College of Agriculture & Natural Resources Department of Plant, Soil and Microbial Sciences

Office location 
Great Lakes Bioenergy Research Center (GLBRC)
164C Food Safety and Toxicology

Google Scholar Profile
GitHub Page

Summary

            Dr. Gian Maria Niccolò (Nico) Benucci is a microbial ecologist, bioinformatician, and data analyst who co-leads a computational biology team at the Great Lakes Bioenergy Research Center (GLBRC), and teaches statistics for the Departments of Plant, Soil & Microbial Sciences and Statistics & Probability at Michigan State University (MSU). He and his team supports researchers and students in answering scientific questions by developing robust bioinformatics and machine-learning frameworks, standardized HPC pipelines, and open-source software — advancing both scientific rigor and the broader GLBRC mission of advancing sustainable biofuels and bioproducts.

            Dr. Benucci is a computational scientist fluent in R, Bash, and Python, HPC/SLURM environments, and works with workflow managers including Snakemake and Nextflow. He develops end-to-end pipelines for whole-genome, metagenomic, and amplicon sequencing (16S rRNA, ITS, shotgun) — spanning sequence processing, taxonomic profiling, functional annotation, and comparative genomics. His statistical toolkit covers uni- and multi-variate methods, simulations, and machine learning, grounded in rigorous design for observational and experimental studies. Dr. Benucci is an active open-source R developer, authoring and maintaining packages for microbiome analysis, environmental DNA classification, and data visualization (available on GitHub). He follows FAIR principles in data management and metadata curation, structures projects around version-controlled, documented codebases, and contributes to microbiome data standards through the National Microbiome Data Collaborative (NMDC). Dr. Benucci also co-leads the Bioinformatics Community of Practice (CoP) and bioinformatics support hours at GLBRC, and contributes to the Global Initiative of Sustainable Agriculture and Environment.

Courses:
STT464 – Statistics for Biologists (every other year).
PLB847 – Advanced Mycology, “Bioinformatics and Communuty Ecology with R” module (2015-2017).