Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be highly correlated. Notably, convergent and parallel mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments.
We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The Emergence component selects mutations that emerge more often than expected, while the Correlation component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available, as is often the case for viruses and microorganisms.
We evaluate the properties of ConDor under different conditions using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.
To learn more about the usage of ConDor, please read our help page
We need your help to improve this web service. Please send your comments and/or suggestions to:frederic[dot]lemoine[at]pasteur[dot]fr and olivier[dot]gascuel[at]mnhn[dot]fr
Morel, M., Zhukova, A., Lemoine, F. and Gascuel, O. "Accurate detection of Convergent Mutations in Large Protein Alignments with ConDor". doi: https://doi.org/10.1101/2021.06.30.450558
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You need several files to run ConDor:
- An amino acid alignment in fasta format including outgroup sequences
- A phylogenetic tree in Newick format, including the outgroup sequences;
- A text file with outgroup sequence names
- A text file containing sequence names with the convergent phenotype
- To infer trees from aligned sequences, do not hesitate to use NGPhylogeny.fr
- An example analysis is available here. For the example, we used the dataset from (Besnard et al., 2009) used in the PCOC paper (Rey et al. 2018). It consists of 79 sequences of the PEPC protein in sedges (plant species at C3/C4 transition) and the corresponding tree. You can find a brief analysis of these results in our help page.