Viral diversity and clonal evolution from unphased genomic data

The Threat from Viruses

Infectious disease represent the most significant threat to human health. Significant geologic cataclysmic events have caused the extinction of countless species, but these “Wrath of God” events predate the emergence of Homo sapiens. Pandemic infections have accompanied the rise of human civilization frequently re-occurring leaving a lasting imprint on human history punctuated by profound loss of life. Emerging infections become endemic and are here to stay marking their presence with an annual death toll. Each decade brings a new onslaught of emerging infectious agents. We are surprised again and again but are never prepared. The long-term consequences often remain unrecognized and are always inconvenient including cancer, cardiovascular disease and immune associated diseases that threaten our health. Reliance on clusters of clinical symptoms in the face of diverse and non-descriptive viral infection symptoms is a foolhardy form of crisis management. Viral success is based on rapid replication resulting in large numbers. Single-stranded RNA viruses with their high replication error rate represent a paradigm for resilience.

On statistical modeling of sequencing noise in high depth data to assess tumor evolution

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pre-treatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

Genomic Analysis of Viral Outbreaks

Genomic analysis is a powerful tool for understanding viral disease outbreaks. Sequencing of viral samples is now easier and cheaper than ever before and can supplement epidemiological methods by providing nucleotide-level resolution of outbreak-causing pathogens. In this review, we describe methods used to answer crucial questions about outbreaks, such as how they began and how a disease is transmitted. More specifically, we explain current techniques for viral sequencing, phylogenetic analysis, transmission reconstruction, and evolutionary investigation of viral pathogens. By detailing the ways in which genomic data can help us understand viral disease outbreaks, we aim to provide a resource that will facilitate the response to future outbreaks.

High-resolution Genomic Surveillance of 2014 Ebolavirus Using Shared Subclonal Variants

Background: Viral outbreaks, such as the 2014 ebolavirus, can spread rapidly and have complex evolutionary dynamics, including coinfection and bulk transmission of multiple viral populations. Genomic surveillance can be hindered when the spread of the outbreak exceeds the evolutionary rate, in which case consensus approaches will have limited resolution. Deep sequencing of infected patients can identify genomic variants present in intrahost populations at subclonal frequencies (i.e. <50%). Shared subclonal variants (SSVs) can provide additional phylogenetic resolution and inform about disease transmission patterns. Methods: We use metrics from population genetics to analyze data from the 2014 ebolavirus outbreak in Sierra Leone and identify phylogenetic signal arising from SSVs. We use methods derived from information theory to measure a lower bound on transmission bottleneck size. Results and Conclusions: We identify several SSV that shed light on phylogenetic relationships not captured by consensus-based analyses. We find that transmission bottleneck size is larger than one founder population, yet significantly smaller than the intrahost effective population. Our results demonstrate the important role of shared subclonal variants in genomic surveillance.