Running bismark benchtest

#!/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

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:

• Batch1_69plex_lane1.stats.csv
• Batch2_77plex_lane2.stats.csv

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:

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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:

1. 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.
2. When we filter for 5x coverage (again, with all samples merged), the 0% methylation peak dramatically reduces.
3. 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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