Welcome to my GitHub profile! I'm a bioinformatic scientist with a passion for solving biological problems through computational methods. As an ex-bench scientist, I am eager to bridge gap between computational scientists and biologists. I've had the opportunity to work on various projects and develop a diverse set of skills in the dynamic and evergrowing field of genomics, transcriptomics and data visualization.

• Name: Kalyanee Shirlekar
• Location: Gainesville
• Email: kshirlekar@ufl.edu
• LinkedIn: https://www.linkedin.com/in/kalyanee-shirlekar/

• Bioinformatics: I specialize in developing and executing bioinformatics pipelines such as RNA-Seq, Chip-Seq, ATAC-Seq, scRNA-Seq. I am accustomed with the Nexflow pipelines, and other bioinformatics tools, packages and genome browsers. My programming expertise lies in R and Python as well as basic level bash scripting.
• Machine Learning: I am highly interested in appplying machine learning techniques to study biological conditions implicated in precision medicine.
• Data Visualization I am comfortable in Rshiny and love to experiment with different visualizations that can aid data interpretation.
• Genomics/Precision Medicine: Having worked as a Variant Analyst/Biocurator for 4 years, I am well-versed with fundamentals of clinical cancer biology, clinical trial design and management, classification of cancer types (SNOMED CT terms), guideline recommendations e.g. NCCN/FDA and the importance of targeted therapies in personalized medicine.

Here are some of the projects I've worked on:

1. RShiny Applications: I am working on developing my RShiny skills by creating competant and visualing compelling dashboards that will be simply the analysis part of the transcriptome sequence datasets.
2. R-Cheat-Sheet: I am creating a cheat sheet for regular users of R where there are some code snippets you require for each and every R/RMD/ShinyApp file you develop. All you have to do is look for this repository and fork out the piece of code you need.

• Python
• R
• Bioinformatic tools: trimmomatic, deseq2, limma-voom, edgeR, bcftools, samtools,
• Genome Browsers: IGV, WashU, Ensemble
• Machine Learning techniques: Data Cleaning, Management, Application of different (clasification and regression) models
• Statistics
• Data Visualization: Rshiny, MS Office
• [Additional skills]

• M.S. in Bioinformatics: [Northeastern University] - 2023
• B.S.- M.S. in [Biology]: [Indian Institute of Science Education and Research] - 2016

Feel free to reach out to me for collaborations, questions, or discussions related to computational biology & visualizations:

I'm always eager to connect with fellow computational biologists and research scientists. Let's work together to advance the field of oncology and data science!