Microbial Evolution
My current research is to identify novel lineages of symbionts and understanding the molecular evolution of prokaryotes.
About Me
Computational Biologist
Specialization in Microbial Genomics
Welcome!
I'm Yumary Vasquez, a Postdoctoral Scholar at the Joint Genome Institute at Lawrence Berkeley National Laboratory.
I completed my PhD from the Quantitative and Systems Biology program at UC Merced. During my graduate education, I studied the coevolution of the nutritional symbionts with the agricultural pest, the Macrosteles quadrilineatus leafhopper.
I have a Bachelor of Science from California State University San Marcos in Biotechnology, with a minor in Computer Science. I have previous lab experience studying T-cells in obese mice & population genomics in ladybugs and parasitic wasps.
Outside of work, my hobbies include yoga and baking.
Research
General Research Interests: genomics, next generation sequencing, data science, machine learning, evolution.
Skills
Publications
For the complete list, please check my google scholar. Email me if you have any questions about my papers.
YM Vasquez,Z Li, AZ Xue, GM Bennett. Molecular Ecology Resources 2023
[paper]
YM Vasquez, GM Bennett. iScience 2022
[paper]
G Waneka, YM Vasquez, GM Bennett, DB Sloan. Genome Biology and Evolution 2021
[paper]
H Vansant, YM Vasquez, JJ Obrycki, A Sethuraman. Biological Control 2019
[paper]
A Sethuraman, FJ Janzen, MA Rubio, YM Vasquez, JJ Obrycki. Insect Science 2018
[paper]
Projects
This page is a collection of projects I have worked on, including a blog post about the work in the project.
Post 4: Bee vs. Wasp ID
Currently, I am working my way through the "Practical Deep Learning for Coders" course. This is a quick project that I did for the first
lesson where I adapted the code to identify bee photos compared to wasp photos. Check out the code above and watch out for more code.
Post 3: Viki Recommendation
I'm an avid Chinese and Korean drama watcher. When I first started grad school, I stumbled across a C-drama on Netflix, and now I mainly use Viki to watch asian dramas.
In this project, I am working on creating a recommendation system using descriptions and information from Viki dramas. Currently, this code uses TF-IDF Vectorizer from scikit-learn
in order to provide recommendations based on description, genre, or description & genre.
This is a beginning project but I plan on adding multiple features including:
1. Create a shiny app using python code (still in development)
2. Search recommendations based on genre and/or description of show
3. Show all shows that contain a certain actor/actress; as well as recommendations on other shows that are similar to the actor/actress
4. Updates to include new shows (current dataset is until May 2022)
Any suggestions to this project are welcome!
Post 2: Brewery Map (updated April 2023)
During the Summer of 2022, my husband and I talked about "what we like to do for fun." One thing we talked about is how we both love to check out breweries wherever we go.
Following this conversation, we also talked about creating a shiny map with our brewery visits. This is the beginning of that work.
This is in no way the complete list of breweries we have been to (we forgot the name of a few), but it is a starting point.
We both also have been to other breweries without each other, and I have not added those as well.
My hope for this small, for-fun project is to take in text input that will update the maps with suggestions from people who visit this site.
Post 1: CheckerSpot Butterflies
In 2019, I was in a semester long course at UC Merced called Interdisciplinary Computational Graduate Education (ICGE). This class was a National Research Training Program funded by the NSF.
In this class, we grouped up with other graduate students across disciplines and chose our own project. My own team had a mathmatician, a cognitive scientist, a physicist and myself (a biologist).
As a group we decide to do some twitter scraping using this TweetScraper. At first, we wanted to look at twitter discourse on the idea of climate change.
But you can imagine how many tweets revolved around climate change.... so we settled on looking at climate change tweets related to legislation. Specifically, we focused our twitter scraping on one case: Juliana vs. US
We scraped tweets between August 2015 - February 2019, and use those tweets for a sentiment analysis. We also plotted the number of tweets every month per year and connected peaks to specific climate change related events.
Our results and methods (along with our presentation) can be found on the link for this post. However, for this post I wanted to discuss a few things:
This was not my first time using a tweet scraper, but it still amazes me how easy it is to grab data from the internet just based on a hashtag.
This was my first time using data that wasn't biological, so sentiment analysis was new to me. Finally, working with people from other disciplines was a great way I was able to learn techniques from outside my field. Most of conversations today revolve around biological ideas, but during this time my conversations covered many many topics that I had never been introduced to.