My name is Josh Loecker, and I am currently a 5th-year graduate student at the University of Nebraska-Lincoln obtaining a Doctorate in Biochemistry with a Specialization in Bioinformatics. Despite this, I consider myself more of a Software Engineer than biologist. I am skilled in working with and building, high-performance pipelines and solutions, and have a passion for developing robust and maintainable software that is usable by individuals without computational expertise.
The 'Project' section is still a work-in-progress.
Description goes here.
Constraint-based Optimization of Metabolic Objectives (COMO, DOI) is a comprehensive, user-friendly pipeline designed to streamline the integration of multi-omics data for metabolic modeling and drug discovery. By combining heterogeneous datasets (including bulk and single-cell RNA-seq, microarrays, and proteomics) with genome-scale metabolic models, COMO allows researchers to efficiently construct context-specific models for various cell and tissue types within a unified Docker or Conda environment. The pipeline automates complex tasks such as data processing, simulation, and drug perturbation analysis to identify potential therapeutic targets and repurposable drugs. COMO was validated through a study on B cell metabolism in rheumatoid arthritis and systemic lupus erythematosus and offers a robust computational solution for accelerating the discovery of low-cost, effective disease treatments.
FastqToGeneCounts is a highly parallelized Snakemake workflow designed to automate the processing of bulk RNA-seq data on high-performance computing (HPC) clusters. Tailored to interface seamlessly with NCBI's Gene Expression Omibus Database, the pipeline requires only a list of SRR codes to begin: it handles parallel downloading and unpacking of raw data, performs quality control (using FastQC and MultiQC), optionally trims reads (Trim Galore), and aligns sequences to the genome using STAR. While specifically optimized to format inputs for metabolic drug discovery and repurposing packages (such as COMO), FastqToGeneCounts serves as a robust, standalone solution for any researcher needing to efficiently generate standardized gene count matrices and quality metrics for differential gene expression analysis.
I built this page using pure HTML + CSS, inspired by the websites of Guido van Rossum (the creator of Python) and Bjarne Stroustrup (the creator of C++). The entire content of this website is contained in two files: index.html and style.css. This is a callback to my passion for writing fast, maintainable software; there are (effectively) zero dependencies, absolutely no out-of-date third-party packages, and it places an extremely minimal load on my home server to host this site (which my wife and electricity bill appreciate :D).