Dataset Information

Viral diversity and clonal evolution from unphased genomic data.

ABSTRACT:

Background

Clonal expansion is a process in which a single organism reproduces asexually, giving rise to a diversifying population. It is pervasive in nature, from within-host pathogen evolution to emergent infectious disease outbreaks. Standard phylogenetic tools rely on full-length genomes of individual pathogens or population consensus sequences (phased genotypes).

Methods

We introduce two measures of diversity to study the evolution of clonal populations using unphased genomic data, which eliminate the need to construct full-length genomes. Our method follows a maximum likelihood approach to estimate evolutionary rates and times to the most recent common ancestor, based on a relaxed molecular clock model; independent of a growth model. Deviations from neutral evolution indicate the presence of selection and bottleneck events.

Results

We evaluated our methods in silico and then compared it against existing approaches with the well-characterized 2009 H1N1 influenza pandemic. We then applied our method to high-throughput genomic data from marburgvirus-infected non-human primates and inferred the time of infection and the intra-host evolutionary rate, and identified purifying selection in viral populations.

Conclusions

Our method has the power to make use of minor variants present in less than 1% of the population and capture genomic diversification within days of infection, making it an ideal tool for the study of acute RNA viral infection dynamics.

SUBMITTER: Khiabanian H

PROVIDER: S-EPMC4240099 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Bacterial cells during many replication cycles accumulate spontaneous mutations, which result in the birth of novel clones. As a result of this clonal expansion, an evolving bacterial population has different clonal composition over time, as revealed in the long-term evolution experiments (LTEEs). Accurately inferring the haplotypes of novel clones as well as the clonal frequencies and the clonal evolutionary history in a bacterial population is useful for the characterization of the evolutionary pressure on multiple correlated mutations instead of that on individual mutations. RESULTS:In this paper, we study the computational problem of reconstructing the haplotypes of bacterial clones from the variant allele frequencies observed from an evolving bacterial population at multiple time points. We formalize the problem using a maximum likelihood function, which is defined under the assumption that mutations occur spontaneously, and thus the likelihood of a mutation occurring in a specific clone is proportional to the frequency of the clone in the population when the mutation occurs. We develop a series of heuristic algorithms to address the maximum likelihood inference, and show through simulation experiments that the algorithms are fast and achieve near optimal accuracy that is practically plausible under the maximum likelihood framework. We also validate our method using experimental data obtained from a recent study on long-term evolution of Escherichia coli. CONCLUSION:We developed efficient algorithms to reconstruct the clonal evolution history from time course genomic sequencing data. Our algorithm can also incorporate clonal sequencing data to improve the reconstruction results when they are available. Based on the evaluation on both simulated and experimental sequencing data, our algorithms can achieve satisfactory results on the genome sequencing data from long-term evolution experiments. AVAILABILITY:The program (ClonalTREE) is available as open-source software on GitHub at https://github.com/COL-IU/ClonalTREE.