Today I ran a trial PCR on subsample of the flanking primers to see if they worked correctly. The primers I used were:
1676
Flk_TLR_FWD
GCAATAGCTTGTCACCGCC
JH
6/1/2015
20
59
O.lurida
TLR2.1 Flanking
Q9DD78
1675
Flk_TLR_REV
TCTAGTATGCGCTTCGTTTGC
JH
6/1/2015
20
59
O.lurida
Q9DD78
1674
Flk_CRAF_FWD
GGACATCCAGTGGCAACATTC
JH
6/1/2015
21
60
O.lurida
CRAF1 Flanking
Q60803
1673
Flk_CRAF_REV
CCAGGACATTAGGCTTGCTGA
JH
6/1/2015
21
60
O.lurida
Q60803
Using the Apex Red PCR Master Mix I created master mixes for each set of primers and ran them together.
Reagent Table
Reaction_Components
Volume
Final Concentration
2x Apex Red
12.5
125
Forward Primer (10uM)
0.5
5
Reverse Primer (10uM)
0.5
5
H20
10.5
105
1:1 cDNA
1
25
Using the final concentration I mixed each master mix going from largest to smallest volume.
I then pipetted 24 ul in each well followed by 1 ul of water or sample depending.
Then I ran the following PCR program:
Temp
Time
95 C
5 min
95 C
30 sec
55 C
30 sec
72 C
30 sec
repeat steps 2-4 40 times
72 C
3 min
4 C
Hold
Once the PCR finished I ran the products on a 1.3% agarose gel
Gel Reagent Table
Reagent
Volume
1X Low TAE
100 ml
Agarose
1.3 g
EtBr
10 ul
Add agarose to TAE.
Microwave 1 minute stir
Repeat until no particulate matter in solution
Add EtBr while agarose still hot
Gently pour in one corner of the gel cast until tray is full
I then ran the gel at 100 v for 35 minutes. I placed it on the transilluminator to view any bands that may have formed. The gel is below.
Gel Layout:
TLR2.1
CRAF 1
Empty
Ladder
NTC
NT1
HT1
ST1
NC1
HC1
SC1
NTC
Ladder
NTC
NT1
HT1
ST1
NC1
HC1
SC1
NTC
Ladder
Empty
As you can tell the TLR2.1 primers still did not amplify in the Dabob population. This could be an indication that this gene is not expressed in this population. More population replicates are needed to determine if this is true.
After seeing these nice bands, I cut them out and stored the individual bands for sequencing in 1.5 ml tubes. I also decided to run the remaining primers using the reagents above and then produce the gels tomorrow.
Today I needed to develop a way to reverse engineer a flanking primer from a primer that someone else designed. For my qPCR runs, I have an HSP70 and Actin primers which were developed previously by someone else with little information on how they were developed. The primers work great but to determine if they are reaching the same efficiency in every sample or cover the same isoform of the gene in every population I need to develop flanking primers. Luckily NCBI has some advance functions in the Primer Blast program which allowed me to find the template region in my transcriptome to develop flanking primers.
First I needed to take the sequences of the primers I had and find where they existed in the Transcriptome I'm using.
Under primer parameters I put in the forward and reverse primer sequences. Then under Primer Pair Specificity Checking Parameters I changed the Database type to custom and uploaded the Oly Transcriptome V3. Then for added effect I changed the organism to the Ostrea taxa. I don't think this really did much but it didn't detract either. Then I hit get primers and waited for the results.
You can see what the input looks like above. Below is the output for the primers.
This out put tells me the reference name of the template from the Transcriptome on the left, the size of the primer product, and where the primer lays down on the sequence. Under each primer sequence there is a line of ...................... these indicate that the complimentary strand is a 100% match. If any of these dots are replaced with a letter it tells you whats different and that its not a 100% match. You can also see that the top 3 template matches have reference names that are very similar. This is because they are overlapping reads of the same region. If the names were different then we could assume it was a different region.
Once you've got the reference name, then you can follow the same method I used to develop the other flanking primers here.
Today after some conversations between Brent and Steven based on the previous primer checks (here and here) the idea arose that not all populations may share the same isoforms of the target genes. To determine if the genes are the same in all populations, its necessary to produce a sequence containing the primer region plus 100 bp on either side for sequencing in sub samples from each population. This put me in a unique situation. I needed to not only have the primer for the target gene, but also a primer that encompasses the target gene + original primer + 100 bp on either side of the sequence. I decided to use the NCBI Primer Blast, that I used to generate the original primers, to create the new primers.
First I need to figure out where in the sequence does the original primer amplify. To do this I have to upload the original sequence and the primer sequences into NCBI. For this I'm looking at the TLR2.1 primer since it has the most interesting results from the primer check.
The original primer sequences for TLR2.1:
FWD ACAAAGATTCCACCCGGCAA
First I run the sequence and the primers through NCBI Primer Blast:
Once the Primer Blast returns the primer target. I can then find where the primer begins and ends in the original sequence.
The forward primer begins at roughly 340 bp and the reverse primer begins at roughly 460 bp. Using these numbers I subtract 100 bp from the start of the forward primer and add 100 bp to the beginning of the reverse primer. This extends the range to 240 bp - 560 bp which encompasses the original target and primer area as well as 100 bp on either side. To give the Primer Blast some leeway to produce the new primers, I give a 100 bp range for the forward and reverse primers. The range for the forward primer becomes 140-240 bp. The range for the reverse primer becomes 560-660 bp. I also required the product size to be between 325 bp and 1000 bp long. This is so that the 100 bp on either side plus the 109 bp original target are amplified.
The primers that NCBI now covers the entire area that needs to be sequenced. All I have to do is choose primers that have low self complimentarity.
After looking at the offered primers I chose one that should give a 425 bp product with low self complimentarity.
This is the quickest way I've found to take the primers I produced originally and create primers to sequence the target area of interest. If this works I'll do this with all the primers of interest that worked in the previous primer runs.
Last week with the guidance and assistance from Steven, we turned the Oly Transcriptome V3 into annotated list of possible genes for qPCR investigation. You can see Steven's notebook about how he discerned which genes would be useful for our study. The list which contains sequence ID's that relate to the Oly transcriptome V3 Fasta file.
Being a fool I decided to try to hand search the fasta using wordpad and the list of ID's. Each ID took approximately 5 minutes or more to find in the document. The fasta was so large that it was nearly impossible to scroll through because it lagged my computer. So I decided to find a coding way to search the file quickly.
First I attempted to find a way to do it with python as it seems like a quick way to write a few lines of code and produce a good output document. The samtools package contains a function called faidx which via extensive research seemed like it would do pretty much what I wanted by search the fasta using the input sequence IDs and produce an output document with the related sequences. Sadly because I'm on a windows machine, samtools is a nightmare to install and I couldn't find a good work around. Normally I would then port everything over to eagle and run it all remotely which is pretty cool and easy to do. Instead though, Ryan Waples suggested I look into an R package called SeqinR (Seq in R, get it!).
I'm a much better R programmer than I am at python or perl. The other nice thing about R is that it works on all OS without needing language specific dependencies. So I popped open R, installed the SeqinR package and began playing with my data. At the bottom is what I did in R to search the fasta using the unique sequence IDs.
There isn't a good way that I've found to produce an output document from the list function in R. Instead of figuring it out I just cheated a little and copy/pasted from the command line output to a google doc.
I then reviewed this video by Steven on how to make primers
Following his example and doing some reading on proper primer design. I took each sequence into NCBI Primer creator to develop primers for each sequence. I copied the sequence into the input field and select a product between 100 and 400 bp and disabled the specificity check for the primers. These options are highlighted in the image below in yellow.
After hitting "get primers" I was lead to the next page with a list of possible primer combinations. From these combinations I need to pic primers that would work well for qPCR. From my reading on qPCR primers I chose to primers based on the lowest Self Complementarity and 3' Self Complementarity values to reduce the possibility for primer dimers. You can see an example of this in the image below.
Once I selected these values I then copied the FWD and REV primer sequence and product size into a google sheet to create a list of primers to order. You can view this list here. From this list I'll order primers and then check them via qPCR to determine if they produce interesting results. Hopefully we'll see some cool differences between populations using these primers.
