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:
Download FastA file.
Split into individual FastA files for each sequence (used pyfaidx v0.5.5.2)
Identify sequences (in original FastA file, not individual files) annotated as vitellogenin.
Design primers on best vitellogenin match (based on TransDecoder score and BLASTp e-values) using Primer3.
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: - 20181129_primer3_primers.txt
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: 0.00Primer3 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.