Month: December 2019

Once we had the our consensus sequences from our 2 samples that worked through the PCR, we were able to determine the best model of molecular evolution. In order to do this, we used jModelTest2 to figure the best model. I downloaded jModel2 from the site given and opened a folder with a program that uses JAVA. We then went back to Geneious and exported our alignment in Phylip format (relaxed). Afterwards, we went back to JAVA and opened the exported alignment and computed the likelihood scores. Once the scores were done, we chose the best model based on some optimality criterion.Two methods were used: the Alkaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best model shown for AIC was 11369.78 and the bet model based on BIC was 11622.87, which are the same model.

We then looked at MrBayes analysis, and we made a tree with substitution model GTR and rate variation +G (gamma). The MCMC was set to 10,100. The subsample frequency was set to 200, and the heated chains were left at 4. The priors for this Bayesian analysis were unconstrained branch length: Exponential (10), and Shape parameter (10). The distribution shown demonstrated that our analysis was too short.

We used maximum likelihood to infer a phylogenetic tree of our aligned data set with RAxML We choose our evolutionary model as rapid bootstrap with rapid hill climbing. Once the RAxML was done, we built a consensus tree with a support threshold of 50%. The clades with high support match those from the Bayesian analysis.

We also tried a different program that uses maximum likelihood, PHYML, which we ran with bootstrapping and HKY85 as out molecular model of evolution. We used the tree window to manipulate our resulting best ML tree with bootstrap support by making the outgroup the root, showing th bootstrap proportions, and making the tree easy to read.

We used the same alignment and the HKY85 model of molecular evolution to infer the best tree using MrBayes. We ran it with 3 million generations and a subsample frequency of 500. The outgroup was chose to be a ray.When the run is done, we exported the final tree with support values, the posterior distribution, and the trace.

We conducted a double digest restriction associated DNA study on mimulus guttatus.The first step was collecting sample which was done on two field trip which can be read about here. Next, we extracted DNA from the sampled we collected as well as previously collected samples from our boy, Alec. Then we double digested out DNA using two restriction enzymes, which can be read on here. These enzymes cut up the genome into many pieces. Next, we ligated unique DNA barcodes into each of our individuals (the procedure being here). Our next step was to use PCR to add a second unique barcode and to test if our library construction was successful (procedure here). Our PCR was successful as evidenced by the gels that were not photographed. After the test PCR, we did a larger reaction (25 microliters) that was identical that we did with the next step. This was the last step we did as a class.

In a perfect world we would have done the following steps. We would do size selection in which we select DNA of specific sizes and we would target 400-600 bps. Size selection can be done in 3 ways, with pippen prep (automated system) in the Suni Lab #Suni, the second way with gel extraction, or we can use magnetic beads to isolate the DNA fragments as well. After size selection, we would then normalize our DNA samples, this means to bring all of our DNA samples to approx. the same concentration (making more likely the same number of DNA fragments to be sequenced). Final step would be to combine all size selected PCR products into one vessel (or 2). Next, we would run the samples on an Ilumina sequencer (out Iseq1000/walle). Sequencing would take approximately 16 hours and if successful, would generate 10s of millions of reads. These data would be run through a bioinformatics pipeline (help Dr. Z). We would align these sequenced data with the published mimulus guttatus genome and call SNPs. Finally, we would use the SNPs to infer population differentiation using a metric like Fst and assess population genetic diversity by looking at number of alleles, allelic diversity, etc. Based on what I know about mimulus guttatus, I expect populations that are geographically divergent to be genetically divergent. #MolecularEcologyForever