Author

M.-C. Harrison

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· 3 Ecology· 1

Signature Assays, Organisms & Methods

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Organism:yeast· 4 Assay:DNA methylation· 3 Approach:machine learning (classical)· 3 Assay:whole-genome sequencing· 3 Research Focus:chromatin & epigenetic regulation· 2 Research Focus:gene regulatory network inference· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

A. L. LaBella· 4 shared A. Rokas· 4 shared C. T. Hittinger· 4 shared D. A. Opulente· 4 shared J. F. Wolters· 4 shared M. Groenewald· 4 shared X. Zhou· 4 shared X.-X. Shen· 4 shared

Activity by Year

2023-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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4 shared papersEvolutionary Biology

A. L. LaBella

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Diverse signatures of convergent evolution in cacti-associated yeasts

C. Goncalves, M.-C. Harrison, J. L. Steenwyk, D. A. Opulente, A. L. LaBella, J. F. Wolters, X. Zhou, X.-X. Shen, M. Groenewald, C. T. Hittinger, A. Rokas

This study identifies convergent evolution in cacti-associated yeasts using genomic and phenotypic data, finding thermotolerance and horizontal gene transfer as key adaptive traits.

bioRxiv · Sep 14, 2023 · PreprintEvolutionary Biology

Saccharomycotina yeasts defy longstanding macroecological patterns

K. T. David, M.-C. Harrison, D. A. Opulente, A. L. LaBella, J. F. Wolters, X. Zhou, X.-X. Shen, M. Groenewald, M. Pennell, C. T. Hittinger, A. Rokas

This study uses machine learning to model global yeast species distributions and finds they defy traditional macroecological patterns.

bioRxiv · Aug 29, 2023 · PreprintEcology

Machine learning illuminates how diet influences the evolution of yeast galactose metabolism

M.-C. Harrison, E. J. Ubbelohde, A. L. LaBella, D. A. Opulente, J. F. Wolters, X. Zhou, X.-X. Shen, M. Groenewald, C. T. Hittinger, A. Rokas

This study uses machine learning to analyze yeast genomes and growth data to uncover how diet influences the evolution of galactose metabolism.

bioRxiv · Jul 20, 2023 · PreprintEvolutionary Biology

Genomic and ecological factors shaping specialism and generalism across an entire subphylum

D. A. Opulente, A. L. LaBella, M.-C. Harrison, J. F. Wolters, C. Liu, Y. Li, J. Kominek, J. L. Steenwyk, H. R. Stoneman, J. VanDenAvond, C. R. Miller, Q. K. Langdon, M. Silva, C. Goncalves, E. J. Ubbelohde, , C. T. Hittinger

The paper analyzes genomic, metabolic, and ecological data from nearly all Saccharomycotina yeast species to show that intrinsic genetic factors, not trade-offs or extrinsic factors, drive variation in niche breadth.

bioRxiv · Jun 19, 2023 · PreprintEvolutionary Biology