FRESCo: finding regions of excess synonymous constraint in diverse viruses
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Author(s)
Lin, Michael F.
Jungreis, Irwin
Kellis, Manolis
Sabeti, Pardis C
Sealfon, Rachel S.
Wolf, Maxim Y.
Date Issued
February 2015
Journal
Genome Biology
Publisher
BioMed Central
Citation
Sealfon, Rachel S, Michael F Lin, Irwin Jungreis, Maxim Y Wolf, Manolis Kellis, and Pardis C Sabeti. “FRESCo: Finding Regions of Excess Synonymous Constraint in Diverse Viruses.” Genome Biology 16, no. 1 (February 17, 2015).
Version
Final published version
Abstract
Background
The increasing availability of sequence data for many viruses provides power to detect regions under unusual evolutionary constraint at a high resolution. One approach leverages the synonymous substitution rate as a signature to pinpoint genic regions encoding overlapping or embedded functional elements. Protein-coding regions in viral genomes often contain overlapping RNA structural elements, reading frames, regulatory elements, microRNAs, and packaging signals. Synonymous substitutions in these regions would be selectively disfavored and thus these regions are characterized by excess synonymous constraint. Codon choice can also modulate transcriptional efficiency, translational accuracy, and protein folding.
Results
We developed a phylogenetic codon model-based framework, FRESCo, designed to find regions of excess synonymous constraint in short, deep alignments, such as individual viral genes across many sequenced isolates. We demonstrated the high specificity of our approach on simulated data and applied our framework to the protein-coding regions of approximately 30 distinct species of viruses with diverse genome architectures.
Conclusions
FRESCo recovers known multifunctional regions in well-characterized viruses such as hepatitis B virus, poliovirus, and West Nile virus, often at a single-codon resolution, and predicts many novel functional elements overlapping viral genes, including in Lassa and Ebola viruses. In a number of viruses, the synonymously constrained regions that we identified also display conserved, stable predicted RNA structures, including putative novel elements in multiple viral species.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13059-015-0603-7
