#!/bin/bash
## Job Name
#SBATCH --job-name=alan
## Allocation Definition
#SBATCH --account=srlab
#SBATCH --partition=srlab
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=6-00:00:00
## Memory per node
#SBATCH --mem=100G
#SBATCH --mail-type=ALL
#SBATCH --mail-user=sr320@uw.edu
## Specify the working directory for this job
#SBATCH --chdir=/gscratch/scrubbed/sr320/040421-alan
# Directories and programs
bismark_dir="/gscratch/srlab/programs/Bismark-0.21.0"
bowtie2_dir="/gscratch/srlab/programs/bowtie2-2.3.4.1-linux-x86_64/"
samtools="/gscratch/srlab/programs/samtools-1.9/samtools"
reads_dir="/gscratch/scrubbed/samwhite/2018OALarvae_DNAm_discovery/trimmed_files/"
genome_folder="/gscratch/srlab/sr320/data/Cgig-genome/roslin_M/"
source /gscratch/srlab/programs/scripts/paths.sh
#
# ${bismark_dir}/bismark_genome_preparation \
# --verbose \
# --parallel 28 \
# --path_to_aligner ${bowtie2_dir} \
# ${genome_folder}
#
#/zr3644_11_R2.fastp-trim.20201206.fq.gz
find ${reads_dir}*_R1_001_val_1.fq.gz \
| xargs basename -s _R1_001_val_1.fq.gz | xargs -I{} ${bismark_dir}/bismark \
--path_to_bowtie ${bowtie2_dir} \
-genome ${genome_folder} \
-p 4 \
-score_min L,0,-0.6 \
--non_directional \
-1 ${reads_dir}{}_R1_001_val_1.fq.gz \
-2 ${reads_dir}{}_R2_001_val_2.fq.gz \
find *.bam | \
xargs basename -s .bam | \
xargs -I{} ${bismark_dir}/deduplicate_bismark \
--bam \
--paired \
{}.bam
${bismark_dir}/bismark_methylation_extractor \
--bedGraph --counts --scaffolds \
--multicore 28 \
--buffer_size 75% \
*deduplicated.bam
# Bismark processing report
${bismark_dir}/bismark2report
#Bismark summary report
${bismark_dir}/bismark2summary
#run multiqc
/gscratch/srlab/programs/anaconda3/bin/multiqc .
# Sort files for methylkit and IGV
find *deduplicated.bam | \
xargs basename -s .bam | \
xargs -I{} ${samtools} \
sort --threads 28 {}.bam \
-o {}.sorted.bam
# Index sorted files for IGV
# The "-@ 16" below specifies number of CPU threads to use.
find *.sorted.bam | \
xargs basename -s .sorted.bam | \
xargs -I{} ${samtools} \
index -@ 28 {}.sorted.bam
find *deduplicated.bismark.cov.gz \
| xargs basename -s _bismark_bt2_pe.deduplicated.bismark.cov.gz \
| xargs -I{} ${bismark_dir}/coverage2cytosine \
--genome_folder ${genome_folder} \
-o {} \
--merge_CpG \
--zero_based \
{}_bismark_bt2_pe.deduplicated.bismark.cov.gz
#creating bedgraphs post merge
for f in *merged_CpG_evidence.cov
do
STEM=$(basename "${f}" .CpG_report.merged_CpG_evidence.cov)
cat "${f}" | awk -F $'\t' 'BEGIN {OFS = FS} {if ($5+$6 >= 10) {print $1, $2, $3, $4}}' \
> "${STEM}"_10x.bedgraph
done
for f in *merged_CpG_evidence.cov
do
STEM=$(basename "${f}" .CpG_report.merged_CpG_evidence.cov)
cat "${f}" | awk -F $'\t' 'BEGIN {OFS = FS} {if ($5+$6 >= 5) {print $1, $2, $3, $4}}' \
> "${STEM}"_5x.bedgraph
done
#creating tab files with raw count for glms
for f in *merged_CpG_evidence.cov
do
STEM=$(basename "${f}" .CpG_report.merged_CpG_evidence.cov)
cat "${f}" | awk -F $'\t' 'BEGIN {OFS = FS} {if ($5+$6 >= 10) {print $1, $2, $3, $4, $5, $6}}' \
> "${STEM}"_10x.tab
done
for f in *merged_CpG_evidence.cov
do
STEM=$(basename "${f}" .CpG_report.merged_CpG_evidence.cov)
cat "${f}" | awk -F $'\t' 'BEGIN {OFS = FS} {if ($5+$6 >= 5) {print $1, $2, $3, $4, $5, $6}}' \
> "${STEM}"_5x.tab
done
Category Archives: Lab Notebooks
Laura’s Notebook: Oly RNA – Data processing test-run
Things to note about the QuantSeq libraries:
- I used the QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina
- Reads are therefore stranded – generated from forward (FWD) strand
- The kit does not enrich for poly(A) or ribosomal RNA (rRNA) before 1st strand synthesis
- 1st strand synthesis occurs near the 3’ (poly(a)) end of the strand via oligodT priming. Therefore reads are more likely to contain the 3’ end of the sequence. Also, reads are less likely to contain exon junction sites, and are not randomly distributed across the mRNA sequence. Thus, data does not likely contain isoform information (does not represent the whole gene), and is more likely to appear as “duplicated” since reads are more likely to be pulled from the same start location.
- Library insert lengths averaged ~250bp, but sequencing length was 100bp. Since adapter sequences & poly-a tails can be up to ~25 total, all raw reads contain ~75-85bp of actual mRNA sequence information.
- Sequencing was performed on a NovaSeq, and was single-end.
Data processing test-run on 8 samples
I’m developing my pipeline based on 8 of the total 144 samples. Of the 8 test samples, 2 are from adult gill, 2 from juvenile whole-body, and 4 are from larval samples (sample numbers are 141, 159, 302m, 331, 43, 441, 483, and 563). Here is my bioinformatics pipeline.
NOTE: for each of STAR, Bowtie2, and Salmon I tested various combinations of settings to try to optimize alignment rate. More details on that later.
Some notes on mapping QC
- % mapped reads – higher when mapping to genome. E.g. for human samples, 70-90% of reads typically map to human genome (lower mapping rate expected for transcriptomes).
- k-mer and GC content may reveal PCR biases. k-mer and GC content varies by species/experiment, BUT should be homogenous across samples in the same experiment
- Could check if rRNA and small RNAs are present – should not be. R packages NOISeq or EDASeq provide useful plots for QC of count data.
- Could try Picard for mapping quality control. Try using the
CollectMultipleMetrics
function. This does the following:
Collect multiple classes of metrics. This ‘meta-metrics’ tool runs one or more of the metrics collection modules at the same time to cut down on the time spent reading in data from input files. Available modules include CollectAlignmentSummaryMetrics, CollectInsertSizeMetrics, QualityScoreDistribution, MeanQualityByCycle, CollectBaseDistributionByCycle, CollectGcBiasMetrics, RnaSeqMetrics, CollectSequencingArtifactMetrics, and CollectQualityYieldMetrics.
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Laura’s Notebook: Oly OA RNA isolation – raw data
Erica at the NWGC worked very quickly to get my samples sequenced on the NovaSeq. On Monday she pooled them and ran each pool on the Bioanalyzer. Here is the bioanalyzer report, and a note from Erica about the pool concentrations:
Here is the QC: I did dilute an aliquot of the pool down further to run the samples better. The original qubit pools were as follows and these were what was loaded on the bioanalyzer.
- Batch1_69plex 10.966ng/ul
- Batch2_77plex 11.649ng/ul
The diluted qubit values are:
- Batch1_69plex 2.777ng/ul
- Batch2_77plex 2.43ng/ul
Once the samples were finished on the NovaSeq, she sent me a summary of the run, and files for each pool with the # reads per sample:
Date | Lane | Lane | Concentration (pM) | SAV Clusters/mm^2 | % Clusters PF | Overall Q30 | Read 1 Q30 |
---|---|---|---|---|---|---|---|
200420 | 1 | Batch1_69plex | 270 | 506.12 | 79.29 | 89.73 | 88.93 |
2 | Batch2_77plex |
I received a link to the raw data, which is not yet demultiplexed (I will need to do that). The data is stored on Globus.org. I will need to transfer it from there to the Nightengales directory on Owl in the O. lurida folder, which is where all raw NGS data is housed for the Roberts Lab. This could be done in a few ways, but I will follow Shelly’s lead, with a few changes based on my work-at-home situation. She directed me to this notebook post, and this github issue as references. Globus endpoints are set up via a GUI, so since I am working at home I opted to tranfer the files to my external hard drive, and then to other locations.
Set up Globus personal endpoint, download data
Following these Globus setup instructions I set my laptop up as a personal endpoint on Globus. I edited my Globus settings to be able to write to my external hard drive. I then transferred the two FASTQ files with their md5 files to my external hard drive.
Mount Owl, transfer files to Nightengales
I mounted Owl on my computer using Finder –> Go –> Connect to Server. I entered owl’s address (afp://owl.fish.washington.edu), then my username and pw.
To be continued…
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Laura’s Notebook: Oly OA RNA isolation – sequencing
I’m done with my QuantSeq libraries! After getting a few quotes for sequencing, we’ve decided have the UW Genome Sciences sequencing core do it (the Sequencing Northwest Genomics Center). They also pooled my libraries for me for a small fee, but I needed to provide them with library concentrations (from QuBit). I also sent them my BioAnalyzer results (mean library length).
On Friday, April 17 I prepped my samples and walked them over to Erica at the genome science center. Here’s what needed to be done:
Quantified a few more libraries using Qubit.
I had to quantify my juvenile O. lurida libraries, and a couple random libraries that I previously omitted. I used the Qubit 1X dsDNA HS Assay.
Transerred libraries to new PCR plates
I submitted 8 uL of each library, which is ~half of my volumes (started with ~16 uL). I also wanted to make sure the sequencing facility would correctly pool my libraries in two batches – 1) Ctenidia+Juvenile and 2) Larvae. This is because some of my index numbers were the same in Batch 1 as in Batch 2, so they needed to be run on separate lanes. To make this very clear, I prepared 2 PCR plates, one for each batch/lane. Also, because I re-prepped a few libraries, I needed to carefully identify which to transfer, and which to omit.
Library details
Below are details for the libraries I submitted, with information such as original RNA concentration used for library prep, # PCR cycles used to amplify libraries, library concentration (DNA, from Qubit), mean library length (for a few), index number. All this information is located in this Sample-Submission-and-Info.xlsx spreadsheet.
Here is the manifest that I sent to Erica: Spencer,LH_Sample-Manifest-2020-04-17.xlsx
Here is the sample submission sheet: Spencer,LH_Sample-Submission-Form-2020-04-17.pdf
A note on PhiX Spike-in:
Erica asked whether I would like PhiX spike-in. Standard protocol for any Illumina platform is to do a 1% spike-in for quality control. The QuantSeq library prep manual does not specify whether it is recommended for my library type, although it does specify for QuantSeq libraries prepped using an add-on module which tack on UMIs to each library. I eailed my QuantSeq rep, and they replied quickly with the following: “For standard QuantSeq libraries without UMIs, 1% is fine. The reason libraries with UMIs need more is due to the spacer we use with the UMIs. It has a unique fingerprint that requires a more PhiX. That is the only reason. Even when labs combine non-UMI libraries with UMI libraries, they no longer need the higher percentage of PhiX. It is for UMI only libraries in a flow cell lane. It is rather specific.” So, in the end I had them use a 1% PhiX spike-in.
Adult O. lurida ctenidia samples, multiple populations and pCO2 exposures
Plate/Batch/Lane # | Well | Sample No. | [RNA] (ng/ul) | Cycles, round down (for most) | Tissue source | Tissue type | [DNA] (ng/uL) | Bioanalyzer mean bp | INDEX # | Index bp sequence | Population | Parental pCO2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | D06 | 291 | 158.0 | 15 | Adult | ctenidia | 3.54 | NT | 7035 | GTTACC | Dabob Bay | High |
1 | C08 | 292 | 29.6 | 15 | Adult | ctenidia | 1.32 | NT | 7037 | TGGCGA | Dabob Bay | High |
1 | A11 | 293 | 39.6 | 16 | Adult | ctenidia | 1.03 | NT | 7014 | AATCCG | Dabob Bay | High |
1 | A07 | 294 | 110.0 | 16 | Adult | ctenidia | 1.95 | 245 | 7008 | TGTGCA | Dabob Bay | High |
1 | D05 | 295 | 34.8 | 15 | Adult | ctenidia | 3.58 | NT | 7050 | TCGAGG | Dabob Bay | High |
1 | D04 | 296 | 180.0 | 15 | Adult | ctenidia | 3.46 | NT | 7046 | CTCCAT | Dabob Bay | High |
1 | C01 | 298 | 182.0 | 15 | Adult | ctenidia | 3.08 | NT | 7028 | GCTCGA | Dabob Bay | High |
1 | B09 | 299 | 50.4 | 15 | Adult | ctenidia | 2.46 | NT | 7024 | CCGCAA | Dabob Bay | High |
1 | B10 | 301 | 75.8 | 15 | Adult | ctenidia | 2.82 | NT | 7025 | TTTATG | Dabob Bay | Ambient |
1 | B03 | 302 | 62.4 | 14 | Adult | ctenidia | 4.9 | NT | 7018 | GTCAGG | Dabob Bay | Ambient |
1 | B05 | 303 | 95.2 | 14 | Adult | ctenidia | 2.4 | NT | 7020 | TATGTC | Dabob Bay | Ambient |
1 | B12 | 304 | 200.0 | 15 | Adult | ctenidia | 2.8 | NT | 7027 | CAAGCA | Dabob Bay | Ambient |
1 | A01 | 305 | 75.2 | 16 | Adult | ctenidia | 2.96 | 267 | 7002 | GATCAC | Dabob Bay | Ambient |
1 | D08 | 306 | 136.0 | 15 | Adult | ctenidia | 4.14 | NT | 7051 | CACTAA | Dabob Bay | Ambient |
1 | D09 | 307 | 89.4 | 15 | Adult | ctenidia | 3.32 | NT | 7053 | CGCCTG | Dabob Bay | Ambient |
1 | A05 | 308 | 73.6 | 16 | Adult | ctenidia | 2.48 | 256 | 7006 | GTGTAG | Dabob Bay | Ambient |
1 | B08 | 309 | 170.0 | 14 | Adult | ctenidia | 1.46 | NT | 7023 | CACACT | Dabob Bay | Ambient |
1 | A02 | 311 | 158.0 | 16 | Adult | ctenidia | 3.76 | 262 | 7003 | ACCAGT | Oyster Bay C1 | High |
1 | C04 | 312 | 90.6 | 15 | Adult | ctenidia | 1.58 | NT | 7032 | CGAAGG | Oyster Bay C1 | High |
1 | C12 | 313 | 72.4 | 15 | Adult | ctenidia | 1.81 | NT | 7041 | CTCTCG | Oyster Bay C1 | High |
1 | E01 | 314 | 42.2 | 17 | Adult | ctenidia | 4.32 | NT | 7049 | GTGCCA | Oyster Bay C1 | High |
1 | C02 | 315 | 148.0 | 15 | Adult | ctenidia | 2.32 | NT | 7029 | GCGAAT | Oyster Bay C1 | High |
1 | A09 | 316 | 146.0 | 16 | Adult | ctenidia | 3.74 | 252 | 7012 | ATGAAC | Oyster Bay C1 | High |
1 | C09 | 317 | 158.0 | 15 | Adult | ctenidia | 2.14 | NT | 7038 | ACCGTG | Oyster Bay C1 | High |
1 | A06 | 318 | 174.0 | 16 | Adult | ctenidia | 4.08 | 262 | 7007 | CTAGTC | Oyster Bay C1 | High |
1 | E04 | 319 | 77.6 | 17 | Adult | ctenidia | 3.02 | NT | 7011 | TTAACT | Oyster Bay C1 | High |
1 | C05 | 321 | 148.0 | 15 | Adult | ctenidia | 3.64 | NT | 7033 | AGATAG | Oyster Bay C1 | Ambient |
1 | A08 | 322 | 44.6 | 16 | Adult | ctenidia | 2.7 | 269 | 7010 | CGGTTA | Oyster Bay C1 | Ambient |
1 | D03 | 323 | 102.0 | 15 | Adult | ctenidia | 3.7 | NT | 7044 | ACAGAT | Oyster Bay C1 | Ambient |
1 | D11 | 324 | 172.0 | 16 | Adult | ctenidia | 2.84 | NT | 7009 | TCAGGA | Oyster Bay C1 | Ambient |
1 | B01 | 325 | 180.0 | 14 | Adult | ctenidia | 2.92 | NT | 7016 | TACCTT | Oyster Bay C1 | Ambient |
1 | D02 | 326 | 130.0 | 15 | Adult | ctenidia | 4.04 | NT | 7043 | AAGACA | Oyster Bay C1 | Ambient |
1 | D01 | 327 | 85.2 | 15 | Adult | ctenidia | 1.97 | NT | 7042 | TGACAC | Oyster Bay C1 | Ambient |
1 | A12 | 328 | 156.0 | 14 | Adult | ctenidia | 2.12 | NT | 7015 | GGCTGC | Oyster Bay C1 | Ambient |
1 | D10 | 329 | 162.0 | 16 | Adult | ctenidia | 5.1 | NT | 7045 | TAGGCT | Oyster Bay C1 | Ambient |
1 | E05 | 331 | 42.2 | 19 | Adult | ctenidia | 9.6 | NT | 7001 | CAGCGT | Fidalgo Bay | High |
1 | D12 | 332 | 65.8 | 16 | Adult | ctenidia | 2.14 | NT | 7047 | GCATGG | Fidalgo Bay | High |
1 | C06 | 333 | 78.6 | 15 | Adult | ctenidia | 2.98 | NT | 7034 | TTGGTA | Fidalgo Bay | High |
1 | E02 | 334 | 64.8 | 17 | Adult | ctenidia | 3.92 | NT | 7048 | AATAGC | Fidalgo Bay | High |
1 | C07 | 335 | 180.0 | 15 | Adult | ctenidia | 2.58 | NT | 7036 | CGCAAC | Fidalgo Bay | High |
1 | E03 | 336 | 94.8 | 17 | Adult | ctenidia | 4.6 | NT | 7052 | GGTATA | Fidalgo Bay | High |
1 | C11 | 337 | 194.0 | 15 | Adult | ctenidia | 4.02 | NT | 7040 | GATTGT | Fidalgo Bay | High |
1 | A04 | 338 | 81.6 | 16 | Adult | ctenidia | 2.74 | 269 | 7005 | ACATTA | Fidalgo Bay | High |
1 | A10 | 339 | 77.2 | 16 | Adult | ctenidia | 1.91 | NT | 7013 | CCTAAG | Fidalgo Bay | High |
1 | B07 | 341 | 89.6 | 14 | Adult | ctenidia | 1.31 | NT | 7022 | GGAGGT | Fidalgo Bay | Ambient |
1 | B11 | 342 | 162.0 | 15 | Adult | ctenidia | 1.78 | NT | 7026 | AACGCC | Fidalgo Bay | Ambient |
1 | B02 | 343 | 114.0 | 14 | Adult | ctenidia | 2.58 | NT | 7017 | TCTTAA | Fidalgo Bay | Ambient |
1 | C03 | 344 | 25.0 | 15 | Adult | ctenidia | 1.58 | NT | 7030 | TGGATT | Fidalgo Bay | Ambient |
1 | B04 | 345 | 190.0 | 14 | Adult | ctenidia | 3.5 | NT | 7019 | ATACTG | Fidalgo Bay | Ambient |
1 | B06 | 346 | 43.6 | 14 | Adult | ctenidia | 1.36 | NT | 7021 | GAGTCC | Fidalgo Bay | Ambient |
1 | D07 | 347 | 69.0 | 15 | Adult | ctenidia | 4.36 | NT | 7031 | ACCTAC | Fidalgo Bay | Ambient |
1 | A03 | 348 | 54.4 | 16 | Adult | ctenidia | 3.06 | 265 | 7004 | TGCACG | Fidalgo Bay | Ambient |
1 | C10 | 349 | 82.0 | 15 | Adult | ctenidia | 1.9 | NT | 7039 | CAACAG | Fidalgo Bay | Ambient |
Juvenile O. lurida whole-body samples from those deployed in Port Gamble, with two populations and two parental pCO2 exposures
Plate/Batch/Lane # | Well | Sample No. | [RNA] (ng/ul) | Cycles, round down (for most) | Tissue source | Tissue type | [DNA] (ng/uL) | Bioanalyzer mean bp | INDEX # | Index bp sequence | Population | Parental pCO2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | G02 | 137 | 99.2 | 15 | Juvenile | whole body, individual | 5.2 | NT | 7090 | CCTGCT | Dabob Bay | Ambient |
1 | F01 | 139 | 136.0 | 14 | Juvenile | whole body, individual | 13.3 | 294 | 7081 | GCAGCC | Dabob Bay | Ambient |
1 | G03 | 140 | 174.0 | 15 | Juvenile | whole body, individual | 7.62 | 314 | 7091 | GCGCTG | Dabob Bay | Ambient |
1 | F08 | 141 | 190.0 | 15 | Juvenile | whole body, individual | 23.6 | NT | 7088 | AGACCA | Dabob Bay | Ambient |
1 | G05 | 156 | 110.0 | 16 | Juvenile | whole body, individual | 4.32 | NT | 7093 | TTCGAG | Fidalgo Bay | Ambient |
1 | G07 | 159 | 184.0 | 16 | Juvenile | whole body, individual | 23 | 283 | 7095 | AGGCAT | Fidalgo Bay | Ambient |
1 | F04 | 161 | 168.0 | 14 | Juvenile | whole body, individual | 6.22 | 290 | 7084 | AAGTGG | Fidalgo Bay | Ambient |
1 | G01 | 162 | 164 | 15 | Juvenile | whole body, individual | 6.74 | 289 | 7080 | AACAAG | Fidalgo Bay | Ambient |
1 | G06 | 168 | 89.0 | 16 | Juvenile | whole body, individual | 15.9 | 305 | 7094 | AGAATC | Dabob Bay | High |
1 | G04 | 169 | 95.6 | 16 | Juvenile | whole body, individual | 3.68 | 310 | 7092 | GAACCT | Dabob Bay | High |
1 | F05 | 171 | 146.0 | 15 | Juvenile | whole body, individual | 3.22 | 305 | 7085 | CTCATA | Dabob Bay | High |
1 | F06 | 172 | 116.0 | 15 | Juvenile | whole body, individual | 10.6 | NT | 7086 | CCGACC | Dabob Bay | High |
1 | G08 | 181 | 138.0 | 16 | Juvenile | whole body, individual | 2.84 | 280 | 7096 | ACACGC | Fidalgo Bay | High |
1 | F03 | 183 | 196.0 | 14 | Juvenile | whole body, individual | 5.88 | NT | 7083 | TGCTAT | Fidalgo Bay | High |
1 | F02 | 184 | 96.4 | 14 | Juvenile | whole body, individual | 6.1 | NT | 7082 | ACTCTT | Fidalgo Bay | High |
1 | F07 | 185 | 102.0 | 15 | Juvenile | whole body, individual | 15 | 296 | 7087 | GGCCAA | Fidalgo Bay | High |
Larval O. lurida, multiple cohorts, parental pCO2 and temperature exposures. Larvae are pooled by pulse/family.
Plate/Batch/Lane # | Well | Sample No. | [RNA] (ng/ul) | Vol RNA used | Cycles, round down (for most) | Tissue source | Tissue type | [DNA] (ng/uL) | Bioanalyzer mean bp | INDEX # | Index bp sequence | Population | Parental Temperature | Parental pCO2 | Larval sample # |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | F08 | 34 | 104.0 | 3.37 | 19 | Larval | whole body, pooled | 3.8 | NT | 7073 | GACATC | Oyster Bay C1 | 6 | Ambient | 77-A |
2 | F12 | 35 | 94.4 | 3.71 | 21 | Larval | whole body, pooled | 0.522 | NT | 7075 | CGTCGC | Oyster Bay C1 | 6 | Ambient | 10-A |
2 | F10 | 37 | 74.6 | 4.69 | 21 | Larval | whole body, pooled | 5.46 | NT | 7076 | ATGGCG | Oyster Bay C1 | 6 | Ambient | 69-A |
2 | E04 | 39 | 89.8 | 3.90 | 15 | Larval | whole body, pooled | 2.04 | NT | 7056 | ATATCC | Oyster Bay C1 | 6 | Ambient | 48-A |
2 | F02 | 41 | 108.0 | 3.24 | 19 | Larval | whole body, pooled | 6.82 | NT | 7068 | CCAATT | Oyster Bay C1 | 10 | High | 06-A |
2 | D10 | 43 | 156.0 | 2.24 | 15 | Larval | whole body, pooled | 2.48 | 261 | 7050 | TCGAGG | Oyster Bay C1 | 10 | High | 08-A |
2 | F11 | 44 | 41.4 | 5.00 | 21 | Larval | whole body, pooled | 0.18 | NT | 7078 | GCCACA | Oyster Bay C1 | 10 | High | 32-A |
2 | F03 | 45 | 25.1 | 5 | 19 | Larval | whole body, pooled | 4.8 | NT | 7077 | ATTGGT | Oyster Bay C1 | 10 | High | 79-A |
2 | F06 | 46 | 98.6 | 3.55 | 19 | Larval | whole body, pooled | 4.52 | NT | 7074 | CGATCT | Oyster Bay C1 | 10 | High | 24-A |
2 | E10 | 47 | 128.0 | 2.73 | 16 | Larval | whole body, pooled | 2.58 | NT | 7070 | AACCGA | Oyster Bay C1 | 10 | High | 26-A |
2 | A10 | 401 | 93.4 | 3.75 | 16 | Larval | whole body, pooled | 1.24 | 325 | 7012 | ATGAAC | Dabob Bay | 10 | Ambient | 14-A |
2 | A05 | 402 | 114.0 | 3.07 | 16 | Larval | whole body, pooled | 2.76 | 346 | 7006 | GTGTAG | Dabob Bay | 10 | Ambient | 31-A |
2 | C06 | 403 | 136.0 | 2.57 | 14 | Larval | whole body, pooled | 4.16 | NT | 7032 | CGAAGG | Dabob Bay | 10 | Ambient | 75-A |
2 | D06 | 404 | 112.0 | 3.13 | 15 | Larval | whole body, pooled | 2.92 | NT | 7046 | CTCCAT | Dabob Bay | 10 | Ambient | 80-A |
2 | A09 | 411 | 72.6 | 4.82 | 16 | Larval | whole body, pooled | 3.08 | 288 | 7011 | TTAACT | Dabob Bay | 10 | High | 23-A |
2 | G02 | 412 | 31.2 | 5.00 | 23 | Larval | whole body, pooled | 2.52 | NT | 7069 | AGTTGA | Dabob Bay | 10 | High | 27-A |
2 | D02 | 413 | 130.0 | 2.69 | 15 | Larval | whole body, pooled | 1.97 | NT | 7041 | CTCTCG | Dabob Bay | 10 | High | 58-A |
2 | E01 | 414 | 168.0 | 2.08 | 15 | Larval | whole body, pooled | 0.858 | NT | 7053 | CGCCTG | Dabob Bay | 10 | High | 60-A |
2 | B10 | 421 | 57.6 | 5.00 | 14 | Larval | whole body, pooled | 1.63 | NT | 7024 | CCGCAA | Dabob Bay | 6 | Ambient | 59-A |
2 | G03 | 432 | 74.0 | 4.73 | 23 | Larval | whole body, pooled | 6.28 | NT | 7065 | AAGCTC | Dabob Bay | 6 | High | 72-A |
2 | E07 | 434 | 162.0 | 2.16 | 15 | Larval | whole body, pooled | 5.58 | NT | 7059 | GGTGAG | Dabob Bay | 6 | High | 74-A |
2 | G01 | 441 | 16.2 | 5.00 | 23 | Larval | whole body, pooled | 0.866 | NT | 7061 | GAAGTG | Fidalgo Bay | 10 | Ambient | 20-A |
2 | C12 | 443 | 60.2 | 5.00 | 15 | Larval | whole body, pooled | 1.36 | NT | 7039 | CAACAG | Fidalgo Bay | 10 | Ambient | 53-A |
2 | C10 | 444 | 70.6 | 4.96 | 14 | Larval | whole body, pooled | 4.08 | NT | 7037 | TGGCGA | Fidalgo Bay | 10 | Ambient | 63-A |
2 | D09 | 445 | 160.0 | 2.19 | 15 | Larval | whole body, pooled | 4 | NT | 7049 | GTGCCA | Fidalgo Bay | 10 | Ambient | 65-A |
2 | C04 | 451 | 68.4 | 5.12 | 14 | Larval | whole body, pooled | 2.24 | NT | 7030 | TGGATT | Fidalgo Bay | 10 | High | 16-A |
2 | A12 | 453 | 196.0 | 1.79 | 14 | Larval | whole body, pooled | 0.968 | NT | 7014 | AATCCG | Fidalgo Bay | 10 | High | 36-A |
2 | B08 | 473 | 124.0 | 2.82 | 14 | Larval | whole body, pooled | 2.34 | NT | 7022 | GGAGGT | Fidalgo Bay | 6 | High | 46-A |
2 | B02 | 474 | 77.2 | 4.53 | 14 | Larval | whole body, pooled | 3.02 | NT | 7016 | TACCTT | Fidalgo Bay | 6 | High | 47-A |
2 | B04 | 475 | 27.4 | 5.00 | 14 | Larval | whole body, pooled | 2.44 | NT | 7018 | GTCAGG | Fidalgo Bay | 6 | High | 50-A |
2 | B05 | 476 | 39.2 | 5.00 | 14 | Larval | whole body, pooled | 4.24 | NT | 7019 | ATACTG | Fidalgo Bay | 6 | High | 54-A |
2 | D01 | 477 | 164.0 | 2.13 | 15 | Larval | whole body, pooled | 2.28 | NT | 7040 | GATTGT | Fidalgo Bay | 6 | High | 76-A |
2 | E09 | 481 | 89.2 | 3.92 | 15 | Larval | whole body, pooled | 2.18 | NT | 7036 | CGCAAC | Oyster Bay C1 | 10 | Ambient | 02-A |
2 | C01 | 482 | 22.2 | 5.00 | 14 | Larval | whole body, pooled | 1.42 | NT | 7027 | CAAGCA | Oyster Bay C1 | 10 | Ambient | 04-A |
2 | A07 | 483 | 99.0 | 3.54 | 16 | Larval | whole body, pooled | 1.22 | 219 | 7009 | TCAGGA | Oyster Bay C1 | 10 | Ambient | 03-A |
2 | D07 | 484 | 58.4 | 5.00 | 15 | Larval | whole body, pooled | 1.67 | NT | 7047 | GCATGG | Oyster Bay C1 | 10 | Ambient | 09-A |
2 | B09 | 485 | 19.1 | 5.00 | 14 | Larval | whole body, pooled | 2.9 | NT | 7023 | CACACT | Oyster Bay C1 | 10 | Ambient | 34-A |
2 | A06 | 487 | 118.0 | 2.97 | 16 | Larval | whole body, pooled | 1.27 | 290 | 7007 | CTAGTC | Oyster Bay C1 | 10 | Ambient | 40-A |
2 | C02 | 488 | 60.0 | 5.00 | 14 | Larval | whole body, pooled | 3.06 | NT | 7028 | GCTCGA | Oyster Bay C1 | 10 | Ambient | 44-A |
2 | C11 | 489 | 68.0 | 5.00 | 15 | Larval | whole body, pooled | 3.84 | 272 | 7038 | ACCGTG | Oyster Bay C1 | 10 | Ambient | 49-A |
2 | B07 | 490 | 186.0 | 1.88 | 14 | Larval | whole body, pooled | 2.32 | NT | 7021 | GAGTCC | Oyster Bay C1 | 10 | Ambient | 64-A |
2 | E02 | 491 | 58.4 | 5.00 | 15 | Larval | whole body, pooled | 6.64 | NT | 7054 | AATGAA | Oyster Bay C1 | 10 | Ambient | 66-A |
2 | D04 | 492 | 82.6 | 4.24 | 15 | Larval | whole body, pooled | 2.02 | NT | 7043 | AAGACA | Oyster Bay C1 | 10 | Ambient | 81-A |
2 | D11 | 506 | 29.2 | 5.00 | 15 | Larval | whole body, pooled | 1.63 | NT | 7051 | CACTAA | Oyster Bay C1 | 10 | High | 62-A |
2 | E08 | 513 | 142.0 | 2.46 | 15 | Larval | whole body, pooled | 5.92 | NT | 7060 | TTCCGC | Oyster Bay C1 | 6 | Ambient | 45-A |
2 | F05 | 521 | 66.6 | 5.00 | 19 | Larval | whole body, pooled | 8.66 | NT | 7071 | CAGATG | Oyster Bay C1 | 6 | High | 01-A |
2 | A01 | 522 | 32.2 | 5.00 | 16 | Larval | whole body, pooled | 2.42 | NT | 7002 | GATCAC | Oyster Bay C1 | 6 | High | 07-A |
2 | A04 | 523 | 71.4 | 4.90 | 16 | Larval | whole body, pooled | 2.16 | 276 | 7005 | ACATTA | Oyster Bay C1 | 6 | High | 25-A |
2 | A11 | 524 | 63.0 | 5.00 | 14 | Larval | whole body, pooled | 3.24 | NT | 7013 | CCTAAG | Oyster Bay C1 | 6 | High | 28-A |
2 | B06 | 525 | 140.0 | 2.50 | 14 | Larval | whole body, pooled | 2.44 | NT | 7020 | TATGTC | Oyster Bay C1 | 6 | High | 30-A |
2 | B03 | 526 | 138.0 | 2.54 | 14 | Larval | whole body, pooled | 2.2 | NT | 7017 | TCTTAA | Oyster Bay C1 | 6 | High | 33-A |
2 | D03 | 527 | 124.0 | 2.82 | 15 | Larval | whole body, pooled | 1.42 | 243 | 7042 | TGACAC | Oyster Bay C1 | 6 | High | 61-A |
2 | C03 | 528 | 87.6 | 4.00 | 14 | Larval | whole body, pooled | 4.48 | NT | 7029 | GCGAAT | Oyster Bay C1 | 6 | High | 68-A |
2 | E05 | 529 | 56.8 | 5.00 | 15 | Larval | whole body, pooled | 2.86 | NT | 7057 | AGTACT | Oyster Bay C1 | 6 | High | 70-A |
2 | D08 | 531 | 95.4 | 3.67 | 15 | Larval | whole body, pooled | 2.1 | NT | 7048 | AATAGC | Oyster Bay C2 | 10 | Ambient | 17-A |
2 | C09 | 532 | 93.6 | 3.74 | 14 | Larval | whole body, pooled | 2.12 | NT | 7035 | GTTACC | Oyster Bay C2 | 10 | Ambient | 42-A |
2 | F01 | 533 | 6.5 | 5.00 | 17 | Larval | whole body, pooled | 5.36 | NT | 7063 | ACGTCT | Oyster Bay C2 | 10 | Ambient | 56-A |
2 | D05 | 541 | 44.4 | 5.00 | 15 | Larval | whole body, pooled | 1.4 | NT | 7044 | ACAGAT | Oyster Bay C2 | 10 | High | 12-A |
2 | A03 | 542 | 32.8 | 5.00 | 16 | Larval | whole body, pooled | 2.22 | 270 | 7004 | TGCACG | Oyster Bay C2 | 10 | High | 13-A |
2 | C05 | 543 | 162.0 | 2.16 | 14 | Larval | whole body, pooled | 5.24 | NT | 7031 | ACCTAC | Oyster Bay C2 | 10 | High | 43-A |
2 | B12 | 551 | 96.4 | 3.63 | 14 | Larval | whole body, pooled | 1.69 | NT | 7026 | AACGCC | Oyster Bay C2 | 6 | Ambient | 35-A |
2 | B11 | 553 | 186.0 | 1.88 | 14 | Larval | whole body, pooled | 3.28 | NT | 7025 | TTTATG | Oyster Bay C2 | 6 | Ambient | 55-A |
2 | B01 | 554 | 188.0 | 1.86 | 14 | Larval | whole body, pooled | 2.38 | NT | 7015 | GGCTGC | Oyster Bay C2 | 6 | Ambient | 78-A |
2 | F09 | 561 | 28.0 | 5.00 | 21 | Larval | whole body, pooled | 4.1 | NT | 7066 | GACGAT | Oyster Bay C2 | 6 | High | 05-A |
2 | G05 | 562 | 126 | 2.777778 | 23 | Larval | whole body, pooled | 9.88 | NT | 7064 | CAGGAC | Oyster Bay C2 | 6 | High | 11-A |
2 | E11 | 563 | 47.0 | 5.00 | 16 | Larval | whole body, pooled | 4.66 | NT | 7045 | TAGGCT | Oyster Bay C2 | 6 | High | 15-A |
2 | C08 | 564 | 152.0 | 2.30 | 14 | Larval | whole body, pooled | 2.24 | NT | 7034 | TTGGTA | Oyster Bay C2 | 6 | High | 52-A |
2 | F07 | 565 | 31.2 | 5.00 | 19 | Larval | whole body, pooled | 3.2 | NT | 7067 | TCGTTC | Oyster Bay C2 | 6 | High | 57-A |
2 | G04 | 571 | LOW | 5.00 | 23 | Larval | whole body, pooled | 0.232 | 287 | 7072 | GTAGAA | Negative control | NA | NA | NA |
2 | A08 | 431b | 83.0 | 4.22 | 16 | Larval | whole body, pooled | 3.36 | 285 | 7010 | CGGTTA | Dabob Bay | 6 | High | 51-A |
2 | E12 | 442b | 69.8 | 5.00 | 17 | Larval | whole body, pooled | 3.2 | NT | 7062 | CAATGC | Fidalgo Bay | 10 | Ambient | 38-A |
2 | A02 | 452b | 97.2 | 3.60 | 16 | Larval | whole body, pooled | 2.68 | 294 | 7003 | ACCAGT | Fidalgo Bay | 10 | High | 18-A |
2 | C07 | 461b | 84.0 | 4.17 | 14 | Larval | whole body, pooled | 2.02 | NT | 7033 | AGATAG | Fidalgo Bay | 6 | Ambient | 22-A |
2 | F04 | 462b | 106 | 3.301887 | 19 | Larval | whole body, pooled | 8.46 | NT | 7001 | CAGCGT | Fidalgo Bay | 6 | Ambient | 29-A |
2 | E03 | 471b | 108.0 | 3.24 | 15 | Larval | whole body, pooled | 5.2 | NT | 7055 | ACAACG | Fidalgo Bay | 6 | High | 19-A |
2 | D12 | 472b | 97.0 | 3.61 | 15 | Larval | whole body, pooled | 2.42 | NT | 7052 | GGTATA | Fidalgo Bay | 6 | High | 21-A |
2 | E06 | 552b | 74.6 | 4.69 | 15 | Larval | whole body, pooled | 1.84 | NT | 7058 | ATAAGA | Oyster Bay C2 | 6 | Ambient | 41-A |
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Laura’s Notebook: April 23, 2020 – Oly methylation characterization
Met with Katherine and Steven a couple weeks ago and updated them on my DML, DMG, and size-associated loci (SAL) analyses. I am testing out using RMarkdown to write my results, so check out this notebook entry for a summary of these activities:
One thing I was missing was a general characterization of O. lurida methylation patterns. This is what I tackled the past couple days. To do so, I merged methylation data from all 18 samples (18 .bam files) into one .bam files, and ran that through MethylKit for some quick summary stats, then called methylation status using 50% threshold, filtered for 5x, and annotated using the O. lurida feature files. This is all posted in my RMarkdown notebook, 01b-General-Methylation-Patterns.html.
Here are barplots showing the % of methylated loci that overlap with genome features; note that I should also include a bar showing where all CpG loci are:
One interesting observation from this analysis is that our filtering is excluding most of the 0% methylated loci. Check out the below % methylation frequency plots:
- When all samples are combined into one file, and we filter for only 2x coverage, we get a typical peak at 0% methylation. We also see a small peak at 50% methylation, probably b/c there are a lot of loci with 2x coverage, and 1 of the 2 reads was methylated.
- When we filter for 5x coverage (again, with all samples merged), the 0% methylation peak dramatically reduces.
- When filtered for 10x coverage, the 0% peak nearly disappears.
This explains why we don’t have much 0% methylation in our data – low coverage for un-methylated loci. This may mean that our analyses omits loci where methylation is substantially different between populations (i.e. 100% vs. 0% methylated). But, can we infer that low-coverage is a result of low methylation, or just poor coverage? Will dig into this further.
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Laura’s Notebook: Oly OA RNA isolation – juvenile ctenidia
I’m done with my adult ctenidia & larvae libraries, and have enough kit leftover for ~17 more samples. I’ve decided to prep a few of my juvenile samples, which were collected at the end of the summer deployment. It could be very interesting to assess differences in juveniles and whether they are similar to those observed in the parents that were directly exposed.
I’m doing the whole body samples collected from the Hood Canal and Fidalgo Bay populations after they were deployed in Port Gamble Bay. I have n=4 per population and parental pH treatment (high or ambient). I had wanted to do ctenidia tissues, but then I checked out the frozen samples and noticed that they definitely are not just gill – lots of mantle tissue in there too. Therefore, I decided to do whole-body samples to try to standardize the tissue type.
Step 1: Homogenize tissue (March 6th, 2020)
Need: LN, dry ice, bleach, DI water, mortar + pestle, metal spatulas
- Added 1mL RNAzol to 1.5 mL microcentrifuge tubes.
- Cleaned mortars, pestles, and metal spatulas. Did this by cleaning under hot water, soaking in 10% bleach/DI solution for a minimum of 10 minutes, rinsing thoroughly with DI water, then rinsing with 190 proof ethanol and letting dry.
- Put tubes with RNAzol on scale. Ground tissues to powder, scraped with metal spatula and carefully transferred powder to tube. Added approximately 50 mg.
- I did 8 samples at a time (the # of mortar+pestle kits I have), then cleaned and repeated with another 8.
Step 2: RNA isolation (March 7th, 2020)
Need: RNAzol, DEPC-treated water, isopropanol, 200-proof ethanol, 1.7 mL tubes
Followed the RNAzol® RT RNA Isolation Reagent protocol for Total RNA isolation, using half of my homogenate, so 500 uL.
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Laura’s Notebook: February 2020 goals
Yikes, it’s been a few months …
- Finish QuantSeq libraries – last step is to process some deployed juvnile Olys (RNA isolation, library prep)
- Coordinate sequencing – UW or UMinnesota?
- DMG and DMR analysis on Oly methylation data. Make sure that I’ve controlled for genotype (i.e. differences aren’t due to presence/absence of certain genes/loci) – does the filtering accomplish this?
- Prepare and deliver presentation at Aquaculture America 2020
- Revise Oly Temp/Food draft, and rough draft of introduction
- Submit Polydora paper to Aquaculture Research
- Meet OA/Reproduction deadlines
Also … Met with Krista – we are a go on the internship. I will get my hands on the data as soon as it’s ready (April?). She supports me doing the NSF INTERN in Fall/Winter, so should continue pursuing that. Need to have full analysis of data by November at latest.
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Laura’s Notebook: Oly DMG analysis, Jan. 30th, 2020
Today I identified 46 differentially methylated genes among two Olympia oyster populations, Hood Canal and South Sound. This was performed using a binomial GLM and Chi-square tests. The script was adapted from Hollie Putnam’s script (/hputnam/Geoduck_Meth/master/RAnalysis/Scripts/GM.Rmd), which may have been adopted from the Lieu et al. 2018 paper .
The analysis was performed in a RMarkdown notebook, please see that here: 09-DMG-analysis
Here are the GO terms associated with genes of known function. Some notes:
– 18 out of the 46 genes were annotated with GO terms
– 9 out of the 46 genes were annotated but did not have associated GO terms (may have to find those manually …)
– 19 out of the 46 genes were of unknown function
term ID | description | frequency | pin? | log10 p-value | uniqueness | dispensability |
---|---|---|---|---|---|---|
GO:0006468 | protein phosphorylation | 4.137 % | -3.7877 | 0.40 | 0.00 | |
GO:0006807 | nitrogen compound metabolic process | 38.744 % | -2.2764 | 0.78 | 0.03 | |
GO:0006207 | ‘de novo’ pyrimidine nucleobase biosynthetic process | 0.192 % | -2.2764 | 0.46 | 0.06 | |
GO:0006281 | DNA repair | 2.234 % | -2.4853 | 0.50 | 0.20 | |
GO:0006030 | chitin metabolic process | 0.077 % | -1.6311 | 0.49 | 0.21 | |
GO:0006520 | cellular amino acid metabolic process | 5.591 % | -2.2764 | 0.42 | 0.35 | |
GO:0006412 | translation | 5.686 % | -2.4853 | 0.28 | 0.55 | |
GO:0016567 | protein ubiquitination | 0.523 % | -1.4336 | 0.44 | 0.56 |
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Laura’s Notebook: QuantSeq – inventory of finished libraries
Library prep is complete! However, I will need to redo many 😦 Based on Bioanalyzer results, any libraries with concentration <1.0 ng/uL will need to be re-done. Out of the total 132 samples that I prepped, 34 had concentrations that were LOW or <1.0 ng/uL (according to Qubit). That’s about a 25% incompletion rate. Here is the final inventory.
Next step – redo the 34 samples. Need to decide whether I should re-isolate RNA, or just try to re-do the library generation. This is a question for lab meeting tomorrow.
Ctenidia
Cohort | pCO2 | HOMOGENATE TUBE # | TISSUE TYPE | TISSUE SAMPLE # | [RNA] after Dnase treatment (ng/uL) | End Point PCR cycles | ds cDNA concentration |
---|---|---|---|---|---|---|---|
Dabob Bay | High | 291 | CTENIDIA | HL10-10 | 158.0 | 15 | LOW |
Dabob Bay | High | 292 | CTENIDIA | HL10-11 | 29.6 | 15 | 1.32 |
Dabob Bay | High | 293 | CTENIDIA | HL10-12 | 39.6 | 16 | 1.03 |
Dabob Bay | High | 294 | CTENIDIA | HL6-10 | 110.0 | 16 | 1.95 |
Dabob Bay | High | 295 | CTENIDIA | HL6-11 | 34.8 | 18 | LOW |
Dabob Bay | High | 296 | CTENIDIA | HL6-12 | 180.0 | 15 | 3.46 |
Dabob Bay | High | 298 | CTENIDIA | HL6-14 | 182.0 | 15 | 3.08 |
Dabob Bay | High | 299 | CTENIDIA | HL6-15 | 50.4 | 15 | 2.46 |
Dabob Bay | Ambient | 301 | CTENIDIA | HL10-19 | 75.8 | 15 | 2.82 |
Dabob Bay | Ambient | 302 | CTENIDIA | HL10-20 | 62.4 | 14 | 4.90 |
Dabob Bay | Ambient | 306 | CTENIDIA | HL10-21 | 136.0 | 18 | LOW |
Dabob Bay | Ambient | 304 | CTENIDIA | HL6-19 | 200.0 | 15 | 2.80 |
Dabob Bay | Ambient | 305 | CTENIDIA | HL6-20 | 75.2 | 16 | 2.96 |
Dabob Bay | Ambient | 303 | CTENIDIA | HL6-21 | 95.2 | 14 | 2.40 |
Dabob Bay | Ambient | 307 | CTENIDIA | HL6-16 | 89.4 | TOO LOW | TOO LOW |
Dabob Bay | Ambient | 308 | CTENIDIA | HL6-17 | 73.6 | 16 | 2.48 |
Dabob Bay | Ambient | 309 | CTENIDIA | HL6-18 | 170.0 | 14 | 1.46 |
Oyster Bay | High | 311 | CTENIDIA | SN6-16 | 158.0 | 16 | 3.76 |
Oyster Bay | High | 312 | CTENIDIA | SN6-17 | 90.6 | 15 | 1.58 |
Oyster Bay | High | 313 | CTENIDIA | SN6-18 | 72.4 | 15 | 1.81 |
Oyster Bay | High | 314 | CTENIDIA | SN6-19 | 42.2 | 17 | LOW |
Oyster Bay | High | 315 | CTENIDIA | SN6-20 | 148.0 | 15 | 2.32 |
Oyster Bay | High | 316 | CTENIDIA | SN6-21 | 146.0 | 16 | 3.74 |
Oyster Bay | High | 317 | CTENIDIA | SN6-22 | 158.0 | 15 | 2.14 |
Oyster Bay | High | 318 | CTENIDIA | SN6-23 | 174.0 | 16 | 4.08 |
Oyster Bay | High | 319 | CTENIDIA | SN6-24 | 77.6 | 16 | 0.64 |
Oyster Bay | Ambient | 321 | CTENIDIA | SN6-25 | 148.0 | 15 | 3.64 |
Oyster Bay | Ambient | 322 | CTENIDIA | SN6-26 | 44.6 | 16 | NOT QUANTIFIED |
Oyster Bay | Ambient | 323 | CTENIDIA | SN6-27 | 102.0 | 15 | 3.70 |
Oyster Bay | Ambient | 324 | CTENIDIA | SN6-28 | 172.0 | 16 | 0.78 |
Oyster Bay | Ambient | 325 | CTENIDIA | SN6-29 | 180.0 | 14 | 2.92 |
Oyster Bay | Ambient | 326 | CTENIDIA | SN6-30 | 130.0 | 15 | 4.04 |
Oyster Bay | Ambient | 327 | CTENIDIA | SN6-31 | 85.2 | 15 | 1.97 |
Oyster Bay | Ambient | 328 | CTENIDIA | SN6-32 | 156.0 | 14 | 2.12 |
Oyster Bay | Ambient | 329 | CTENIDIA | SN6-33 | 162.0 | 15 | 0.33 |
Fidalgo Bay | High | 331 | CTENIDIA | NF6-16 | 42.2 | 16 | LOW |
Fidalgo Bay | High | 332 | CTENIDIA | NF6-17 | 65.8 | 17 | LOW |
Fidalgo Bay | High | 333 | CTENIDIA | NF6-18 | 78.6 | 15 | 2.98 |
Fidalgo Bay | High | 334 | CTENIDIA | NF6-19 | 64.8 | 17 | LOW |
Fidalgo Bay | High | 335 | CTENIDIA | NF6-20 | 180.0 | 15 | 2.58 |
Fidalgo Bay | High | 336 | CTENIDIA | NF6-21 | 94.8 | 20 | LOW |
Fidalgo Bay | High | 337 | CTENIDIA | NF6-22 | 194.0 | 15 | 4.02 |
Fidalgo Bay | High | 338 | CTENIDIA | NF6-23 | 81.6 | 16 | 2.74 |
Fidalgo Bay | High | 339 | CTENIDIA | NF6-24 | 77.2 | 16 | 1.91 |
Fidalgo Bay | Ambient | 341 | CTENIDIA | NF6-25 | 89.6 | 14 | 1.31 |
Fidalgo Bay | Ambient | 342 | CTENIDIA | NF6-26 | 162.0 | 15 | 1.78 |
Fidalgo Bay | Ambient | 343 | CTENIDIA | NF6-27 | 114.0 | 14 | 2.58 |
Fidalgo Bay | Ambient | 344 | CTENIDIA | NF6-28 | 25.0 | 15 | 1.58 |
Fidalgo Bay | Ambient | 345 | CTENIDIA | NF6-29 | 190.0 | 14 | 3.50 |
Fidalgo Bay | Ambient | 346 | CTENIDIA | NF6-30 | 43.6 | 14 | 1.36 |
Fidalgo Bay | Ambient | 347 | CTENIDIA | NF6-31 | 69.0 | 15 | LOW |
Fidalgo Bay | Ambient | 348 | CTENIDIA | NF6-32 | 54.4 | 16 | 3.06 |
Fidalgo Bay | Ambient | 349 | CTENIDIA | NF6-33 | 82.0 | 15 | 1.90 |
Larvae
Spawning Tank | Cohort | Treatment | Homo./RNA TUBE # | [RNA] after Turbo Dnase treatment (ng/uL) | End Point PCR cycles | ds cDNA concentration |
---|---|---|---|---|---|---|
HL-10 Ambient | Dabob Bay | 10 Ambient | 401 | 93.4 | 16 | 1.24 |
HL-10 Ambient | Dabob Bay | 10 Ambient | 402 | 114.0 | 16 | 2.76 |
HL-10 Ambient | Dabob Bay | 10 Ambient | 403 | 136.0 | 14 | 4.16 |
HL-10 Ambient | Dabob Bay | 10 Ambient | 404 | 112.0 | 15 | 2.92 |
HL-10 Low | Dabob Bay | 10 Low | 411 | 72.6 | 16 | 3.08 |
HL-10 Low | Dabob Bay | 10 Low | 412 | 31.2 | 17 | LOW |
HL-10 Low | Dabob Bay | 10 Low | 413 | 130.0 | 15 | 1.97 |
HL-10 Low | Dabob Bay | 10 Low | 414 | 168.0 | 15 | 0.86 |
HL-6 Ambient | Dabob Bay | 6 Ambient | 421 | 57.6 | 14 | 1.63 |
HL-6 Low | Dabob Bay | 6 Low | 431b | 83.0 | 16 | 3.36 |
HL-6 Low | Dabob Bay | 6 Low | 432 | 74.0 | 17 | LOW |
HL-6 Low | Dabob Bay | 6 Low | 434 | 63.0 | 15 | 5.58 |
NF-10 Ambient A | Fidalgo Bay | 10 Ambient | 441 | 16.2 | 15 | LOW |
NF-10 Ambient A | Fidalgo Bay | 10 Ambient | 442b | 69.8 | 17 | LOW |
NF-10 Ambient A | Fidalgo Bay | 10 Ambient | 443 | 60.2 | 15 | 1.36 |
NF-10 Ambient B | Fidalgo Bay | 10 Ambient | 444 | 70.6 | 14 | 4.08 |
NF-10 Ambient B | Fidalgo Bay | 10 Ambient | 445 | 160.0 | 15 | 4.00 |
NF-10 Low B | Fidalgo Bay | 10 Low | 451 | 68.4 | 14 | 2.24 |
NF-10 Low A | Fidalgo Bay | 10 Low | 452b | 97.2 | 16 | 2.68 |
NF-10 Low B | Fidalgo Bay | 10 Low | 453 | 196.0 | 14 | 0.97 |
NF-6 Ambient B | Fidalgo Bay | 6 Ambient | 461b | 84.0 | 14 | 2.02 |
NF-6 Ambient A | Fidalgo Bay | 6 Ambient | 462b | 106.0 | 16 | 0.21 |
NF-6 Low B | Fidalgo Bay | 6 Low | 471b | 108.0 | 15 | 5.20 |
NF-6 Low B | Fidalgo Bay | 6 Low | 472b | 97.0 | 15 | 2.42 |
NF-6 Low B | Fidalgo Bay | 6 Low | 473 | 124.0 | 14 | 2.34 |
NF-6 Low A | Fidalgo Bay | 6 Low | 474 | 77.2 | 14 | 3.02 |
NF-6 Low A | Fidalgo Bay | 6 Low | 475 | 27.4 | 14 | 2.44 |
NF-6 Low A | Fidalgo Bay | 6 Low | 476 | 39.2 | 14 | 4.24 |
NF-6 Low A | Fidalgo Bay | 6 Low | 477 | 164.0 | 15 | 2.28 |
SN-10 Ambient A | Oyster Bay C1 | 10 Ambient | 481 | 89.2 | 14 | 0.88 |
SN-10 Ambient B | Oyster Bay C1 | 10 Ambient | 482 | 22.2 | 14 | 1.42 |
SN-10 Ambient B | Oyster Bay C1 | 10 Ambient | 483 | 99.0 | 16 | 1.22 |
SN-10 Ambient A | Oyster Bay C1 | 10 Ambient | 484 | 58.4 | 15 | 1.67 |
SN-10 Ambient B | Oyster Bay C1 | 10 Ambient | 485 | 19.1 | 14 | 2.90 |
SN-10 Ambient A | Oyster Bay C1 | 10 Ambient | 486b | 148.0 | 16 | LOW |
SN-10 Ambient B | Oyster Bay C1 | 10 Ambient | 487 | 118.0 | 16 | 1.27 |
SN-10 Ambient A | Oyster Bay C1 | 10 Ambient | 488 | 60.0 | 14 | 3.06 |
SN-10 Ambient A | Oyster Bay C1 | 10 Ambient | 489 | 68.0 | 15 | 3.84 |
SN-10 Ambient B | Oyster Bay C1 | 10 Ambient | 490 | 186.0 | 14 | 2.32 |
SN-10 Ambient B | Oyster Bay C1 | 10 Ambient | 491 | 58.4 | 15 | 6.64 |
SN-10 Ambient B | Oyster Bay C1 | 10 Ambient | 492 | 82.6 | 15 | 2.02 |
SN-10 Low B | Oyster Bay C1 | 10 Low | 41 | 108.0 | 17 | LOW |
SN-10 Low B | Oyster Bay C1 | 10 Low | 43 | 156.0 | 15 | 2.48 |
SN-10 Low B | Oyster Bay C1 | 10 Low | 46 | 98.6 | 18 | LOW |
SN-10 Low B | Oyster Bay C1 | 10 Low | 47 | 128.0 | 17 | LOW |
SN-10 Low A | Oyster Bay C1 | 10 Low | 44 | 41.4 | TOO LOW | TOO LOW |
SN-10 Low B | Oyster Bay C1 | 10 Low | 506 | 29.2 | 15 | 1.63 |
SN-10 Low A | Oyster Bay C1 | 10 Low | 45 | 25.1 | 20 | LOW |
SN-6 Ambient B | Oyster Bay C1 | 6 Ambient | 35 | 94.4 | 20 | LOW |
SN-6 Ambient B | Oyster Bay C1 | 6 Ambient | 513 | 142.0 | 15 | 5.92 |
SN-6 Ambient B | Oyster Bay C1 | 6 Ambient | 39 | 89.8 | 15 | 2.04 |
SN-6 Ambient B | Oyster Bay C1 | 6 Ambient | 37 | 74.6 | 20 | LOW |
SN-6 Ambient A | Oyster Bay C1 | 6 Ambient | 34 | 104.0 | 18 | LOW |
SN-6 Low A | Oyster Bay C1 | 6 Low | 521 | 66.6 | 17 | LOW |
SN-6 Low A | Oyster Bay C1 | 6 Low | 522 | 32.2 | 16 | 2.42 |
SN-6 Low B | Oyster Bay C1 | 6 Low | 523 | 71.4 | 16 | 2.16 |
SN-6 Low B | Oyster Bay C1 | 6 Low | 524 | 63.0 | 14 | 3.24 |
SN-6 Low B | Oyster Bay C1 | 6 Low | 525 | 140.0 | 14 | 2.44 |
SN-6 Low A | Oyster Bay C1 | 6 Low | 526 | 138.0 | 14 | 2.20 |
SN-6 Low B | Oyster Bay C1 | 6 Low | 527 | 124.0 | 15 | 1.42 |
SN-6 Low B | Oyster Bay C1 | 6 Low | 528 | 87.6 | 14 | 4.48 |
SN-6 low A | Oyster Bay C1 | 6 Low | 529 | 56.8 | 15 | 2.86 |
K-10 Ambient | Oyster Bay C2 | 10 Ambient | 531 | 95.4 | 15 | 2.10 |
K-10 Ambient | Oyster Bay C2 | 10 Ambient | 532 | 93.6 | 14 | 2.12 |
K-10 Ambient | Oyster Bay C2 | 10 Ambient | 533 | 6.5 | 17 | LOW |
K-10 Low | Oyster Bay C2 | 10 Low | 541 | 44.4 | 15 | 1.40 |
K-10 Low | Oyster Bay C2 | 10 Low | 542 | 32.8 | 16 | 2.22 |
K-10 Low | Oyster Bay C2 | 10 Low | 543 | 162.0 | 14 | 5.24 |
K-6 Ambient | Oyster Bay C2 | 6 Ambient | 551 | 96.4 | 14 | 1.69 |
K-6 Ambient | Oyster Bay C2 | 6 Ambient | 552b | 74.6 | 15 | 1.84 |
K-6 Ambient | Oyster Bay C2 | 6 Ambient | 553 | 186.0 | 14 | 3.28 |
K-6 Ambient | Oyster Bay C2 | 6 Ambient | 554 | 188.0 | 14 | 2.38 |
K-6 Low | Oyster Bay C2 | 6 Low | 561 | 28.0 | 17 | LOW |
K-6 Low | Oyster Bay C2 | 6 Low | 562 | 126.0 | 17 | LOW |
K-6 Low | Oyster Bay C2 | 6 Low | 563 | 47.0 | 15 | 0.19 |
K-6 Low | Oyster Bay C2 | 6 Low | 564 | 152.0 | 14 | 2.24 |
K-6 Low | Oyster Bay C2 | 6 Low | 565 | 31.2 | 17 | LOW |
homog. Control | RNA Control | 571 | LOW | 18 | LOW | |
homog. Control | RNA Control | 572 | TOO LOW | TOO LOW |
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Laura’s Notebook: QuantSeq library amplification, purification, and QA
The final step in the QuantSeq library prep is to amplify my cDNA libraries (using the optimal cycle number) and then purify. Finally, to assess quantity and quality of finished libraries I use the Qubit High Sensitivity DNA kit to measure cDNA concentration, and the Bioanalyzer High Sensitivity DNA chip kit to measure fragment lengths. I worked in batches based on the number of cycles needed to amplify.
IMPORTANT NOTE: I have 2 sets of indexes with the same numbers. I therefore prepped the ctenidia samples with one set, and the larvae with another set. Therefore, I SHOULD NOT run ctenidia and larval samples in the same lane (since two samples have the same index number).
The spreadsheet where I have all this information organized is in the laura-quantseq repo, file is: 2019-July_RNA-isolation-ctenidia-larvae.xlsx
12/30/2019 – 16 cycles
- I noticed that I didn’t have the full 17 uL in any of the ds cDNA libraries, probably missing ~ 2uL. Not sure why, possibly due to pipette loss?
- Sample #331 volume depleted after PCR, maybe due to evaporation b/c there was a small crease in the foil? This was located at well A1 – should ensure tight seal for all future plates.
Amplification plate configuration
1/1/2020 – 14 cycles
- Again, samples <17 uL.
Amplification plate configuration
1/2/2020 – 15 cycles
- Again, most samples <17 uL.
Amplification plate configuration
1/4/2020 – 17, 18 and 20 cycles
- I amplified 3 batches of samples today – those needing 17, 18 and 20 cycles (ran in that order). I held amplified samples at 10C until all groups were done, then combined all samples onto one plate, purified, and quantified.
- Unfortunately, none of these libraries amplified! I don’t know whether there was an error in the amplification step, OR if the high number of cycle needed represent low concentration/quality. Looking/thinking back through all my steps I do not see any possible errors that could have been made in the PCR mix (it’ a simple master mix using 2 reagents), or during purification. Regardless, I will need to re-do all these libraries!
Amplification plate configuration & index
Amplified and Indexed QuantSeq cDNA libraries
This is the plate configuration of the finished libraries. Based on the quantification/Bioanalyzer results, some of these libraries will need to be re-done. However, this is an important map!
Bioanalyzer quality check
- I ran 1-2 chips per amplification group. The first 2 chips, which I did for the 16 cycle group, worked great! Results below.
- I had some issues with the next few chips. Not sure why, but I suspect that it had to do with the fact that I used the Seeb lab’s pipette/pipette tips.
- I have other chip results, but did not take screen shots at the time – will follow up with those, but for the time being all files are saved in the repo: laura-quantseq/data/library-prep
Results for samples ran for 16 cycles
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