Sam’s Notebook: Primer Design – Gigas COX1 using Primer3

We’re attempting a quick & dirty comparison of relative mitochondria counts between diploid and triploid C.gigas, so needed a single-copy mitochondrial gene target for qPCR. Selected cytochrome c oxidase subunit 1 (COX1), based on a quick glance at the NCBI mt genome browser for C.gigas NC_001276.

Although everything is explained pretty well in the Jupyter Notebook linked below, here’s the brief rundown of the process:

  1. Download FastA files for C.gigas genome, C.gigas mt genome, C.gigas mt coding sequences (only way I could figure out how to get individual gene nucleotides).
  2. Split into individual FastA files for each sequence (used pyfaidx v0.5.5.2)
  3. Design primers on AF177226 (COX1) using Primer3.
  4. Confirm primer specificity using EMBOSS(v6.6.0) primersearch tool to check all individual sequence files for possible matches.

Jupyter Notebook (GitHub):

Grace’s Notebook: 2015 Oysterseeddia Update

Sam’s Notebook: qPCR – Geoduck gonad cDNA with vitellogenin primers

Earlier today I made some cDNA from geoduck gonad RNA for use in this qPCR to test out the vitellogenin primers I designed on 20181129

I also used geoduck gDNA (162ng/uL; from 20170105) as a potential positive control, or as confirmation that these primers will not amplify gDNA.

Primers:

(SR IDs)[https://ift.tt/2G7zOSt

  • 1712
  • 1711

All qPCR reactions were run in duplicate. See qPCR Report (Results section below) for plate layout, cycling params, etc.

qPCR Master Mix calcs (Google Sheet):

Sam’s Notebook: Reverse Transcription – Geoduck gonad RNA pool

To be able to actually test the vitellegenin primers I made, I needed some geoduck cDNA.

I pooled 1uL of each of the following 12 geoduck gonad RNA (isolated previously from histology blocks):

  • 02
  • 04
  • 07
  • 09
  • 38
  • 41
  • 46
  • 51
  • 65
  • 67
  • 68

The concentration of the pooled sample was 95ng/uL.

Used 950ng of the pooled RNA in the following reverse transcription reaction:

  • 10uL RNA
  • 1uL oligo dT primers (Promega)
  • 4uL H2O
  • Incubate 10mins @ 70oC in MJ PTC-200 (no heated lid); immediately on ice after incubation.
  • 1.25uL of 10mM dNTPs (Promega)
  • 5uL 5x MMLV Buffer (Promega)
  • 1uL M-MLV reverse transcriptase (Promega)
  • 3.75uL HH2O
  • Final volume = 25uL
  • Incubate 42oC in MJ PTC-200 (heated lid); 5mins @ 95oC

Will run qPCR and then stored in Sam’s cDNA Box 2 in -20oC.

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Grace’s Notebook: DIA protocol, and Next Steps

Today I worked on documenting what I did (writing up a protocol of sorts) that is in progress. I am re-doing a couple steps just so that I’m ABSOLUTELY certain where files came from, and I’m putting all the new outputs in a brand-new directory so that it’s just all more organized. I will upload all of the directory and files into a new folder in OWL, which will be linked out in the new protocol. I also got some input on how to set the thresholds that we talked about in the proteomics meeting yesterday, so I’ll try that Monday once my process is fully documented so I can just focus on analysis.

DIA protocol (in progress)

(Based on this DIA outline from MacCoss Lab grad students: data_analysis)

my new DIA protocol

All of this is being performed on Woodpecker in FTR 209, Roberts’ Lab. All of the materials, and output files are in /Desktop/grace/Bri-line
After all is finished, I’ll upload to OWL or GitHub repo… probably GitHub repo is the best…

DIA next steps

From Lindsay:

The quants exported from Encyclopedia (*.elib.peptides.txt) are all I use. You can view multiple samples using the Multi Elib browser.

I don’t bother checking chromatograms until I have a reasonably scaled list (e.g. statistically significant, significantly differential, etc). You can’t manually curate data at DIA scale, so you gotta have either (in order of ascending difficulty from easiest to most laborious:) (a) willingness to accept that there’s gonna be a lot of bad quants; (b) a specific hypothesis you want to manually validate a la cherry-picking; <– this is what I usually do for one-off bio experiments (c) harsh filters for the quant with thresholds like min 3 quantitative transitions, min 2 peptides, etc; or (d) empirically validate quantitative peptides like with a calibration curve. <– this is probably overkill for almost everyone

We decided on setting stringent thresholds. Bri-line doesn’t give you any options to do so, so Lindsay suggested this (GitHub Issue #507):

There’s built in “filters” when running encyclopedia, the parameters include “min quantitative ions” etc. Other filtering is all DIY. I use R or Python to parse the *.elib.peptides.txt and run statistical tests. Then I set up skyline doc to view results of statistical testing

I’ll try that out on Monday (I’m devoting the better part of Monday to this project, or maybe all day if the Bioanalyzer still is out of commission), as well as finish up the protocol.

In the meantime, I’ll read up on MS stats (Yaamini has used this before), read up on the project more, and revisit the paper: Oyster-larval-proteomics-2015

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Grace’s Notebook: 2015 Oysterseed Plan and Crab Meeting Notes

Crab Meeting today: shared current status of what we’ve got (FISH546 grace-Cbairdi-transcriptome). Additionally, we had a proteomics meeting today and I got some input on next steps for the DIA: set some stringent thresholds, MS Stats, make some tables, etc. I will make a separate post on what I did (protocol) tomorrow as I work with the data. Today’s post will just include a general outline of what I have and next steps.

Crab Meeting

(Will edit and publish within the next couple of days)

Shared my current status of transcriptome annotation and basic analyses (FISH546 grace-Cbairdi-transcriptome).

Steven and Sam were both surprised by the taxonomy pie chart, specifically that there were ~2500 taxonomic groups identified. (Rscript; (data_set_from_Steven). I will go back and investigate which transcriptome was used, and also make clear that there are two different fasta files: one from the practice assembly using the tiny .fastq files; one from the true assembly on Mox.

I also am going to get some more BLAST jobs running as we wait for the Bioanalyzer to be fixed (after which I can hopefully continue with RNA extractions if results look good).

2015 Oysterseed Proteomics Chat

What I have (finally!): I have some *.elib.peptides.text files, which are the quant files from the DIA.

(Tomorrow’s post will be dedicated to the DIA protocol I used.)

What I will do: I will set a stringent threshold and use MS Stats, take a rough look at the data. Make a table. Emma said she’ll be available to help if (when) I get stuck.

I also need to make sure I can identify the proteome I used. I know where it is, but I’ll include it in tomorrow’s post with the protocol.

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Sam’s Notebook: Primer Design – Geoduck Vitellogenin using Primer3

In preparation for designing primers for developing a geoduck vitellogenin qPCR assay, I annotated a de novo geoduck transcriptome assembly last week. Next up, identify vitellogenin genes, design primers, confirm their specificity, and order them!

All of this was done in a Jupyter Notebook on my computer (Swoose).

Jupyter notebook (GitHub):

Annoated transcriptome FastA (271MB):

Although everything is explained pretty well in the Jupyter Notebook, here’s the brief rundown of the process:

  1. Download FastA file.
  2. Split into individual FastA files for each sequence (used pyfaidx v0.5.5.2)
  3. Identify sequences (in original FastA file, not individual files) annotated as vitellogenin.
  4. Design primers on best vitellogenin match (based on TransDecoder score and BLASTp e-values) using Primer3.
  5. Confirm primer specificity using EMBOSS(v6.6.0) primersearch tool to check all individual sequence files for possible matches.