I helped run the control dataset to enable comparative analysis to the concatenated positive dataset to ensure concatenation had no effect on the bacterial % dominance of lactobacillus. I was also responsible for writing the analyses for the Krona plots, as well as interpreting the alpha and beta diversity results. The most interesting part for me was examining how microbial composition differed between the BV+ and control groups, especially through the visualizations, which made patterns of abundance more intuitive to understand.
Future work could build on this analysis by exploring deeper statistical comparisons between the different pH levels in the vignal microbiome, such as identifying significantly different taxa. If I were to continue this project, I would focus on strengthening the statistical validation of the findings and linking the observed microbial patterns more directly to biological or clinical implications. Since certain other bacterial populations such as fannyhessia are often overlooked due to gardenrella and lactobacillus concentration shifts and dominance.
Hello all,
In this poster I worked on creating the background and the visual for the methods section. I also ran the concatenated datasets with the workflow created by my classmates this made the krona pies that later turned into the images used in figure 2. The most interesting part of the poster for me was the alpha diversity. Learning and seeing that a healthy vaginal microbiome has less diversity was at first a shock to me but seeing how Lactobacillus must be very abundant for a healthy microbiome so much to the point where other bacteria is suppressed.
For future work I think it would be a great idea to see how different demographics have different microbiomes. I wonder if it is possible to try and filter for the causes of variations that may impact BV prevalence for certain demographics and vice versa.
I created the workflow, assisted in running the datasets through the workflow, and oversaw the formatting/aesthetic portions of the poster, including maintaining the color scheme, selecting appropriate fonts to assist readability at small font sizes, formatting tables, minimizing whitespace, and maintaining concision in all writing to reduce word density. The most interesting part of our project, and the class as a whole, for me was being able to actually see the differences in taxonomical results in varying microbiomes and utilizing the krona plots tool to be able to visualize those findings.
If I were to continue this project, I would first replicate our exact experimental design using longer sample sequences (which would likely have to be originally obtained as they were nowhere out there for us to use!). I would then expand our scope to evaluate potential differences based on various factors within BV+ microbiomes, such as pH, AMR prevalences, and other demographics.
I searched NCBI to find the two datasets we used by looking for long read, whole genome vaginal sequences. I explored the Galaxy tools we used for our project, including Kraken, Krona, and Bracken, which was a new tool for us. Once we established our initial workflow, I iterated with different strategies to analyze the data, eventually deciding to concatenate our negative and positive datasets. I also worked on the data analysis and presentation of results. For the pie charts, I tested multiple strategies to present the data after being unsatisfied with the Krona chart, and created my own pie charts in Excel. What I found most interesting was running the Simpson Diversity results, because that’s when I learned that a healthy vaginal microbiome was actually characterized by low, not high alpha diversity.
If I were to expand on this project, I would like have more information about the individuals that the samples originate from. It would be interesting to be able to consider how demographics like age, gravidity, or menopausal stage affect the prevalence and manifestation of BV.