Boston, Massachusetts, United States
I am the Lead Bioinformatics Scientist in the East Coast Information Research Department at Abbvie, acting as a liaison between wet lab biologists, data scientists and software developers to create effective, efficient, and easy to use analysis workflows. My background in Genetics and Molecular Biology helps bridge the gaps between experimental design, execution, and analysis. As the Director of Bioinformatics at Honeycomb Biotechnologies, I led the coding and product development efforts for BeeNetPLUS, the user-friendly single cell RNA-seq analysis pipeline used for data that comes through the HIVE scRNAseq Solution. As an HHMI postdoctoral fellow in the Seidman lab at Harvard Medical School I utilized single cell and single nuclei RNA-seq and ATAC-seq technology to determine the mechanism of congenital heart disease mutations. Skills: Languages: R/R Shiny/R Markdown, Bash, SLURM, WDL, Linux, Python Bioinformatics: compute cluster analysis, Zendesk, AWS, Google Cloud Platform, Docker, analysis of Next-Gen Sequencing (NGS) Data (RNA-seq, scRNA-seq, snRNA-seq, ATAC-seq, scATAC-seq, ChIP-seq, 4C, TCR), GitLab/GitHub Biochemistry & Biology: Nucleofection (CRISPR, base editing), Immunoprecipitation, Chromatin Immunoprecipitation, ATAC-seq, Library Prep (Illumina), PCR, qPCR, Cell Culture (stem and immortalized cells), Cryosectioning, Immunohisotochemistry/ Immunofluorescence, Nuclei/RNA/Protein extraction, RNA-scope, Western Blot Other: Documentation Management, SOP creation, Product Development, Management, Task Delegation, Communication with Stakeholders, Communication with Academic Scientists, Time Management, Self-Sufficiency, Team Player
• One-on-one tutoring of 5-10 PIs, postdocs, and graduate students in R, shell scripting, and compute clustering via Zoom. • Established lab-wide protocols and best-practices for compute clustering analysis of next-generation sequencing data. • Wrote and implemented working pipelines for analysis of ChIP-seq, RNA-seq, and ATAC-seq data
• Created a congenital heart disease single-nuc RNA-seq atlas from human tissue to determine biological mechanism for human disease. • Develop an algorithm to prioritize non-coding variants for pathogenicity of disease as part of the Pediatric Cardiac Genomics Consortium (PCGC). • Established lab-wide protocols, analysis pipelines, and best-practices for ATAC-seq, single cell ATAC-seq, and ChIP-seq experiments. • Curated epigenetics datasets (ChIP-seq, ATAC-seq, and RNA-seq data) at multiple timepoints across cardiomyocyte differentiation as a reference atlas. • Characterized biological mechanisms for the role of transcription factors GATA6, CHD4, and CHD7.
Preferred Editor: 8/2018-3/2019