Ashwini Sunkavalli
Boston,MA
Summary
Cell and molecular biologist with extensive experience integrating wet-lab research and computational analysis to investigate complex biological systems. Experienced in experimental design, biological data analysis, and connecting molecular and phenotypic findings across bacterial, viral, and mammalian models.
Overview
15
15
years of professional experience
Work History
Graduate Researcher
Tufts University School of Medicine
Boston, MA
01.2016 - 01.2025
- Designed and executed multifactor, time-resolved RNA-seq experiments to investigate transcriptional regulation and stress adaptation in Neisseria gonorrhoeae under iron-replete and iron-depleted conditions.
- Analyzed transcriptomic datasets using differential expression, time-course analysis, and operon-level interpretation to characterize dynamic bacterial stress responses.
- Developed and applied gene regulatory network and module-level analyses to identify candidate regulators associated with iron homeostasis, oxidative stress, and metabolic adaptation.
- Integrated transcriptomic findings with phenotypic assays, including growth, viability, oxidative stress tolerance, and epithelial cell invasion experiments, to connect molecular regulation with bacterial behavior.
- Performed motif discovery and computational regulatory analysis to prioritize putative transcriptional relationships and biologically relevant targets.
- Generated publication-style visualizations and analytical summaries to communicate complex multi-layer datasets and support biological interpretation.
Research Associate II
University of Massachusetts Medical School
Worcester, MA
01.2014 - 01.2016
- Investigated Influenza A virus replication, RNA biology, and viral fitness under antiviral pressure using molecular and cell-based assays.
- Performed viral mutagenesis, plaque assays, strand-specific quantitative PCR, and replication kinetics analyses to assess viral behavior across experimental conditions.
- Designed primers and quantitative assays to distinguish viral RNA species and support detailed analysis of infection dynamics.
- Contributed to experimental design, data interpretation, and project execution in virology and RNA biology studies.
Lead Research Technician - Cancer Genomics
Dana-Farber Cancer Institute
Boston, MA
01.2010 - 01.2014
- Supported large-scale whole-exome and whole-genome sequencing studies in cancer genomics through high-throughput sample processing and sequencing workflows.
- Managed next-generation sequencing library preparation, normalization, and quality control to maintain consistency across high-volume projects.
- Contributed to genomics studies published in Nature Genetics, JAMA Oncology, and The Lancet Oncology.
- Trained staff and collaborators on sequencing workflows, laboratory standards, and quality control practices.
Education
Master of Science (MS) - Genetics, Molecular & Cell Biology
Tufts University School of Medicine
12.2025
Master of Science (MS) - Biotechnology
University of Texas at Dallas
Bachelor of Science - Biotechnology
SRM University
Skills
- R programming
- Python
- Data visualization
- Reproducible computational workflows
- High-performance computing (HPC)
- RNA-seq preprocessing
- Quality control
- Differential expression analysis
- DESeq2
- EdgeR
- Limma
- FastQC
- Fastp
- Rockhopper
- Time-course transcriptomic analysis
- Custom integrative analysis
- Gene regulatory network inference
- Module-level analysis
- Network topology analysis
- Igraph
- Gephi
- Bayesian network inference
- Bnlearn
- Motif discovery
- MEME Suite
- Tomtom
- Operon prediction
- Operon-level transcriptomic analysis
- Public biological dataset integration
- Biological data visualization
- Scientific figure generation
- Integration of transcriptomic data
- Network data
- Phenotypic data
- DNA/RNA isolation
- Strand-specific RNA sequencing library preparation
- PCR
- Primer design
- QPCR-based quantification
- Bacterial growth assays
- Viability assays
- Oxidative stress assays
- Mammalian cell culture
- Viral infection assays
- Epithelial cell adherence assays
- Invasion assays
- Multifactor experimental design
- Phenotype-linked analysis
Skills
R programming for transcriptomic, statistical, and quantitative biological data analysis, Python for data visualization, reproducible computational workflows, high-performance computing (HPC) environments, RNA-seq preprocessing, quality control, and differential expression analysis, DESeq2, edgeR, limma, FastQC, fastp, Rockhopper, time-course transcriptomic analysis, custom integrative analysis of gene expression and phenotypic datasets, gene regulatory network inference and module-level analysis, network topology analysis using igraph and Gephi, Bayesian network inference using bnlearn, motif discovery using MEME Suite and Tomtom, operon prediction and operon-level transcriptomic analysis, public biological dataset integration and analysis, Biological data visualization in R and Python, scientific figure generation and interpretation, integration of transcriptomic, network, and phenotypic data for biological insight, DNA/RNA isolation and quality control, strand-specific RNA sequencing library preparation, PCR, primer design, and qPCR-based quantification, bacterial growth, viability, and oxidative stress assays, mammalian cell culture and viral infection assays, epithelial cell adherence and invasion assays, multifactor experimental design and phenotype-linked analysis
Selected Publications
- Wang A, Fairhurst AM, Liu K, Wakeland B, Barnes S, Malladi VS, Viswanathan K, Arana C, Dozmorov I, Singhar A, Du Y, Imam M, Moses A, Chen C, Sunkavalli A, et al., Communications Biology, 2024
- Sunkavalli A, McClure R, Genco CA., Molecular regulatory mechanisms drive emergent pathogenetic properties of Neisseria gonorrhoeae., Microorganisms, 2022
- McClure RS, Sunkavalli A, Balzano PM, Massari P, Cho C, Nauseef WM, Apicella MA, Genco CA., Global network analysis of Neisseria gonorrhoeae identifies coordination between pathways, processes, and regulators expressed during human infection., mSystems, 2020
- Campbell JD, Lathan C, Sholl L, Ducar M, Vega M, Sunkavalli A, et al., JAMA Oncology, 2017
- Pastor-Garcia A, Gonzalez-Barca E, Colomo L, Domingo-Domenech E, Sunkavalli A, et al., The Lancet Oncology, 2015
- Brastianos PK, Taylor-Weiner A, Manley PE, Jones RT, Dias-Santagata D, Thorner AR, Sunkavalli A, et al., Nature Genetics, 2014
Work Availability
monday
tuesday
wednesday
thursday
friday
saturday
sunday
morning
afternoon
evening
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Websites
- https://www.linkedin.com/in/ashwini-sunkavalli-research-scientist-computational-biology/
- https://scholar.google.com/citations?user=p6f6CA4AAAAJ&hl=en
- https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://sciprofiles.com/profile/2109631&ved=2ahUKEwjtv8Wl7MeVAxVhzvACHan7LBgQFnoECBYQAQ&usg=AOvVaw0tsIQIJxALD4k09W1GMXWe
Timeline
Graduate Researcher
Tufts University School of Medicine
01.2016 - 01.2025
Research Associate II
University of Massachusetts Medical School
01.2014 - 01.2016
Lead Research Technician - Cancer Genomics
Dana-Farber Cancer Institute
01.2010 - 01.2014
Master of Science (MS) - Genetics, Molecular & Cell Biology
Tufts University School of Medicine
Master of Science (MS) - Biotechnology
University of Texas at Dallas
Bachelor of Science - Biotechnology
SRM University