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:
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Download FastA file.
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Split into individual FastA files for each sequence (used pyfaidx v0.5.5.2)
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Identify sequences (in original FastA file, not individual files) annotated as vitellogenin.
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Design primers on best vitellogenin match (based on TransDecoder score and BLASTp e-values) using Primer3.
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Confirm primer specificity using EMBOSS(v6.6.0) primersearch tool to check all individual sequence files for possible matches.
RESULTS
All files were transferred to Gannet using rsync.
Output direoctory:
Primer3 human-readable output:
Here’s the info on the Primer3 top primer pair (scroll to the right to see primer sequences):
PRIMER PICKING RESULTS FOR TRINITY_DN51983_c0_g1_i8.p1.cds
No mispriming library specified
Using 0-based sequence positions
OLIGO start len tm gc% any_th 3'_th hairpin seq
LEFT PRIMER 1347 18 59.89 55.56 9.11 0.13 42.06 TTACGCCACGGCAACTGT
RIGHT PRIMER 1471 18 60.05 61.11 10.11 0.00 0.00 CGCAGTGCCAACAAGCTG
SEQUENCE SIZE: 14484
INCLUDED REGION SIZE: 14484
PRODUCT SIZE: 125, PAIR ANY_TH COMPL: 10.66, PAIR 3'_TH COMPL: 0.00
Primer3 Primer Design Parameters:
The EMBOSS primersearch tool produced only two matches:
The second file is the original FastA file from which the primers were generated, so that’s expected.
The first file is the a different isoform of the same gene.
In any case, I’ll go ahead and order this primer set.