ISCB News and Announcements

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.

Polls are Open!

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.

Join us for the

ISMB 2020 Virtual Conference

Registration is now open at:

https://www.iscb.org/cms_addon/registration_conference/ismb2020virtual

June 26, 2020 at 11:00AM EDT, At Home with Covid-19
by Brian Shoichet, an ISCB COVID-19 Webinar

The urgency of the coronavirus pandemic has motivated investigators world wide to seek approved drugs or investigation new drugs as a way to rapidly advance therapeutics into clinical trials to treat the disease. I will describe a large collaboration, hosted by the UCSF Quantitative Biology Institute, to do that in a mechanistically focused way. Using AP-MS, a host-pathogen network of viral and human proteins was created, and drugs were sought targeting the human partner. From among 322 high confidence human proteins associated with 26 viral proteins emerged 63 that were druggable. Against those, 69 drugs were tested for efficacy, and from these 10 drugs in two broad classes emerged: those targeting protein biogenesis, and those acting against the Sigma1 and Sigma2 receptors. The activities of these drugs, and the chemoinformatics infrastructure that supported their selection, will be discussed. The mechanism-based repurposing strategy will be compared to a complementary effort that targets viral proteins and seeks novel chemical matter, using structure-based ultra-large library docking.

*Access to the webinar series is complimentary for ISCB members. Nonmembers can join for a fee.

• Invited speakers
• Roundtable networking breakouts
• Women in STEM panel discussion
• Flash talks selected from an open call for abstracts
• Scientific talks selected from articles submissions to Interface Focus Journal

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• June 12, 2020 at 11:00AM EDT, Fast Coalescent-Based Computation of Local Branch Support from Quartet Frequencies by Erfan Sayyari, hosted by EvolCompGen COSI
• June 23, 2020 at 11:00AM EDT, Engineering Alternative Polyadenylation with Deep Generative Neural Networks by Johannes Linder, hosted by iRNA COSI and the RNA Society

ISCB Congratulates and Introduces the 2020 Class of Fellows!