Todd Treangen
Rice University, TX·Computer Science·Early-career
Reading DNA through sequencing and writing it through synthesis are now cheap and fast. This creates both an opportunity and a risk: huge DNA datasets could reveal hidden microbes, but engineered DNA could also be misused.
- Fields
Computer Science
Microbiology & Immunology
Ecology & Evolution
- Topics
bioinformatics
computational biology
metagenomics
- Grants
- 2 known grants, through Apr 2028
What they work on
- Identify yet-unseen pathogens by linking taxonomy-based classification of sequences with genes whose functions are of concern, so that novel threats can be recognized in sequencing data.
- Catalog known and rare microbial genes, or "microbial dark matter," across petabyte-scale sequence collections, using probabilistic algorithms combined with comparative genomics.
- Tell engineered DNA apart from natural DNA by building graph-based pan-genomes, which capture all known gene variants, and combining them with codon usage bias models.
- Build GuarDNA, a modular open platform for biosecurity and biosurveillance, and evaluate each part and the whole system with continuous integration testing.
- Link species abundance networks with genome-exchange networks in Yellowstone hot spring microbial mats, using genome assembly graphs built from metagenomic sequencing data.
- Model how microbial gene-exchange networks change over time with a stochastic graph grammar framework, and use deep graph generators to find new rules for gene duplication and horizontal gene transfer.
- Combine genome-level simulations with the detected genomic events to estimate the evolutionary benefits and rates of these events. Then characterize community cooperation and resiliency through network analysis.
More about the research
This work builds computational tools to make sense of microbial DNA at scale. One project aims to detect new or engineered pathogens. The other works out the rules that let microbial communities cooperate and stay stable. Both rely on metagenomics, the sequencing of all DNA in an environmental or host sample, combined with new algorithms built on graphs.
Why it matters
Tools that flag unfamiliar pathogens and engineered DNA could strengthen pandemic preparedness and biosecurity screening, which would help public health agencies and DNA synthesis providers. Learning how microbial communities adapt and recover from disturbance could inform conservation, the management of animals and plants, and health research on host-associated microbiomes. All software will be released as open source.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Angela Oliverio
Syracuse University, NY·Ecology & Evolution
Measure how strain diversification and horizontal gene transfer affect microbiome assembly and function, using experimental evolution of synthetic sourdough and other fermented food communities.
Freddy Bunbury
University of Chicago, IL·Microbiology & Immunology
Identify genome features, such as amino acid frequencies and gene content, that predict the optimal growth temperature of cyanobacteria, using thermophilic hot-spring strains.
Javier Tabima Restrepo
Clark University, MA·Ecology & Evolution
Reconstruct the evolutionary relationships among Basidiobolus species, using genome sequences from fungal samples collected around the world.
The school
Rice University: 24 labs on PI Pages, $278M a year on research, 267 research doctorates a year.
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