Travis Wheeler
- Associate Professor
- Member of the Graduate Faculty
Contact
- (520) 621-7253
- Roy P. Drachman Hall, Rm. B207A
- Tucson, AZ 85721
- twheeler@arizona.edu
Biography
Travis Wheeler is an Associate Professor in the University of Arizona College of Pharmacy Practice. He earned his bachelors in Evolutionary Biology from the University of Arizona, then his PhD in Computer Science from UArizona, with a research emphasis on computational genomics. He spent 5 years as a postdoc and research scientist in the research group of Sean Eddy at HHMI Janelia Research Campus, then joined the Computer Science faculty at the University of Montana in 2014, where he remained until his move back to Arizona in 2022. Dr. Wheeler leads a group (http://wheelerlab.org/people) with research focus that can be broadly described as “algorithms and machine learning approaches for computational biology”, primarily emphasizing applications to genomics, drug discovery, and animal behavior classification.Degrees
- Ph.D. Computer Science
- University of Arizona, Tucson, Arizona, United States
- Efficient construction of accurate multiple alignments and large-scale phylogenies
- B.A. Ecology and Evolutionary Biology
- University of Arizona, Tucson, Arizona, United States
Work Experience
- Department of Pharmacy Practice & Science, University of Arizona (2022 - Ongoing)
- Department of Computer Science, University of Montana (2019 - 2022)
- Department of Computer Science, University of Montana (2014 - 2019)
- HHMI Janelia Research Campus (2011 - 2014)
- HHMI Janelia Research Campus (Sean Eddy) (2009 - 2011)
- University of Arizona, Tucson, Arizona (2000 - 2003)
- Intuit, Inc (1995 - 2000)
Interests
Teaching
Computation (introductory, through advanced algorithms)BioinformaticsMachine LearningProbabilistic ModelingDrug Discovery
Research
Computational biology: - Algorithms, Machine Learning, Software engineering- Genomics, proteomics, drug discovery, animal tracking/behavior
Courses
2023-24 Courses
-
Honors Thesis
BIOC 498H (Spring 2024) -
Pharmacy Administration
PHSC 596E (Spring 2024) -
Research
CSC 900 (Spring 2024) -
Rsrch Ecology+Evolution
ECOL 610A (Spring 2024) -
Thesis
CSC 910 (Spring 2024) -
Directed Research
ACBS 492 (Fall 2023) -
Honors Thesis
BIOC 498H (Fall 2023)
2022-23 Courses
-
Honors Directed Research
BIOC 392H (Spring 2023) -
Independent Study
PHSC 599 (Spring 2023) -
Honors Directed Research
BIOC 392H (Fall 2022) -
Honors Thesis
MCB 498H (Fall 2022) -
Research
PHSC 900 (Fall 2022)
Scholarly Contributions
Journals/Publications
- Krause, G., Shands, W., & Wheeler, T. J. (2024). Sensitive and error-tolerant annotation of protein-coding DNA with BATH. bioRxiv.
- Olson, D. R., Demekas, D., Colligan, T., & Wheeler, T. (2024). NEAR: Neural Embeddings for Amino acid Relationships. bioRxiv.
- Roddy, J. W., Rich, D. H., & Wheeler, T. J. (2024). nail: software for high-speed, high-sensitivity protein sequence annotation. bioRxiv.
- Anderson, T., & Wheeler, T. (2023). An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models. bioRxiv, 2023--09.
- Colligan, T., Irish, K., Emlen, D. J., & Wheeler, T. J. (2023). DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals. PLOS ONE, 18(7), 1-20.
- Copeland, C. J., Roddy, J. W., Schmidt, A. K., Secor, P. R., & Wheeler, T. J. (2023). VIBES: A Workflow for Annotating and Visualizing Viral Sequences Integrated into Bacterial Genomes. bioRxiv, 2023--10.
- Glidden-Handgis, G., & Wheeler, T. J. (2023). WAS IT A MATch I SAW? Approximate palindromes lead to overstated false match rates in benchmarks using reversed sequences. bioRxiv.
- Groza, C., Chen, X., Wheeler, T. J., Bourque, G., & Goubert, C. (2023). GraffiTE: a Unified Framework to Analyze Transposable Element Insertion Polymorphisms using Genome-graphs. bioRxiv.
- Nord, A. J., & Wheeler, T. J. (2023). Mirage2's high-quality spliced protein-to-genome mappings produce accurate multiple-sequence alignments of isoforms. PLOS ONE, 18(5), e0285225.
- Schimunek, J., Seidl, P., Elez, K., Hempel, T., Le, T., No\'{e}, F., Olsson, S., Raich, L., Winter, R., Gokcan, H., Gusev, F., Gutkin, E. M., Isayev, O., Kurnikova, M. G., Narangoda, C. H., Zubatyuk, R., Bosko, I. P., Furs, K. V., Karpenko, A. D., , Kornoushenko, Y. V., et al. (2023). A community effort in SARS-CoV-2 drug discovery. Molecular Informatics.
- Storer, J. M., Walker, J. A., Baker, J. N., Hossain, S., Roos, C., Wheeler, T. J., & Batzer, M. A. (2023). Framework of the Alu Subfamily Evolution in the platyrrhine Three-Family Clade of Cebidae, Callithrichidae, and Aotidae. Genes, 14(2), 249.
- Brodie, J. F., Henao-Diaz, L. F., Pratama, B., Copeland, C., Wheeler, T., & Helmy, O. E. (2022). Fruit size in Indo-Malayan island plants is more strongly influenced by filtering than by in situ evolution. The American Naturalist.
- Geller-McGrath, D., Konwar, K., Edgcomb, V. P., Pachiadaki, M., Roddy, J., Wheeler, T., & McDermott, J. E. (2022). MetaPredict: A machine learning-based tool for predicting metabolic modules in incomplete bacterial genomes. bioRxiv.
- Hubley, R., Wheeler, T. J., & Smit, A. F. (2022). Accuracy of multiple sequence alignment methods in the reconstruction of transposable element families. NAR Genomics and Bioinformatics, 4(2), lqac040.
- Marbut, A. C., McKinney-Bock, K., & Wheeler, T. J. (2022). Reliable Measures of Spread in High Dimensional Latent Spaces. arXiv preprint arXiv:2212.08172.
- Nord, A. J., & Wheeler, T. J. (2022). Mirage2's high-quality spliced protein-to-genome mappings produce accurate multiple-sequence alignments of isoforms. bioRxiv.
- Roddy, J. W., Lesica, G. T., & Wheeler, T. J. (2022). SODA: a TypeScript/JavaScript library for visualizing biological sequence annotation. NAR Genomics and Bioinformatics, 4(4).
- Venkatraman, V., Roy, A., Gaiser, J., & Wheeler, T. (2022). Molecular fingerprints are not useful in large-scale search for similarly active compounds. bioRxiv.
Proceedings Publications
- Marbut, A., McKinney-Bock, K., & Wheeler, T. (2023). Reliable measures of spread in high dimensional latent spaces. In International Conference on Machine Learning.