Author

Christina M. Chavez

Vanderbilt University

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 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:DNA methylation· 2 Organism:yeast· 2 Research Focus:benchmarking· 1 Research Focus:genome assembly· 1 Assay:long-read sequencing· 1 Assay:metagenomics· 1 Research Focus:phylogenetics / evolution· 1

Frequent Co-Authors

Antonis RokasiD· 3 shared Chris Todd HittingeriD· 2 shared Abigail Leavitt LaBellaiD· 1 shared Carla GonçalvesiD· 1 shared Dayna GoltziD· 1 shared Grace H MoralesiD· 1 shared Jacob Lucas SteenwykiD· 1 shared Jonathan E. SchmitziD· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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3 shared papersEvolutionary Biology

Antonis RokasORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

A trait syndrome ties cell morphology to glycolysis across the yeast subphylum

Linda C. Horianopoulos, Christina M. Chavez, Antonis Rokas, Chris Todd Hittinger

The paper expands a previous finding of a correlation between glucose uptake rates and cell surface area-to-volume ratio across 11 yeast species to 282 species, finding additional correlations with genome size and growth rate.

iScience · Jun 12, 2026Peer-reviewedEvolutionary Biology

The Macroevolution of Filamentation Morphology Across the Saccharomycotina Yeast Subphylum

Christina M. Chavez, Marie‐Claire Harrison, Thodoris Danis, Marizeth Groenewald, Chris Todd Hittinger, Antonis Rokas

The paper examines filamentation morphology evolution in Saccharomycotina yeasts using integrated genomic, metabolic, and environmental data from 1,154 strains, finding that filamentation is common and linked to phylogeny and environmental factors.

FEMS Yeast Research · Jan 1, 2026Peer-reviewedEvolutionary Biology

BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data

Jacob Lucas Steenwyk, Thomas J. Buida, Carla Gonçalves, Dayna Goltz, Grace H Morales, Matthew E. Mead, Abigail Leavitt LaBella, Christina M. Chavez, Jonathan E. Schmitz, Maria Hadjifrangiskou, Yuanning Li, Antonis Rokas

BioKIT provides a unified toolkit for processing and analyzing diverse sequence data, demonstrating its utility through quality assessments and codon usage analysis across multiple datasets.

Genetics · May 10, 2022Peer-reviewedBioinformatics