https://www.golgi.bio/team/gina-turco

Project: Modeling vascular differentiation as a biological system● Used Python and R (Non-Hierarchical Clustering) to systematically map the gene regulatory network (GRN) involved in plant biomass production and vascular differentiation at a cell-type-resolution.● Integrated microarray and RNA-seq data with our GRN (244 nodes, 621 edges) to model the GRNs response to environmental stress with use of co-expression and motif analysis.● Used cytoscape.js/Javascript to develop web based visualizations of gene regulatory networks.Project: Mathematically modeling the relationship between gene expression and vascular cell identity● Generated single cell sequencing (Drop-Seq) data on transgenic plants and analyzed the results through Drop-Seq packages in R (Seurat and Monocle).● Developed novel modeling algorithms in Python to determine the nonlinear relationship between a cell's expression pattern and cell identity.● Performed high performance computing on XSEDE (SLURM) and SGE based clusters.● Development of algorithms and pipelines for analysis of large-scale genomic datasets with the use of NGS tools (bwa, bowtie, STAR, samtools, bedtools).● Collaborated with the Joint Genome Institute.Project: Transcriptome and epigenetic regulation of vascular tissue in crop species ● Generated tissue-specific RNA-seq data with laser capture microdissection (LCM) of Sorghum.● Used comparative genomics to compare and contrast the vascular transcriptome of the model species Arabidopsis to the crop species Sorghum and Maize.● Used machine learning (PCA) and statistical analysis, ANOVAs, enrichment testing and Bayesian Modeling in R to describe the relationship between DNA methylation data (bisulfite sequencing data) from multiple tissue types and tissue specificity. ● Performed high throughput computing on AWS (Hadoop) and Open Science Grid (HTCondor ). ● Organized and queried large genomic datasets with HIVE and SQL.● Collaborated with the Joint Genome Institute.

Teaching Assistant for BIS180L Genome Biology Lab Course● Taught students how to develop web-based applications in Shiny for visualization of plant transcription and expression quantitative trait locus (QTL) data. ● Taught students how to use Freebayes and NGS tools (samtools, BWA, blast, IGV) to identify SNPs and perform genome-wide association studies (GWAS) in R.● Taught students how to perform differential gene expression analysis in R while fitting a generalized linear model.● Taught students version control in git and organization with R notebook and markdown formatting.

● Developed software in Python to automate the identification of conserved non-coding sequences (CNS) from whole genome sequencing (WGS) data.● Used git and wrote unit tests to identify bugs and ensure non-regressions in code.● Contributed to the development of a web based visualization tool (CoGe) for comparative genomics where results were formatted and submitted through a SQL database. I also provided outputs in UCSC formats for visualization.

Used qRT-PCR and flow cytometry to determine which genes are turned on in response to activation of the steroid and xenobiotic nuclear receptor (SXR) and their role in tumor suppression of Non-Hodgkin's Lymphoma. This project focused on endocrine disrupting chemicals (EDCs) that interact with SXR to regulate gene expression and cause cancer.