JOHN J. CAPERELLA Jr., MS 13 Crotona Avenue, Harrison, NY 10528 • 914-484-0257 • jcaperella@gmail.com https://github.com/jcaperella29 Bioinformatician & Data Analyst MS in Bioinformatics with 2+ years computational research experience EDUCATION Brandeis University, Waltham, MA May 2024 MS, Master of Science in Bioinformatics, GPA: 4.0 Pace University, Dyson College of Arts and Sciences, Pleasantville, NY Bachelor of Science in Biochemistry, cum laude; Mathematics Minor May 2019 Honors/Awards: Biochemistry Excellence Award, Induction into the Pace University Dyson Society of Fellows, Dean’s Scholarship, Dean’s List, Multiple Outside Presentation Awards SKILLS Coding languages /deployment/ data processing tools: Proficient in R (statistical packages, machine learning, Bioconductor, Shiny); Python (machine learning, Biopython); Bash; Nextflow, Docker, CWL, Google Cloud Platform. Knowledge of SQL and web development. Genomics/Genetics software and methods: Experience with analytical tools including genome-wide association studies with PLINK and R, burden tests, meta-analysis with METAL, command line FASTQ processing tools, variant calling, ATAC peak analysis and De novo genome assembly. Transcriptomics: Some experience in the development and usage of scripts for processing microarray, RNA -seq and ScRNA-seq data, preparing counts matrices as well as performing meta-analysis on RNA-seq studies in R as well as weighted correlation network analysis. Machine learning and dimension reduction: Development and usage of Linear Regression, Logistic Regression, Large Language models, Random Forest and Neural Network models in R and Python. Usage of hierarchical and k-means clustering and dimension reduction methods such as principal components analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Structural Biology: Experience with tools such as PyMOL, AutoDock and AlphaFold. Databases: Experience finding relevant data from databases such as Genebass, Ensembl, UCSC, NCBI, Protein Data Bank, Open Targets Genetics, UniProt, Gene Ontology. RESEARCH & PROJECTS “RNA_SEQ_Processing App” – An all-in-one R Shiny web app for interactive RNA-seq analysis — no coding required. It includes differential expression, power analysis, Random Forest, PCA/UMAP, and pathway enrichment, a clean, tabbed UI with built-in visualizations and downloadable results. It is fully Docker, Singularity, and Apptainer compatible — perfect for HPC or local use. Designed for bioinformatics teams and core facilities. Built for real-world datasets, reproducibility, and ease of use. The GitHub repository for it can be found at https://github.com/jcaperella29/RNA_SEQ_APP. “JCAP GWAS Shiny App” – Interactive R Shiny platform for SNP QC, GWAS, gene mapping, pathway enrichment, and machine learning-based prediction. Accepts VCF/phenotype/covariate files, supports data visualization (PCA, UMAP, Q-Q, Manhattan plots), gene annotation, and power analysis. Fully containerized (Singularity/HPC-ready). It can be found at https://github.com/jcaperella29/GWAS_SHINY_APP. “Bioinformatics Tools Hub”- Front-End Web Development + Scientific Tool Integration | HTML, CSS, JS | Designed and deployed a custom biotech-themed web portal to showcase RNA-Seq, ATAC-Seq, miRNA-Seq, GWAS and other sequence analysis applications. Implemented a responsive, multi-tab interface using HTML, CSS, and JavaScript for clear navigation of bioinformatics tools. Integrated animated DNA-inspired visuals and deployed using GitHub Pages for public access and open science collaboration. Enhanced accessibility for non-programming researchers by centralizing complex data workflows into a clean, web-based user interface. It can be found at https://jcaperella29.github.io/JC_BIOINFORMATICS_HUB/. RELEVANT EXPERIENCE Independent Software Engineer 2024–Present Designed, built, and deployed a range of tools including Flask and Shiny web applications, Python command-line interfaces, and APIs. Undertaking projects focused on providing user-friendly solutions to real-world problems in both biology and business contexts. Selected work is available on my GitHub @ https://github.com/jcaperella29. Pace University, Pleasantville, NY Computational Research Assistant- Department of Biology 2021 - 2024 • Automated zebrafish hair cell counting with Python GUI and reporting • Used ML and AlphaFold to predict Cxcr4b receptor partners. • Provided computational support for the lab. • Introduced the team to text mining tools and their use in literature review. • Supported wet lab work and zebrafish system upkeep. Pace University, Pleasantville, NY Research Assistant- Department of Biology 2020 -2021 • Provided lab support via zebrafish husbandry and dechorionation; RNA extraction; RT-PCR; agarose gel electrophoresis; EdU labeling and Click chemistry; confocal microscopy, and sequence analysis. Pace University, New York, NY Biology and Chemistry Tutor 2019 -2020 • Provided support to undergraduates for General Biology, General Chemistry I and II, and Organic Chemistry; Clarified course materials to enhance students’ understanding; Conducted pre-exams reviews; Reviewed students’ exam results and provided reinforcement of course materials.