Browsing by Author "Biggs PJ"
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- ItemAbundant dsRNA picobirnaviruses show little geographic or host association in terrestrial systems.(Elsevier, 2023-08) Knox MA; Wierenga J; Biggs PJ; Gedye K; Almeida V; Hall R; Kalema-Zikusoka G; Rubanga S; Ngabirano A; Valdivia-Granda W; Hayman DTSPicobirnaviruses are double-stranded RNA viruses known from a wide range of host species and locations but with unknown pathogenicity and host relationships. Here, we examined the diversity of picobirnaviruses from cattle and gorillas within and around Bwindi Impenetrable Forest National Park (BIFNP), Uganda, where wild and domesticated animals and humans live in relatively close contact. We use metagenomic sequencing with bioinformatic analyses to examine genetic diversity. We compared our findings to global Picobirnavirus diversity using clustering-based analyses. Picobirnavirus diversity at Bwindi was high, with 14 near-complete RdRp and 15 capsid protein sequences, and 497 new partial viral sequences recovered from 44 gorilla samples and 664 from 16 cattle samples. Sequences were distributed throughout a phylogenetic tree of globally derived picobirnaviruses. The relationship with Picobirnavirus diversity and host taxonomy follows a similar pattern to the global dataset, generally lacking pattern with either host or geography.
- ItemLost in the Forest(Cold Spring Harbor Laboratory, 2022) Smith HL; Biggs PJ; French NP; Smith ANH; Marshall JCTo date, there remains no satisfactory solution for absent levels in random forest models. Absent levels are levels of a predictor variable encountered during prediction for which no explicit rule exists. Imposing an order on nominal predictors allows absent levels to be integrated and used for prediction. The ordering of predictors has traditionally been via class probabilities with absent levels designated the lowest order. Using a combination of simulated data and pathogen source-attribution models using whole-genome sequencing data, we examine how the method of ordering predictors with absent levels can (i) systematically bias a model, and (ii) affect the out-of-bag error rate. We show that the traditional approach is systematically biased and underestimates out-of-bag error rates, and that this bias is resolved by ordering absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordering predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are designated an order according to their similarity to each of the other levels in the training data. We show that the PCO method performs at least as well as the traditional approach of ordering and is not biased.