Global Patterns of Recombination across Human Viruses

Abstract

Viral recombination is a major evolutionary mechanism driving adaptation processes, such as the ability of host-switching. Understanding global patterns of recombination could help to identify underlying mechanisms and to evaluate the potential risks of rapid adaptation. Conventional approaches (e.g., those based on linkage disequilibrium) are computationally demanding or even intractable when sequence alignments include hundreds of sequences, common in viral data sets. We present a comprehensive analysis of recombination across 30 genomic alignments from viruses infecting humans. In order to scale the analysis and avoid the computational limitations of conventional approaches, we apply newly developed topological data analysis methods able to infer recombination rates for large data sets. We show that viruses, such as ZEBOV and MARV, consistently displayed low levels of recombination, whereas high levels of recombination were observed in Sarbecoviruses, HBV, HEV, Rhinovirus A, and HIV. We observe that recombination is more common in positive single-stranded RNA viruses than in negatively single-stranded RNA ones. Interestingly, the comparison across multiple viruses suggests an inverse correlation between genome length and recombination rate. Positional analyses of recombination breakpoints along viral genomes, combined with our approach, detected at least 39 nonuniform patterns of recombination (i.e., cold or hotspots) in 18 viral groups. Among these, noteworthy hotspots are found in MERS-CoV and Sarbecoviruses (at spike, Nucleocapsid and ORF8). In summary, we have developed a fast pipeline to measure recombination that, combined with other approaches, has allowed us to find both common and lineage-specific patterns of recombination among viruses with potential relevance in viral adaptation.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 15, 2021
Source ID
10.1093/molbev/msab046

Entities

People

  • Ioan Filip
  • Juan Ángel Patiño-galindo
  • Raúl Rabadán

Organizations

  • Columbia University
  • Defense Advanced Research Projects Agency
  • National Institutes of Health
  • United States Department of Defense

Tags

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Neural Network Machine Learning.
  • Virology (or Medical Virology).

Technology Areas

  • AI & ML