An evolutionary rate quantifies the amino acid changes that occurred in a protein along a branch of a phylogeny. This rate reflects how constrained the protein is. More constrained proteins evolve at slower rates, while less constrained proteins evolve at faster rates. The rates can also vary between species, as some functional pathways become more or less constrained.
What is evolutionary rate covariation (ERC)?ERC measures the correlation between the evolutionary rates of two proteins across a phylogeny. Proteins that show high ERC may be functionally related, for example acting in the same pathway or complex, because shifts in evolutionary rate are often more similar between functionally related proteins. In this way, ERC uses a unique source of information about function that has been shaped by thousands of years of evolution and is orthogonal to lab experiments.
How can ERC be used to learn about protein function in Plasmodium ?Many Plasmodium proteins are not well-studied or annotated. This ERC dataset includes 4,360 genes across 22 Plasmodium species. Gene names from any of these species can be input into the following tools to screen for new functional connects between proteins:
Importantly, ERC is a screening tool.
Hopson HD, Omelianczyk RI, Ramirez A, Little JH, Clark N, Sigala PA, Leffler EM. Evolutionary rate covariation across malaria parasite species enables inference of protein interactions. Genome Biology and Evolution 2026. DOI: 10.1093/gbe/evag203 PMID: 42664386
The underlying R functions used in this Shiny app are adapted from: https://github.com/nclark-lab/erc
This website is also inspired by the resources available for other species: https://csbweb.csb.pitt.edu/erc_analysis/
Clark NL, Alani E, Aquadro CF. Evolutionary rate covariation reveals shared functionality and co-expression of genes. Genome Research 2012. DOI: 10.1101/gr.132647.111 PMID: 22287101
Little JH, Meyer GH, Grover A, Francette AM, Partha R, Arndt KM, Smith M, Clark N, Chikina M. ERC2.0 evolutionary rate covariation update improves inference of functional interactions across large phylogenies. Genome Research 2025. DOI: 10.1101/gr.280586.125 PMID: 40774815
Findlay GD, Sitnik JL, Wang W, Aquadro CF, Clark NL, Wolfner MF. Evolutionary Rate Covariation Identifies New Members of a Protein Network Required for Drosophila Female Post-Mating Responses. PLOS Genetics 2014. DOI: 10.1371/journal.pgen.1004108 PMID: 24453993
Little J, Chikina M, Clark NL. Evolutionary rate covariation is a reliable predictor of co-functional interactions but not necessarily physical interactions. eLife 2024. DOI: 10.7554/eLife.93333 PMID: 38415754
Kowalczyk A, Gbadamosi O, Kolor K, Sosa J, Andrzejczuk L, Gibson G, St Croix C, Chikina M, Aizenman E, Clark NL, Kiselyov K. Evolutionary rate covariation identifies SLC30A9 (ZnT9) as a mitochondrial zinc transporter. Biochemical Journal 2021. DOI: 10.1042/BCJ20210342 PMID: 34397090
Raza Q, Choi JY, Li Y, O’Dowd RM, Watkins SC, Chikina M, Hong Y, Clark NL, Kwiatkowski AV. Evolutionary rate covariation identifies the GTPase activating protein Raskol as a signaling component of the cadherin adhesion network in Drosophila. PLOS Genetics 2019. DOI: 10.1371/journal.pgen.1007720 PMID: 30763317
Brunette GJ, Jamalruddin MA, Baldock RA, Clark NL, Bernstein KA. Evolution-based screening enables genome-wide prioritization and discovery of DNA repair genes. Proc Natl Acad Sci U S A 2019 DOI: 10.1073/pnas.1906559116 PMID: 31501324
If you encounter any issues, have questions, or suggestions for the website, please contact us at u60514449@umail.utah.edu .
This ERC Shiny application was created by the Leffler Lab (University of Utah) to support exploration of evolutionary rate covariation in Plasmodium species.
For more information about our work, visit https://lefflerlab.org/ .
Given a set of input genes, this tool returns the ERC values for all pairs and returns an empirical p-value, reflecting whether the mean ERC across all pairs is higher than random sets of the same size. Results can be viewed and downloaded as a matrix or clustered heatmap.
Use case: Determine if the genes in a pathway show an ERC signature in Plasmodium and identify which gene pairs within the set are driving it.
Instructions:
Example input gene lists:
PF3D7_1133400, PF3D7_1452000, PF3D7_1116000
or
PBANKA_0915000, PBANKA_1315700, PBANKA_0932000
This tool takes a single gene as input and identifies other genes with the highest evolutionary rate covariation (ERC) with it across the genome. ERC measures the correlation between the evolutionary rates of two proteins across a phylogeny. High ERC values suggest that the two genes may share a function, act in the same pathway, or participate in the same protein complex.
This approach can help uncover potential functional partners for a gene of interest, including genes that have not been experimentally characterized.
Instructions:
Note: High ERC values suggest possible shared function. Genes listed may include orthologs from other Plasmodium species.
Use case: Suppose you are interested in a gene involved in a specific pathway. By entering that gene here, you can quickly identify potential partner genes that may work with it or belong to the same functional network.
This tool reports 1:1 orthologs for a gene across 22 Plasmodium species. Orthologs are genes in different species that evolved from a common ancestral gene and usually retain the same function. Identifying 1:1 orthologs is useful for comparing genes across species and understanding evolutionary conservation.
Orthologs in this dataset were assigned using OrthoMCL on reference genomes from PlasmoDB (v5). Only 1:1 orthologs are included; paralogs are excluded.
Instructions:
Tip: Start with a well-conserved gene to see complete ortholog results across all species.
Use case: This tool is useful if you want to explore the evolutionary conservation of a gene across multiple Plasmodium species, which can help infer function or identify candidate genes for comparative studies.