Renmao Tian, Ph.D.

Profile

Computational biology with a practical path to diagnostics and surveillance.

I develop bioinformatics tools and analysis systems that make microbial sequence data more useful: from marker-gene pipelines and bacterial core-gene phylogenomics to machine-learning classifiers for plasmids and pathogen typing.

Research identity

I work at the interface of microbial genomics, foodborne pathogen surveillance, machine learning, and high-throughput sequencing operations. Current work at Illinois Tech's Institute for Food Safety and Health includes sequencing facility support, Galaxy-based analysis workflows, bacterial pathogen genomics, and participation in FDA GenomeTrakr network activities.

Core capabilities

Research Focus

Pathogen genomics, microbial systems, and methods that scale.

My research program is organized around tools that improve how complex microbial data are generated, interpreted, and translated into decisions for food safety, public health, and microbial ecology.

Foodborne pathogen surveillance

Genome-informed analysis of pathogens including Clostridium botulinum, pathogenic E. coli, and other organisms relevant to food safety and public-health risk.

Machine learning for sequence interpretation

Feature engineering and classifier design for plasmid prediction, pathogen typing, rapid sequence search, and scalable analysis of bacterial genomes.

Reproducible bioinformatics platforms

Pipeline and web-server development for marker-gene amplicon analysis, phylogenomics, long-read sequencing workflows, and user-facing Galaxy tools.

Microbial ecology and evolution

Comparative genomic and metagenomic analysis of microbiomes from groundwater, marine sponge, soil, gut, and engineered environments.

Sequencing facility enablement

Support for high-throughput sequencing analysis, data infrastructure, collaborator training, and end-to-end computational interpretation.

Assay and technology translation

Design of sequencing-based pathogen detection and typing workflows that can connect computational validation to wetlab, regulatory, and industry use cases.

Patent-Pending Platform

SpeseroQ: culture-independent pathogen typing and quantification.

SpeseroQ is a patent-pending long-amplicon sequencing strategy designed to identify pathogens, classify pathogenic types or subtypes, and estimate absolute abundance from complex samples.

The provisional design includes 50 species-gene primer modules across 10 priority pathogen groups, evaluated against 40,666 genomes, with mean typed-classification accuracy of 98.8% in the current in-silico analysis.

Replaces large sets of type-specific qPCR probes with universal primer modules and sequence-level classification.
Targets food, clinical, environmental, and public-health matrices where culture isolation can be slow or expensive.
Creates a platform for academic validation, industry partnership, and translational diagnostic development.

SpeseroQ workflow from sample to pathogen modules, long amplicons, classifier and qAmp, and decision report.

Featured Work

Tools and studies used across microbial genomics and bioinformatics.

Selected work highlights a consistent theme: build methods that turn microbial sequence data into clearer biological or operational insight.

PlasmidHunter

Machine-learning plasmid prediction using gene-content profiles, reported at 97.6% accuracy and published in Briefings in Bioinformatics.

View code

VBCG

Twenty validated bacterial core genes for high-fidelity and high-resolution phylogenomic analysis, derived from 30,500 genomes and published in Microbiome.

ASAP 2

A pipeline and web server to analyze marker-gene amplicon sequencing data automatically and consistently.

Galaxy server

Patescibacteria genome simplification

Advanced the "less is more" model for groundwater Patescibacteria adaptation, with 350+ citations and coverage by Berkeley Lab.

Marine sponge microbiomes

Pioneered genomic and metagenomic analysis of marine sponge-associated sulfur-oxidizing bacteria and ammonia-oxidizing archaea.

Botulinum neurotoxin diversity

Analyzed BoNT gene-cluster variability, plasmid/chromosome integration, and toxin type diversity relevant to pathogen typing.

Experience

Research roles spanning sequencing operations, environmental genomics, and microbial bioinformatics.

2019 - Present | Illinois Institute of Technology

Research Scientist, High Throughput Sequencing Initiative, Institute for Food Safety and Health

Develops bioinformatics tools, sequencing analysis pipelines, and machine-learning methods for microbial genomics, pathogen analysis, and food safety surveillance. Supports sequencing and bioinformatics infrastructure through the HTS Initiative and its Galaxy analysis server.

2016 - 2019 | University of Oklahoma

Postdoctoral Research Associate, Institute for Environmental Genomics

Built automated bioinformatics pipelines for amplicon, genome, and metagenome analysis while studying microbial ecology and evolution in water, soil, gut, and other complex environments.

Funding

Grant writing and collaborative proposal development.

Recent co-investigator activity focuses on pathogen reservoirs, computational genomics, fast sequence search, and sequencing-enabled food safety infrastructure.

FDA GenomeTrakr Network

Co-Investigator. "Identifying the Reservoirs of Microbial Pathogens in Illinois." Illinois Institute of Technology/Institute for Food Safety and Health. $550,000, 5 years, awarded in 2025.

FASS: Fast Alignment-free Sequence Search

Co-Investigator. Computational genomics and data-science proposal activity through NIH R21 and NSF infrastructure programs, focused on machine-learning sequence search.

Teaching & Service

Classroom teaching, mentorship, editorial leadership, and trainee support.

Adjunct Professor of Microbiology

Roosevelt University, 2023. Developed and delivered Microbiology and Microbiology Laboratory courses for undergraduate and graduate students, with student evaluation scores of 4.2 and 4.3 out of 5.

Editorial and academic service

Academic Editor for Microorganisms; Guest Editor for Genes special issue; Research Topic Editor for Frontiers in Microbiology on bacterial pathogens and virulence factor gene diversity and evolution.

Selected Publications

Representative publications across microbial ecology, pathogens, and bioinformatics.

See the publication search link for the full record of 80+ publications.

Tian, R. and Imanian, B. (2024). PlasmidHunter: Accurate and fast prediction of plasmid sequences using gene content profile and machine learning. Briefings in Bioinformatics, 25(4).

Tian, R. and Imanian, B. (2023). VBCG: 20 validated bacterial core genes for phylogenomic analysis with high fidelity and resolution. Microbiome, 11, 247.

Tian, R. and Imanian, B. (2022). ASAP 2: A pipeline and web server to analyze marker gene amplicon sequencing data automatically and consistently. BMC Bioinformatics, 23, 27.

Tian, R. et al. (2020). Small and mighty: adaptation of superphylum Patescibacteria to groundwater environment drives their genome simplicity. Microbiome, 8, 1-15.

Tian, R. et al. (2021). Integration of complete plasmids containing bont genes into chromosomes of Clostridium parabotulinum, Clostridium sporogenes, and Clostridium argentinense. Toxins, 13(7), 473.

Tian, R. et al. (2014). Genomic analysis reveals versatile heterotrophic capacity of a potentially symbiotic sulfur-oxidizing bacterium in sponge. Environmental Microbiology, 16(11), 3548-3561.

Education

Training in marine microbiology, microbial epidemiology, and biological science.

Ph.D., Marine Microbiology

Hong Kong University of Science and Technology, 2011-2015. Genomics and metagenomics of marine microbiomes.

M.Sc., Microbiology

Beijing Institute of Microbiology and Epidemiology, 2008-2011. Genomics and evolution of bacterial pathogens.

B.Sc., Biological Science

Ludong University, 2004-2008. Foundation in biological sciences and microbiology.