This week marked the beginning of our next main project on Mimulus gutatus plant samples. For this lab we focused on DNA extraction. Prof Paul gave us the samples we collected in the field whenever we went to Mount Tamalpais with the class + Alec along with two other samples from other sites.
- The first step was to label 3 2mL tubes with our sample codes – which are shown below.
Tube Numbers / Sample Name / Sample ID Code
Tube 1: CATB- OY / OY01
Tube 2: MONO- 008 / OY02
Tube 3: DIRA- 001 / OY03
- Next, we added three sterile 3.2 mm stainless steel beads to each tube along with a small amount of leaf tissue to each tube, wiping off the tweezers between each sample. The steel beads were used to break up the leaf samples.
- The tubes were loaded onto a tube rack in the modified reciprocating saw rack and the rack was mounted to the saw. Prof Paul reciprocated the samples for about 40 seconds on speed setting 3.
- The tubes were then spun in the centrifuge to pull the plant dust from the lids for about 20 seconds.
- Next, we added 434 microliters of preheated grind buffer that was heated in the water bath.
- After adding the buffer we put our samples into the water bath that the grinder was in (65 degrees) for ten minutes, investing them every 3 minutes (3 times in the 10 min)
- 130 microliters of 2M pH4.7 potassium acetate was then added. The tubes were inverted several times and incubated on ice for 5 minutes
- Samples were then centrifuged on max force for 20 minutes. Centrifuge was balanced
- Next, we labeled new 1.5 mL tubes with the sample codes. Supernatant was transferred to the new tubes, while avoiding transfer of any precipitant that was at the bottom.
- We then added 1.5 volumes of binding bugger. (600 microliters)
- After this we put 650 microliters of the mixture to Epoch spin column tubes and centrifuged for 10 minutes at 15,000 rpm in a centrifuge, then discarded the flow-through in a hazardous waste container (Erlenmeyer flask in our case)
- Then we did this again with the remaining volume.
- Next we washed the DNA found to the silica membrane by adding 500 mL of 7-% EtOH to the Columbus and centrifuged it at 15,000 rpm until all the liquid passed to the collection tube (8min) and discarded the flow through
- Then we did this again ^
- Following that, we centrifuged the columns at 15,000 rpm for another 5 min to remove any residual ethanol
- Collection tubes were discarded and we put the columns in sterile 1.5 mL micro centrifuge tubes which were labeled with sample codes and the date
- NOTE: I put the columns in the wrong tube (the one containing our old DNA) and put the 100 microliters of preheated pure sterile H20 in those tubes… After noticing my mistake Prof Paul said to put it in the new sterile tubes and we ran 70 microliters of the pure sterile water through that into the tubes with the date on them and let that stand for 5 min
- These new tubes were centrifuged for 2 min to elute the DNA
- The tubes that I accidentally used first were labeled with a STAR
- Kept all the tubes in case
This week is the last part of the Sushi Test barcoding project. The first step was to clean up our alignments, which I did by editing the beginnings and the ends of the sequences to make them all line up. After this was done I looked through my alignment to spot the polymorphisms. Out of the first 20 column, 9 were polymorphic.
Next, we chose a model of molecular evolutionary using a program called jModelTest2. To use this we had to go to Geneious and export our alignment in relaxed Phylip format which ensures that if sequence names are truncated and identical then the full length names would be shown followed by a single space. After this I went back to jModelTest2 and opened the exported alignment. I went to ‘Analysis > Compute likelihood scores’ and clicked ‘Compute likelihood’. this calculated likelihoods for 88 models of molecular evolution.
** It didn’t work for my computer so Prof. Paul told me to use Mikayla’s
AIC chose GTR + I + G
BIC chose GTR + I + G
After this we started with our Bayesian Inference to create our phylogenetic trees. We ran it with a substitution model- GTR (because its what jModelTest2 chose for us), and chose the outgroup to be the shark species that we incorporated in our data. Since the best model showed the +I+G, I chose invgamma for my Rate Variation. Since this was just for lab to learn about the program we only ran it for a short time
- Chain length: 1,100,000
- Subsampling frequency: 200
- Heated chains: 4
- Burn- in length: 100,000
- Heated Chain Temp: 0.2
Once it ran, I opened a parameter estimates tab. It showed a pretty empty graph which was expected since we ran the analysis for such a short time. Then I clicked on the Trace tab. This was also a bad graph, showing an upward curving line (again, expected). Lastly, I looked at the tree. I saw that it was probably missing clades. Once I ran the analysis again with a longer chain length and burn in some clades were recovered and there were new support values.
The next thing to do was look at maximum likelihood. To infer a phylogenetic tree using maximum likelihood, we installed a Plug In called RaxML which does a very fast maximum likelihood inference. We did a ‘Rapid bootstrap with rapid hill climbing’ run and made ‘RAxML bootstrapping tree’ then a ‘consensus tree using those.
Then, we used a different program using maximum likelihood called ‘PHYML’. I used HKY85 model and made my best tree with bootstrap support.
AT HOME –> I ran this again with the HKY85 model but ran it with 3 million generations with a subsample frequency of 500. Burn in was 300,000 and outgroup was the shark species.
This week we went through an introduction of Genieous – a commercial program that performs many functions on DNA and protein sequences. We downloaded the program and made an account [free 2 week trial] and started learning how to use the program.
Two of my reverse reactions from the Sushi Test worked but none of the forward ones did so Prof. Paul assigned extra reactions that I could work with instead to learn about the program. I first downloaded all the fish barcode sequences we would need – “Fish_Barcode_Forward.geneious,” “Fish_Barcode_Reverse.geneious,” “Fish_Barcode_Forward_EXTRA.geneious,” and “Fish_Barcode_Reverse_EXTRA.geneious.” I drag and dropped these files into my Fish Barcode folder that I titled “fish barcodes – yu.”
I already knew my forward reactions did not work so the next step was to look at the two (out of four) reverse reactions to see how well they worked. I looked at the various automatically generated column in the reverse reads folder and saw that the HQ% score was only 9.3% for OY-02 and 2.4% for OY-04. Prof. Paul wrote in the handout that HQ scores of 80% or higher are excellent and 10% may still be usable so I tried to look at them but they were mostly unreadable (oops). The peaks ere unevenly spaced and not very even in height like a good read would be.
The sequences from my two reverse runs were not that short but they were pretty messy and uneven so I decided to continue in the tutorial with the extras titled “KJ03_FbcF_H06” for the forward reaction and “KJ03_FbfR_E11” for the reverse reaction. The forward had an HQ% of 92.9% and the reverse was 96.3%.
After looking at some of the features in the Geneious program, the next setp was to actually assemble forward and reverse sequence reads of the same sample so I highlighted the two KJ03 reads (F and R) and selected ‘De novo assembly.’ This created a new file which held the actual assembly. This showed a consensus sequence, sequence traces for each reads (one of which was the reverse compliment of the original sequence. The next thing I did was edit the two strands (like post transcriptional modification?). I did this by deleting any regions of ‘junk,’ making sure to highlight both the strands and the consensus sequence to avoid any frameshift mutations in the sequence. I also deleted any ambiguities along the sequence (N,Y,etc). If I was able to choose the base based off the complimentary strand I did but most of the time it just showed a dash. Finally, I used the Basic Local Alignment Search Tool (BLAST) to see which organisms had the highest matching sequence to mine.
I found that Thunnus alalunga was the most similar to my sequence and Googled it to find the common name – Albacore.
To find polymorphisms, I went back to the BLAST search results and selected 5 different species and made a nucleotide alignment. There were 7 polymorphisms.