Erik Wright
Baylor College of Medicine, TX·Genetics
Every new genome needs a map that marks where its genes and other working parts lie, and for microbes that map is almost always drawn by software. Current tools leave large gaps.
- Fields
Genetics
Computer Science
Microbiology & Immunology
- Topics
genome annotation
comparative genomics
bioinformatics
- Grants
- 2 known grants, through May 2030
What they work on
- Develop a program that finds protein-coding genes in prokaryotic genomes, using new signatures of true genes to make it more accurate than existing gene-finding tools, including for small proteins.
- Find undiscovered non-coding RNAs, RNA molecules that do not make protein, by training machine-learning models on signals that separate them from empty intergenic DNA.
- Use comparative genomics, comparing many related genomes, to infer the likely functions of poorly understood non-coding RNAs.
- Develop new methods for measuring coevolution, matching evolutionary change between genes, and apply them to a very large set of genomes spanning the microbial tree of life.
- Build a public web tool of the resulting coevolving-gene network, plus web apps that let researchers link proteins of unknown function to known ones.
More about the research
They miss very small proteins because of size cutoffs. They overlook RNA molecules that do not make protein unless those RNAs are already in reference databases. In a typical bacterial genome, little is known about more than half of the proteins. This hidden "dark proteome" may include proteins that help microbes cause disease or resist antibiotics. The lab's two projects use computation, comparative genomics (comparing many genomes) and machine learning to fill these gaps.
Why it matters
Better microbial genome maps could help biomedical researchers find small proteins, regulatory RNAs and disease-related genome features that current tools miss. These include possible new drug targets. Linking unknown proteins to known ones may also suggest new candidates involved in virulence and antibiotic resistance. Free web tools would give researchers who study many different microbes access to these annotations.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Paul Marjoram
University of Southern California, CA·Mathematics & Statistics
Develop a probabilistic evolutionary model using phylogenetic trees and experimental Gene Ontology annotations to automatically predict functions of uncharacterized genes.
Todd Treangen
Rice University, TX·Computer Science·Early-career
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.
Yunan Luo
Georgia Institute of Technology, GA·Computer Science·Early-career
Develop machine learning methods that learn protein representations from amino acid sequence and 3D structure, in order to explain how composition and shape determine protein function.
The school
Baylor College of Medicine: 38 labs on PI Pages, $881M a year on research, 115 research doctorates a year.
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