ISMB 2020
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Tutorial Registration Deadline: TODAY July 9, 2020 9 AM Eastern

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July 07, 2020 at 11:00AM EDT, Genetic Basis Of De Novo Appearance Of Carotenoid Ornamentation In Bare-Parts Of Canaries  by Malgorzata Gazda, hosted by EvolCompGen COSI and SMBE.

Unlike wild and domestic canaries (Serinus canaria), or any of the three dozen species of finches in genus Serinus, the domestic urucum breed of canaries exhibits bright red bills and legs. This novel trait offers a unique opportunity to understand the mechanisms of bare-part coloration in birds. To identify the mutation producing the colorful phenotype, we resequenced the genome of urucum canaries and performed a range of analyses to search for genotype-to-phenotype associations across the genome. We identified a nonsynonymous mutation in the gene BCO2 (beta-carotene oxygenase 2, also known as BCDO2), an enzyme involved in the cleavage and breakdown of full-length carotenoids into short apocarotenoids. Protein structural models and in vitro functional assays indicate that the urucum mutation abrogates the carotenoid-cleavage activity of BCO2. Consistent with the predicted loss of carotenoid-cleavage activity, urucum canaries tended to have increased levels of full-length carotenoid pigments in bill tissue and reduced levels of carotenoid-cleavage products (apocarotenoids) in retinal tissue compared with other breeds of canaries. We hypothesize that carotenoid-based bare-part coloration might be readily gained, modified, or lost through simple switches in the enzymatic activity or regulation of BCO2 and this gene may be an important mediator in the evolution of bare-part coloration among bird species.

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June 30, 2020 at 11:00AM EDT, Global surveillance of COVID-19 by mining news media using a multi-source dynamic embedded topic model by David Buckeridge, an ISCB COVID-19 Webinar

As the COVID-19 pandemic continues to unfold, understanding the global impact of non-pharmacological interventions (NPI) is important for formulating effective intervention strategies, particularly as many countries prepare for future waves. We used a machine learning approach to distill latent topics related to NPI from large-scale international news media. We hypothesize that these topics are informative about the timing and nature of implemented NPI, dependent on the source of the information (e.g., local news versus official government announcements) and the target countries. Given a set of latent topics associated with NPI (e.g., self-quarantine, social distancing, online education, etc), we assume that countries and media sources have different prior distributions over these topics, which are sampled to generate the news articles. To model the source-specific topic priors, we developed a semi-supervised, multi-source, dynamic, embedded topic model. Our model is able to simultaneously infer latent topics and learn a linear classifier to predict NPI labels using the topic mixtures as input for each news article. To learn these models, we developed an efficient end-to-end amortized variational inference algorithm. We applied our models to news data collected and labelled by the World Health Organization (WHO) and the Global Public Health Intelligence Network (GPHIN). Through comprehensive experiments, we observed superior topic quality and intervention prediction accuracy, compared to the baseline embedded topic models, which ignore information on media source and intervention labels. The inferred latent topics reveal distinct policies and media framing in different countries and media sources, and also characterize reaction COVID-19 and NPI in a semantically meaningful manner.