F 4
39 F 5
37 F 6
57 F 7
61 F 7
28 M 1
27 M 2
54 M 3
43 M 4
59 M 4
62 M 5
66 M 5

Samples for RNA extraction:

50ng RNA is needed for the RT protocol (100 ng is ideal esp. since I will use 1ul of template) so previous samples will need to be extracted again: 19, 21, 23, 55, 37, 28, 54, and 43.

Samples for RT (to make cDNA):

Samples that did not amplify previously may have had errors during RT so I will remake their RT in addition to the new samples: 19, 21, 23, 55, 37, 28, 54, 43, 39, 57, 62, and 66.


RNA Isolation:

RNA was isolated with a Quick-DNA/RNA Microprep Plus Kit by ZymoResearch according to the manufacturer’s protocol from geoduck hemolymph samples. 300ul of sample was used and 1200ul of lysis buffer was added for sample prep. All 250ul of sample 19 was used and 1000ul of lysis buffer added instead. The RNA was NOT DNased (will need to be done before RT).

I tested the ‘whole blood’ manufacturer instructions on sample 43B; sample 43A was done normally. Sample 54 was accidentally added to the sample 55 column. I pipetted out the supernatent from the 55 column, denoted the column as 55F, and added the supernatent to the correct 54 column. Then I got a new column for the second half of 55, labelled it as 55(2) and continued the extraction.

Samples were quantified with the hsRNA Assay for Qubit according to manufacturer’s protocol. 2ul of sample and 198ul of working solution was used per assay tube. Standard 1: 92.49 RFU and Standard 2: 1881.11 RFU.

Sample Sex Stage RNA (ng/ul)
19 F 2 35.8
21 F 2 18
23 F 2 low (151.89 RFU)
28 M 1 18.7
37 F 6 high (4893.75 RFU)
43A M 4 high (3520.34 RFU)
43B M 3 3.62
54 F 3 high (3152.78 RFU)
55F F 7 high (3544.7 RFU)
55(2) M 4 57

Although in RFU range, the low sample may be under ng/ul minimum and is likely not enough for RT. High samples will need to be diluted 1:2 and be requantified.

Samples are stored in a box in the -80C freezer in 3, 3, 2, labelled “RNA isolations; geoduck 12/17”. Note that blue tubes are from previous RNA isolations which were DNased, and those with ‘B’ denoted are from the second round of RNA extractions (contain 0ng RNA). The samples extracted today, which are not DNased, are in yellow tubes.

20200205_102703


How much cDNA should I make?

  • RT protocol provides 20ul of template
    • will use 1ul of template for qPCR
      • = total of 20 samples that can be done
        • run samples in duplicate…
          • must do 2 rounds of RT to get enough cDNA for 1 round of qPCR with 17 primers
  • RT calculations

I also picked up the new primers from Biochem stores which are on the benchtop in 209.

Yaamini’s Notebook: Gigas Broodstock RNA Extraction

C. gigas RNA and DNA extractions: Day 1

With everyone else in the lab extracting RNA, I figured I should too (#fomo…?). Over the next few days, I’ll extract RNA and DNA from frozen C. gigas tissues collected after 7 weeks of either low or ambient pH exposure in 2017. These are the adults I already extracted gonad DNA from.

I followed the ZymoResearch Quick DNA/RNA Microprep Plus Kit protocol, which is what Grace uses for her crab samples. Since Grace has been able to use it with her trickier crab hemolymph samples, I’m more confident that it should work with standard frozen oyster tissues. The kit separates out DNA and RNA into a column and flow-through, so I should be able to get both molecules from one sample. Today I’ll test out the protocol using adductor tissue samples from 20 individuals (10 low pH, 10 ambient pH). I decided to start with the adductor first since it’s the tissue I care about least. It’s not directly involved in any sort of ocean acidification acclimation processes, but could be affected. It might be a nice contrast to see how methylation, chromatin accessibility, and gene expression changes in relation to other tissues more active in ocean acidification acclimation like ctenidia and mantle tissues.

Methods: Sample Preparation

Step 1: Prepare for extractions.

  • Label 3 sets of tubes RNase-free centrifuge tubes per sample: one for frozen tissue, one for final RNA storage, and one for final DNA storage.
  • Add 96 mL of 100% ethanol to the 24 mL DNA/RNA wash buffer concentrate.
  • Add 1040 µL Proteinase K Storage Buffer to Proteinase K vial. Vortex and store at -20ºC
  • Set heat block to 55ºC
  • Obtain samples from -80ºC freezer and place in ice: I did this before I did everything else since I was preoccupied with actually locating my samples. By the time I scrounged up the rest of my materials, they were more tissue than frozen…whoops.

Step 2: Cut and weigh no 0.005 g (5 mg) of frozen tissue. Record weight of tissue used in extractions and place tissue in a new, labelled test tube.

  • I tared the scale with a piece of weigh paper. I used tweezers to remove the tissue from the tube, then a razor blade to cut the tissue. Once I got the weight I wanted, I transferred the tissue to the labelled centrifuge tube with the tweezers.
  • Tweezers and the razor blade were washed in 200 mL of a 10% bleach solution, then in two separate DI water rinses. I wiped the tools clean with a kim wipe to use again.
  • I first tried using our lab’s scale with samples 18 and 19, but I couldn’t get a reading less than 10 mg even when barely any tissue. I borrowed the scale from Graham’s lab and was able to get actual weights.

Table 1. Mass of samples used for RNA extractions.

Sample ID Mass (mg)
1-T3 4.8
2-T1 4.7
3-T1 4.5
4-T3 5.1
5-T3 5.5
6-T1 5.3
7-T2 5.4
8-T2 5.1
9-T2 5.4
10-T3 5.3
11-T4 4.8
12-T6 4.4
13-T5 4.7
14-T6 4.8
15-T5 4.5
16-T4 5.5
17-T4 4.5
18-T6 4.6
19-T5 5.3
20-T6 5.0

Step 3: Add at least 150 µL of DNA/RNA shield (2X) and 150 µL nuclease-free water to create 300 µL DNA/RNA shield (1x) to each sample. If the sample is not covered by water, add more DNA/RNA shield (1x) until covered and record the volume of liquid added.

  • I intially added 300 µL DNA/RNA shield (2x) to each sample, but I only noticed this after the samples were on the heat block…I removed samples from the heat block at 3 p.m. and added 300 µL nuclease free water to dilute the DNA/RNA shield.

Step 4: For every 300 µL of sample, add 30 µL PK Digestion Buffer and 15 µL Proteinase K. Mix by vortexing gently.

  • After adding 300 µL nuclease-free water, my sample volume had effectively doubled. I added an additional 30 µL PK Digestion Buffer and 15 µL Proteinase K, totalling 60 µL PK Digestion Buffer and 30 µL Proteinase K per sample.

Step 5: Place samples on a heat block at 55ºC for 2-5 hours.

  • Initially placed samples on the heat block at 1:55 p.m. At 3 p.m., I removed them from the heat block to correct mistakes in Step 3 and 4. I placed them back on the heat block at 3:20 p.m.

Step 6: Vortex sample and centrifuge at maximum speed for 2 minutes to pellet debris. Transfer the aqueous supernatant to an RNase-free tube.

Step 7: Add an equivalent volume of DNA/RNA Lysis Buffer to each sample and mix by vortexing.

Methods: Sample Purification

Step 8: Transfer the sample into a IC-MX spin column in a collection tube and centrifuge at 15,000 x g for 30 seconds.

  • Keep the labelled spin columns for DNA extractions. I originally thought that I would extract RNA today and place the DNA column in the fridge for later extraction, but Sam told me that yields were really poor when he ketp samples in the fridge for a week. I decided to extract both nucleic acids today.
  • Save the flow-through for RNA extractions
  • Since I had to add extra liquid (s/o to past Yaamini for not internalizing directions), I had a lot of sample. Not all of the sample fit into the spin column. I spun the first 1000 µL (about 300 µL remaining) and processed the DNA columns first. When I removed the IC-MX columns with DNA from the collection tubes with flow-through and RNA, I completely spaced and forgot to label the collection tubes. By the time I went back to process them and elute RNA, I had no idea which sample was which! I had to discard the flow-through with RNA and tubes, but thankfully I still had sample remaining that had not gone through a collection tube. Good thing I’m making all these mistakes with low stakes samples that I have so much more of.

Step 9: For RNA only, add an equal volume of 95-100% ethanol to the flow-through and mix by pipetting. Transfer the flow-through into a new IC spin column in a clean collection tube. Centrifuge at 15,000 x g for 30 seconds. Discard the flow-through.

Step 10: Add 400 µL DNA/RNA Prep Buffer to the column. Centrifuge at 15,000 x g for 30 seconds. Discard the flow-through.

Step 11: Add 700 µL DNA/RNA Wash Buffer to the column. Centrifuge at 15,000 x g for 30 seconds. Discard the flow-through.

Step 12: Add 400 µL DNA/RNA Wash Buffer to the column. Centrifuge at 15,000 x g for 2 minutes. Transfer the column to a new microcentrifuge tube.

Step 13: Add 15 µL DNase/RNase-Free Water to the column. Incubate at room temperature for 5 minutes. Centrifuge at 15,000 x g for 30 seconds to elute the RNA.

Going forward

  1. Quantify yields from today
  2. Extract and quantify ctenidia RNA and DNA
  3. Extract and quantify mantle RNA and DNA

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Yaamini’s Notebook: February 2020 Goals

feels

Jumanji understands how studying for quals works (or doesn’t work…depends on the day). What else am I planning on doing this month?!

January Goals Recap:

Virginica Gonad Methylation:

Gigas Gonad Methylation:

  • I didn’t get a chance to do a GO-MWU enrichment or DMG analysis, but I reviewed my last post on C. gigas analysis.
  • Made a labwork preparation plan! The sequencing plan is to figure out the plan later.
  • I held off on putting together an ASLO poster, but I figured out what metrics to include on the poster itself.

Virginica Sperm Methylation:

  • Didn’t tackle anything related to C. virginica sperm samples. We’re putting this on the backburner for now.

** All Mechanism Study**:

Other:

February Goals:

Quals:

  • Finish an initial read of all materials, including comprehensive notes
  • Finish second read of all materials to establish connections to topic, other topics, and my research
  • Meet with committee members as necessary to guide first and second reads

Gigas Gonad Methylation:

  • Compare C. gigas and C. virginica sequencing methods
  • Compare C. gigas and C. virginica general methylation landscape
  • Compare C. gigas and C. virginica DML lists
  • Conduct GO-MWU and DMG analysis for C. gigas DML and compare to C. virginica results
  • Put together poster for ASLO

Virginica Gonad Methylation:

  • Address reviewer comments and send to collaborators for review

All Mechanism Study:

  • Extract RNA from frozen tissue samples
  • Extract DNA from frozen tissue samples
  • Extract RNA from histology blocks

Other:

  • Review paper for ICES
  • Try not to sleep and other good stuff

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Grace’s Notebook: February Goals

Crab

  • Extract RNA from all remaining samples (Sam and Kaitlyn are going to keep helping, I think)
  • Prepare for qPCR on individual crab samples
  • Run qPCRs
  • Start working on crab paper

Oyster

  • FINISH it

Other

  • Submit MS proposal (will have it submitted by this coming week: I have comments from committee members to address, and then it will be ready)
  • Submit App for SAFS Fellowship (everything is ready: just need to have MS proposal on file)

Looking back on January

Crab

  • I submitted the 6 pooled samples on a plate to NWGC yesterday!
  • AMSS talk went well! I didn’t do any new analyses, but made my GSS talk better!

Oyster

  • didn’t touch it at all… 😦 TA work takes up quite a bit of time and brain energy, it turns out!

Other

  • Sent my MS proposal to committee members and received comments and suggestions – currently working on addressing them.

Attended and presented at AMSS and had a great time!

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