Michael Molnar
Principal Bioinformatics Scientist II
Ontario, Canada
Summary
👤
Senior🎓
Top SchoolMichael Molnar is a Principal Bioinformatics Scientist with 8 years of experience at the intersection of genomics, algorithm development, and cloud-scale engineering, currently driving bioinformatics at Roche. He has a track record of turning research-grade methods into production-ready pipelines—improving detection rates for structural variants, cutting alignment runtimes by an order of magnitude, and automating performance metrics for faster decision-making. His expertise spans long- and short-read sequencing (Illumina, PacBio, ONT, Bionano OGM), variant calling, assembly, methylation and chromatin accessibility analysis, and he contributed methylation-calling enhancements to the widely used nanopolish project. With a PhD in Bioinformatics and publications including lead-author work in Nature Methods, he bridges academic rigor and pragmatic engineering, often leveraging HMMs and cloud infrastructure to scale insights for clinical and research teams.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Bioinformatics, Doctor of Philosophy - PhD, Bioinformatics at Western UniversityDiploma, Business Information Systems, Diploma, Business Information Systems at Fanshawe College
Github Skills (9)
algorithm10
bioinformatics10
algorithms10
c-language10
cprogramming-language10
python9
genome-assembly8
cplus7
cpp7
Programming languages (6)
Github contributions (5)
jts/nanopolish
May 2018 - Mar 2019
Signal-level algorithms for MinION data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 commits, 10 PRs, 7 comments in 10 months
Contributions summary:Michael primarily focused on enhancing the methylation calling functionality within the nanopolish repository. Their work included implementing support for various methylation types, modifying the core algorithm to detect and process methylation sites. Additionally, they added strand information to the methylation calls, and optimized the training process for the GpC model, including reservoir sampling. These changes involved modifications to both C++ source files and Python scripts.
signalpythonsciencegenome-assemblyc-plus-plus
mike-molnar/nanopore-workflow
Apr 2022 - May 2022
Snakemake worflow to process and filter long read data from Oxford Nanopore Technologies.
Contributions:1 release, 76 commits, 1 PR in 25 days
technologiesnanoporelong-read-dataoxford-nanoporeoxford
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