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Laura Muñoz-Baena

Publications and source records attributed to Laura Muñoz-Baena.

2 recordsLinked to original sources

Selection profiles in RNA viruses reflect the characteristics of viruses more than individual proteins.

Proteins that are exposed on the surface of a virus are frequently subject to strong selection to escape from neutralizing antibodies. To investigate whether surface-exposed (SE) and non-exposed (NE) proteins encoded by RNA viruses exhibit different patterns of evolution under selection, we analyzed 244 protein-coding genes from 28 species of RNA viruses representing 15 taxonomic families. First, we show that gene-wide rates of non-synonymous (dN) and synonymous (dS) substitutions do not differentiate between SE and NE proteins. To incorporate variation in substitution rates among codon sites, we inferred the posterior distribution over a fixed grid of dN and dS rates for each alignment. This 'evolutionary fingerprint' provides a common framework for comparing the selection profiles of non-homologous genes. Next, we computed the Wasserstein distance for every pair of fingerprints, which is analogous to amount of work required to reshape one distribution to another. After compensating for differences in genetic variation among alignments, we found a small but significant difference between the fingerprints of SE and NE proteins (PERMANOVA, P&#x2009;=&#x2009;0.03). However, we observed larger and more significant effects of whether the virus is enveloped (P&#x2009;<&#x2009;10-5) and the interaction between these factors (P=6.9&#xd7;10-4). The latter effects were driven by high levels of purifying selection in capsid proteins of Picornaviruses. Furthermore, greater amounts of variation in fingerprints were explained by significant differences among virus families and modes of transmission (P&#x2009;<&#x2009;10-5). These results imply the pattern of selection on a virus protein is shaped more by characteristics of the virus than the protein itself.

RNA Viruses↗

HI-FEVER: a Nextflow pipeline for the high-throughput discovery and annotation of endogenous viral elements.

SUMMARY: Endogenous viral elements (EVEs) offer valuable insights into virus and host evolution, but their detection remains computationally and biologically challenging. We present HI-FEVER, a user-friendly Nextflow pipeline for the discovery of EVEs in eukaryotic host genomes. HI-FEVER is highly parallelizable and customizable, ensuring computational efficiency while allowing researchers to fine-tune parameters to their specific needs. Its output provides a comprehensive analysis of discovered EVEs, including detailed annotations which can provide evolutionary insights. HI-FEVER scales seamlessly to handle millions of viral protein queries across multiple host genomes on both laptops and high-performance computing nodes. AVAILABILITY AND IMPLEMENTATION: The HI-FEVER source code is available on GitHub at https://github.com/PaleovirologyLab/hi-fever. Minimal reference databases, test datasets and benchmarking results are hosted on the Open Science Framework at https://osf.io/y357r. A detailed wiki is available at https://github.com/PaleovirologyLab/hi-fever/wiki, including usage instructions, parameter descriptions, and guidance on interpreting outputs. The pipeline includes a Pixi environment compatible with Conda and Apptainer containerization, and Docker images. HI-FEVER has been tested on Linux, Windows (via WSL2), and macOS (Intel and ARM64).

Software↗