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Melyssa Minto, PhD

Melyssa Minto, PhD

Certified

Bioinformatics Scientist

RTI International

Technologies

My Portfolio Highlights

My New Certification

SQL Associate

My New Certification

SQL Associate

Analytics maestro, transforming raw data into symphonies of knowledge.

My Work

Take a look at my latest work.

track

AI Fundamentals

Python
article

Context GenI AI: Using Generative AI in Genomics Research

Python
podcast

CompBio Cafe Podcast | Black Women in Computational Biology Network

track

AI Fundamentals

Python

My Certifications

These are the industry credentials that I’ve earned.

SQL Associate

SQL Associate

DataCamp Course Completion

Take a look at all the courses I’ve completed on DataCamp.

Courses

Introduction to SQL
Understanding Artificial Intelligence
Intermediate SQL
Joining Data in SQL

My Work Experience

Where I've interned and worked during my career.

RTI International | Oct 2022 - Present

Bioinformatic Scientist

- Created a cost efficient compute cluster using AWS EC2 and docker to execute parallelized statistical testing, analyzing DNA methylation for ~760,000 CpGs across ~120 individuals across two brain regions to efficiently determine tissue-specific and -shared DNA methylation differences in decedents diagnosed with alcohol use disorder. - Developed framework for gene-network replication testing to determine network persistence across studies. Reconstructed gene modules in replication datasets, calculated gene contributions, and compared them across datasets to validate replication. This framework facilitated the prioritization of genes for drug repurposing targeting across substance use disorders. - Contributed expertise in neurobiology, addiction, and genomics to 4 publications and 7 proposals across the Genomics and Translational Research Center. - Led small team in developing AI systematic literature review microservice for genes in a specific biological context. Planned project timelines and deliverables, establishing infrastructure for development across teams, and leveraging existing technologies to minimize duplication of efforts and optimize resource utilization.
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Duke University | Aug 2017 - Oct 2022

Graduate Student

- Developed a modular and scalable in-house pipeline to process raw next generation sequencing data on a high-performance computing cluster. This pipeline was widely implemented across the lab for alignment and analysis of DNase, ChIP-seq, RNA-seq, and other sequencing data. - Developed multi-omic integrative workflow to analyze and interpret gene expression and chromatin data. Used chromatin data to determine regulatory regions around induced genes as a search space for transcription factor (TF) binding. Cross-validated enriched transcription factor motifs with transcriptional enrichment to determine putative TFs regulating neuronal plasticity in a stimulus dependent manner. - Conducted time-course analysis of ChIP-seq of two histone modifications in the developing cerebellum. Established that histone markers H3K27ac and H3K27me3 in the developing cerebellum tend to regulate the same genes but in different genomic regions.

Duke University | Apr 2020 - Aug 2020

Project Manager

• Project managed a DukeData+ program which introduces undergraduates to explore data. • Guided the Data+ project team was tasked with piloting an environmental public health tracking tool for North Carolina to allow users to understand and visualize connections between social determinants, health measures (such as asthma rates and heart attacks), and environmental measures (such as PM 2.5 concentration).

ScitoVation | Jan 2017 - Jan 2018

Intern

- Harmonized ~45,000 chemical-assay plate data, bioactivity, and chemical property data from multiple sources, including U.S. EPA funded Collaborative Estrogen Receptor Activity Prediction Project, for chemical testing risk prioritization modeling. - Demonstrated the need for fit for purpose assays for compounds with high vapor pressure, disproportionately low hits in conventional plate assays and their high risk in neurological and hepatic systems, through exploratory data analysis and intuitive data visualization. This finding underpinned the company's decisions to pursue funding and development of assays to address this gap. - Developed a pipeline to perform a systematic literature search in Python using the PubMed API to curate publicly available data on a compound’s bioactivity as well as common terms discussed in the abstracts of research articles to assess the need for risk testing.

North Carolina Museum of Natural Sciences | Jun 2016 - May 2017

Research Intern

Use morphometric analysis to delimit species of crayfish. Incorporated machine learning algorithms to automate morphometric analysis an aid in the identification of characteristics that can be used to distinguish species of crayfish.

Biomedical/Biotechnology Research Institute at North Carolina

N/A

Central University | Jul 2015 - Dec 2016

Research Data Analyst

Served as a lead statistician on a dynamic team for a research project analyzing growth data, epigenetic data, and genetic data. Created R package, MonoInc, to clean longitudinal monotonic data. Created reproducible analysis final report using R Markdown – knitr that includes background methodology and results. Helped to develop the statistical methodology and experimental design for a behavioral zebrafish study that modeled fetal alcohol syndrome.

North Carolina State University | Jun 2016 - Aug 2016

Research Intern

Found differentially expressed genes in the lignin biosynthesis pathway in order to improve models for plants to up-regulate lignin for biofuels.

My Education

Take a look at my formal education

PhD in Computational Biology & BioinformaticsDuke University | 2022
B.S. in MathematicsMeredith College | 2017
B.S. in BiologyMeredith College | 2017

About Me

Melyssa Minto, PhD

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