M.S. in Bioinformatics and Medical Informatics (May 2026), actively looking for roles in biotech where I can apply computational and molecular biology skills to problems in genomics, proteomics, drug discovery, or translational research. Currently wrapping up thesis research in the Blanco-Suárez Lab on astrocyte proteomics and CNS repair.

I build -omics pipelines in Nextflow and Snakemake, but the part I care about most is getting the methodology right for the data at hand. I'm particularly interested in exploring emerging methods and multi-method approaches for multi-dimension data. That's meant designing separate analytical tracks for data that behave differently, building application-level caching so incremental study expansions don't burn through API budgets, and designing imputation strategies that distinguish biological absence from technical dropout. My projects have taken that approach across proteomics, population genomics, and transcriptomics.

ProSIFT — A generalized discovery proteomics pipeline, and my most ambitious project. Nextflow DSL2 end-to-end workflow: input validation, UniProt ID mapping, normalization, missingness-aware imputation (MNAR/MAR-stratified MinProb + KNN), differential abundance (limma + DEqMS via rpy2), and functional enrichment (ORA and preranked GSEA via gseapy against MSigDB). Queries five external databases in parallel (UniProt, PubMed, DisGeNET, DGIdb, CTD) to build integrated per-protein annotation profiles. Outputs structured Parquet tables, interactive HTML reports, and diagnostic plots. Manuscript in preparation.

Ketamine_Astrocyte_Proteomics — Snakemake pipeline for my MS thesis, analyzing how ketamine reshapes the cortical astrocyte proteome. Mouse TMT-labeled DDA LC-MS/MS data. Welch's t-test differential abundance with a separate analytical track for presence/absence proteins, g:Profiler GO/KEGG enrichment with a custom detected-proteome background, REVIGO redundancy reduction, and a three-tier validation framework designed around the limitations of n=3 biological replicates. All thresholds and paths externalized to config.yaml.

Coccinella_Invasion_Genomics — Population genomics pipeline asking which regions of the ladybug genome show signatures of selection during invasion. Parallel selection scans (percentile FST via vcftools + PCA-based outlier detection via pcadapt) with OutFLANK concordance validation. Maps candidate SNPs to genes via a local multi-species BLAST ortholog chain and tests for GO-term overrepresentation using clusterProfiler with pattern-stratified foreground sets. Includes an independent MaxEnt environmental niche model for current and projected habitat suitability.

Protein_Structure_Classification — Multi-label classification of protein functional categories from PDB biophysical features. A custom keyword-matching engine parses free-text PDB classification strings (handling binding disambiguation and DNA/RNA priority logic) into a 23-class binary target matrix. Three classifiers compared: Random Forest, Decision Tree (scikit-learn), and Neural Network (TensorFlow/Keras).

I also make things for fun! Glim — A browser-based wellness companion PWA with an interactive creature, habit tracking, and cross-device sync. A pet project (literally) for learning frontend development.

Languages: Python · R · SQL · Bash

Pipeline/workflow: Nextflow (DSL2) · Snakemake

Proteomics: limma · DEqMS · g:Profiler · gseapy · rrvgo · REVIGO · Proteome Discoverer · MaxQuant

Genomics: vcftools · PLINK · BLAST+ · bcftools · pcadapt · OutFLANK · GATK · samtools · BWA · DRAGEN (limited)

Enrichment and annotation: clusterProfiler · enrichR · UniProt REST · NCBI E-utilities · DisGeNET · DGIdb · CTD · STRING · Cytoscape · GO · KEGG · MSigDB

ML: scikit-learn (RandomForest, KNN, IterativeImputer) · TensorFlow/Keras

Visualization: plotly · matplotlib · seaborn · ggplot2 · kaleido · Tableau

Data and infrastructure: pandas · numpy · scipy · pyarrow/Parquet · Git · conda · HPC clusters (PBS/Torque, Jetstream2) · limited AWS experience (Redshift, Athena, Glue)

I'm actively seeking bioinformatics and data-focused roles in biotech. Feel free to reach out via email at reinahastings13@gmail.com or check out my LinkedIn.