Jessica Mitchell

Computational biologist and experimental scientist adept at analyzing and interpreting NGS, genomic, and transcriptomic datasets to uncover biological mechanisms and generate testable hypotheses. Experienced in NGS data processing and QC, differential expression analysis, weighted gene co-expression network analysis (WGCNA), statistical analysis, data visualization, genome annotation, pathway analysis, and integration of genomic data with physiological, metabolic, and experimental measurements. Strong hands-on experience with R, Python, Perl, Linux, and high-performance computing environments, with a first-author publication in Nature Microbiology demonstrating the translation of bioinformatics analyses into new biological insights.

Brings an experimental background spanning study design through NGS data generation and analysis, enabling computational results to be interpreted in their biological and experimental context.

Scientist II/ Scientist III at Breaking, Inc. (2024-07 – 2026-06)

• Led multidisciplinary R&D projects integrating molecular biology, NGS, microbial phenotyping, analytical methods, and quantitative analysis; developed and validated qPCR/v-qPCR and sequencing workflows with responsibility for study design, optimization, quality control, troubleshooting, and interpretation.
• Built reproducible R-based analysis workflows using statistical modeling, multivariate analyses, random forests, and ggplot2 visualization, integrating molecular, physiological, chemical, and sequencing-derived data to identify biological drivers and guide subsequent experiments.
• Led projects from study design through analysis and communication of results, developed SOPs and reproducible research workflows, collaborated across scientific and engineering disciplines, trained scientists, and supervised and mentored engineering interns.

Postdoctoral Research Fellow, Girguis Lab at Harvard University (2020-06 – 2024-06)

• Analyzed NGS transcriptomic datasets from complex biological systems, performing data processing and QC, differential expression analysis, statistical analysis, visualization, and biological interpretation; developed and adapted computational workflows using R, Python, and Perl in Linux/Unix and high-performance computing environments.
• Performed weighted gene co-expression network analysis (WGCNA) to identify co-expression modules, hub genes, and pathway relationships, integrating these results with genomic, physiological, metabolic, chemical, and experimental data to uncover connections between carbon-fixation pathways and broader cellular metabolism and generate testable hypotheses.
• First author of a peer-reviewed Nature Microbiology paper resulting from this work, leading the computational analysis, biological interpretation, data visualization, and scientific writing; collaborated with multidisciplinary researchers and communicated findings through publications and scientific presentations.

PhD in Organismic and Evolutionary Biology – Harvard University (2014 – 2020)