Bayes-optimal estimation of overlap between populations of fixed size

Mathematical Characterization of Private and Public Immune Receptor Sequences

Diverse T and B cell repertoires play an important role in mounting effective immune responses against a wide range of pathogens and malignant cells. The number of unique T and B cell clones is characterized by T and B cell receptors (TCRs and BCRs), respectively. Although receptor sequences are generated probabilistically by recombination processes, clinical studies found a high degree of sharing of TCRs and BCRs among different individuals. In this work, we use a general probabilistic model for T/B cell receptor clone abundances to define "publicness" or "privateness" and information-theoretic measures for comparing the frequency of sampled sequences observed across different individuals. We derive mathematical formulae to quantify the mean and the variances of clone richness and overlap. Our results can be used to evaluate the effect of different sampling protocols on abundances of clones within an individual as well as the commonality of clones across individuals. Using synthetic and empirical TCR amino acid sequence data, we perform simulations to study expected clonal commonalities across multiple individuals. Based on our formulae, we compare these simulated results with the analytically predicted mean and variances of the repertoire overlap. Complementing the results on simulated repertoires, we derive explicit expressions for the richness and its uncertainty for specific, single-parameter truncated power-law probability distributions. Finally, the information loss associated with grouping together certain receptor sequences, as is done in spectratyping, is also evaluated. Our approach can be, in principle, applied under more general and mechanistically realistic clone generation models.

Molecular epidemiology of continued Plasmodium falciparum disease transmission after an outbreak in Ecuador

To better understand the factors underlying the continued incidence of clinical episodes of falciparum malaria in E-2025 countries targeting elimination, we characterized the molecular epidemiology of Plasmodium falciparum disease transmission after a clonal outbreak in Ecuador. Here we study disease transmission by documenting the diversity and population structure of the major variant surface antigen of the blood stages of P. falciparum encoded by the var multigene family. We used a high-resolution genotyping method, “varcoding”, involving targeted amplicon sequencing to fingerprint the DBLα encoding region of var genes to describe both antigenic var diversity and var repertoire similarity or relatedness in parasite isolates from clinical cases. We identified nine genetic varcodes in 58 P. falciparum isolates causing clinical disease in 2013-2015. Network analyses revealed that four of the varcodes were highly related to the outbreak varcode, with identification of possible diversification of the outbreak parasites by recombination as seen in three of those varcodes. The majority of clinical cases in Ecuador were associated with parasites with highly related or recombinant varcodes to the outbreak clone and due to local transmission rather than recent importation of parasites from other endemic countries. Sharing of types in Ecuadorian varcodes to those sampled in South American varcodes reflects historical parasite importation of some varcodes, especially from Colombia and Peru. Our findings highlight the translational application of varcoding for outbreak surveillance in epidemic/unstable malaria transmission, such as in E-2025 countries, and point to the need for surveillance of local reservoirs of infection in Ecuador to achieve the malaria elimination goal by 2025.

Bayesian estimation of community size and overlap from random subsamples

Counting the number of species, items, or genes that are shared between two groups, sets, or communities is a simple calculation when sampling is complete. However, when only partial samples are available, quantifying the overlap between two communities becomes an estimation problem. Furthermore, to calculate normalized measures of β-diversity, such as the Jaccard and Sorenson-Dice indices, one must also estimate the total sizes of the communities being compared. Previous efforts to address these problems have assumed knowledge of total community sizes and then used Bayesian methods to produce unbiased estimates with quantified uncertainty. Here, we address communities of unknown size and show that this produces systematically better estimates—both in terms of central estimates and quantification of uncertainty in those estimates. We further show how to use species, item, or gene count data to refine estimates of community size in a Bayesian joint model of community size and overlap.

A survey of researchers' code sharing and code reuse practices, and assessment of interactive notebook prototypes

This research aimed to understand the needs and habits of researchers in relation to code sharing and reuse; gather feedback on prototype code notebooks created by NeuroLibre; and help determine strategies that publishers could use to increase code sharing. We surveyed 188 researchers in computational biology. Respondents were asked about how often and why they look at code, which methods of accessing code they find useful and why, what aspects of code sharing are important to them, and how satisfied they are with their ability to complete these tasks. Respondents were asked to look at a prototype code notebook and give feedback on its features. Respondents were also asked how much time they spent preparing code and if they would be willing to increase this to use a code sharing tool, such as a notebook. As a reader of research articles the most common reason (70%) for looking at code was to gain a better understanding of the article. The most commonly encountered method for code sharing-linking articles to a code repository-was also the most useful method of accessing code from the reader's perspective. As authors, the respondents were largely satisfied with their ability to carry out tasks related to code sharing. The most important of these tasks were ensuring that the code was running in the correct environment, and sharing code with good documentation. The average researcher, according to our results, is unwilling to incur additional costs (in time, effort or expenditure) that are currently needed to use code sharing tools alongside a publication. We infer this means we need different models for funding and producing interactive or executable research outputs if they are to reach a large number of researchers. For the purpose of increasing the amount of code shared by authors, PLOS Computational Biology is, as a result, focusing on policy rather than tools.