Summary
Highly experienced bioinformatics professional with a strong background in managing and analyzing complex biological datasets, including next-generation sequencing (NGS) data. Skilled in developing and applying advanced machine learning models and custom algorithms—such as correlation analyses and differential gene expression tools—to derive actionable insights from genomic data. Expertise in creating innovative and visually compelling data visualizations that enhance biological data interpretation and communication. Proven ability to collaborate with multidisciplinary research teams to design experiments, validate computational methods, and drive impactful research outcomes. Recognized for significantly improving data processing workflows and implementing cutting-edge analytical tools, contributing to the success of high-priority projects.
Relevant Work Experience
Bioinformatics Scientist
April 2023 (Current)
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Analyzed large-scale scRNA-seq and scNuc-seq datasets from aged mouse adipose, liver, and brain tissues.
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Utilized contamination removal tools to ensure high-quality data for downstream analysis.
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Acquired and compared published data from NCBI and Ensembl databases for isoform analysis.
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Performed complex clustering on single-cell data using R and Python to study cell types enriched in aged tissues.
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Investigated RNA editing sites in Adar mutants using the A-to-I editing database, REDIportal.
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Examined cell trajectories and identified genes driving differentiation in adipocyte and hepatocyte cells.
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Conducted similarity analysis to identify senescent cells.
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Managed large datasets and software in Linux systems.
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Developed efficient HPC slurm scripts for genetic variant analysis and BAM file analysis.
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Studied transposable elements involved in cell-type differentiation in single-cell data.
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Effectively communicated results to scientists, supporting their projects and grant applications.
Bioinformatics Analyst
January 2022 - April 2023
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Developed dashboards for data visualization using Tableau.
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Proficient in Linux environments for high-performance computing and data management.
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Created advanced R Shiny web applications for interactive visualization of scRNA-seq and Gene Ontology data.
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Conducted comprehensive scRNA-seq analyses on datasets including human gastrulation, astrocytes, and muscle injury studies.
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Performed single-cell pathway analysis and pseudotime modeling to uncover biological insights.
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Applied RNA velocity analysis to identify genes driving trophoblast differentiation.
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Performed cell-cell communication analysis to uncover upregulated transcription factors involved in zebrafish muscle injury.
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Identified genes mediating transitions between distinct cellular states in gastrulation data.
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Supervised and mentored undergraduate interns in the WashU internship program, fostering skill development and research proficiency.
Research Internship
May 2021 - August 2021
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Designed R and Python pipelines for proteomics analysis of wild-type and mutant proteins.
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Developed advanced visualization methods for mass spectrometry data.
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Proficient in MaxQuant and Perseus for proteomics analysis.
Research Internship
January 2019 - August 2019
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Successful in glycogen branching enzyme expression and purification
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Accomplished exceptional results on western blots after SDS-Gel electrophoresis (SDS- PAGE)
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Completed assays for protein of interest (i.e C14 assay)
Projects
Protein Modelling
Protein structure and function determination of an aminoglycoside acetyltransferase in Staphylococcus aureus responsible for apramycin resistance. The function was characterized using various insights of an x-ray crystallography model in PyMol
Delivering presentations in University of Guelph conference
Significant practice with protein modelling softwares (i.e. PyMol, Rosetta, DALI, Consurf etc.)
Biodiversity Data Analysis
Applications of clustering and PCA to understand genetic differences in various bacterial species
Data manipulation of large data set
Building functions in R and Python for deep analysis and visualization
Effective communication of results through reports and presentations
Skills
R programming
Tidyverse, Dplyr, ggplot2, Seurat (single-cell) etc.
Python
Pandas, Numpy, Scikit Learn, Seaborn
Tableau
Pivot Tables, Dashboard, Story Development
Statistical Analysis
Machine learning, A/B testing, Regression etc.
Protein Modelling
PyMol, Rosetta, DALI
Proteomics Analysis
Maxquant & Perseus
Unix
NGS, High Performance Computing, Data Manipultation
R Shiny
Interactive Web Applications
Publications
Naive human pluripotent stem cells have the remarkable ability to self-organize into blastocyst-like structures (‘‘blastoids’’) that model lineage segregation in the pre-implantation embryo. However, the extent to which blastoids can recapitulate the defining features of human post-implantation development remains unexplored. Here, we report that blastoids cultured on thick three-dimensional (3D) extracellular matrices capture hallmarks of early post-implantation development, including epiblast lumenogenesis, rapid expansion and diversification of trophoblast lineages, and robust invasion of extravillous trophoblast cells by day 14. Extended blastoid culture results in the localized activation of primitive streak marker TBXT and the emergence of embryonic germ layers by day 21. We also show that the modulation of WNT signaling alters the balance between epiblast and trophoblast fates in post-implantation blastoids. This work demonstrates that 3D-cultured blastoids offer a continuous and integrated in vitro model system of human embryonic and extraembryonic development from pre-implantation to early gastrulation stages.
Rowan M. Karvas,1 Joseph E. Zemke,1 Syed Shahzaib Ali,1,2 Eric Upton,1 Eshan Sane,1 Laura A. Fischer,1 Chen Dong,1 Kyoung-mi Park,1 Fei Wang,3 Kibeom Park,3 Senyue Hao,3 Brian Chew,1 Brittany Meyer,1 Chao Zhou,3 Sabine Dietmann,1,2, * and Thorold W. Theunissen1,4, * 1