Author

T. T. Barter

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Evolutionary Biology· 2 Physiology· 1

Signature Assays, Organisms & Methods

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Organism:Drosophila· 3 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:chromatin & epigenetic regulation· 1 Assay:DNA methylation· 1

Frequent Co-Authors

L. D. Mueller· 2 shared M. A. Phillips· 2 shared M. R. Rose· 2 shared A. Hinton· 1 shared A. Khong· 1 shared A. Marshall· 1 shared A. Rahman· 1 shared D. Morton· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersPhysiology

L. D. Mueller

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Author profile →

3 papers

Recent·Most cited

Building bridges from genome to physiology using machine learning and Drosophila experimental evolution

J. N. Kezos, T. T. Barter, L. Cabral, M. A. Phillips, G. Azatian, J. E. Buenrostro, P. S. Bhangoo, A. Khong, G. T. Reyes, A. Rahman, L. A. Humphrey, T. J. Bradley, L. D. Mueller, M. R. Rose

The paper uses machine learning and Drosophila experimental evolution to identify genomic regions causally linked to physiological traits.

bioRxiv · Jul 18, 2022Physiology

Combining metabolomics and experimental evolution reveals key mechanisms underlying longevity differences in laboratory evolved Drosophila melanogaster populations

M. Phillips, K. R. Arnold, Z. Vue, H. Beasley, E. Garza Lopez, A. Marshall, D. Morton, M. R. McReynolds, T. T. Barter, A. Hinton

The paper combines metabolomics and experimental evolution to identify genetic and physiological mechanisms underlying longevity differences in Drosophila melanogaster populations.

bioRxiv · Oct 16, 2021Evolutionary Biology

Genome-Wide Architecture of Adaptation in Experimentally Evolved Drosophila

T. T. Barter, Z. S. Greenspan, M. A. Phillips, J. M. Ranz, M. R. Rose, L. D. Mueller

This study uses experimentally evolved Drosophila populations and statistical learning to show that both genome and transcriptome data can predict phenotypes, with widespread pleiotropy observed.

bioRxiv · Oct 30, 2020 · PreprintEvolutionary Biology