Summary and statement of need The analysis of human pathogens requires a diverse collection of bioinformatics tools. These tools include standard genomic and phylogenetic software and custom software developed to handle the relatively numerous and short genomes of viruses and bacteria. Researchers increasingly depend on the outputs of these tools to infer transmission dynamics of human diseases and make actionable recommendations to public health officials ( Black et al., 2020 ; Gardy et al., 2015 ). In order to enable real-time analyses of pathogen evolution, bioinformatics tools must scale rapidly with the number of samples and be flexible enough to adapt to a variety of questions and organisms. To meet these needs, we developed Augur, a bioinformatics toolkit designed for phylogenetic analyses of human pathogens.

Cancer Consortium, and P30 AI027757 CFAR New During HIV infection, a reservoir of long-lived latently infected cells is established that persists during antiretroviral therapy (ART) and is the source of virus replication after treatment cessation. A better understanding of when viruses enter the HIV reservoir (reservoir seeding) will aid efforts to target these long-lived HIV infected cells during their establishment. We studied women infected at two different times with two genetically distinct HIV strains (called superinfection), and assessed the genetic relationship between sequences of the HIV strains that circulated throughout infection (pre-ART HIV RNA sequences) and the HIV strains that persisted in reservoir cells (HIV DNA sequences during ART). We estimated when HIV DNA sequences entered the reservoir by identifying the time the most genetically related HIV RNA sequence was detected. In most cases we observed that viruses in the reservoir included both the initial and superinfecting lineages, suggesting reservoir seeding occurs throughout HIV infection. However, the majority of HIV sequences entered the reservoir near the time of ART initiation, suggesting that novel strategies that aim to reduce reservoir size should focus on times immediately prior to ART.

Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.