Finding information about uncharacterized Drosophila melanogaster genes

Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.

A Second Look at FAIR in Proteomic Investigations

Proteomics is, by definition, comprehensive and large-scale, seeking to unravel ome-level protein features with phenotypic information on an entire system, an organ, cells, or organisms. This scope consistently involves and extends beyond single experiments. Multitudinous resources now exist to assist in making the results of proteomics experiments more findable, accessible, interoperable, and reusable (FAIR), yet many tools are awaiting to be adopted by our community. Here we highlight strategies for expanding the impact of proteomics data beyond single studies. We show how linking specific terminologies, identifiers, and text (words) can unify individual data points across a wide spectrum of studies and, more importantly, how this approach may potentially reveal novel relationships. In this effort, we explain how data sets and methods can be rendered more linkable and how this maximizes their value. We also include a discussion on how data linking strategies benefit stakeholders across the proteomics community and beyond.

Quantifying Use of a Health Virtual Community of Practice for General Practitioners' Continuing Professional Development: A Novel Methodology and Pilot Evaluation

Background

Health care practitioners (HPs), in particular general practitioners (GPs), are increasingly adopting Web-based social media platforms for continuing professional development (CPD). As GPs are restricted by time, distance, and demanding workloads, a health virtual community of practice (HVCoP) is an ideal solution to replace face-to-face CPD with Web-based CPD. However, barriers such as time and work schedules may limit participation in an HVCoP. Furthermore, it is difficult to gauge whether GPs engage actively or passively in HVCoP knowledge-acquisition for Web-based CPD, as GPs’ competencies are usually measured with pre- and posttests.

Objective

This study investigated a method for measuring the engagement features needed for an HVCoP (the Community Fracture Capture [CFC] Learning Hub) for learning and knowledge sharing among GPs for their CPD activity.

Methods

A prototype CFC Learning Hub was developed using an Igloo Web-based social media software platform and involved a convenience sample of GPs interested in bone health topics. This Hub, a secure Web-based community site, included 2 key components: an online discussion forum and a knowledge repository (the Knowledge Hub). The discussion forum contained anonymized case studies (contributed by GP participants) and topical discussions (topics that were not case studies). Using 2 complementary tools (Google Analytics and Igloo Statistical Tool), we characterized individual participating GPs’ engagement with the Hub. We measured the GP participants’ behavior by quantifying the number of online sessions of the participants, activities undertaken within these online sessions, written posts made per learning topic, and their time spent per topic. We calculated time spent in both active and passive engagement for each topic.

Results

Seven GPs participated in the CFC Learning Hub HVCoP from September to November 2017. The complementary tools successfully captured the GP participants’ engagement in the Hub. GPs were more active in topics in the discussion forum that had direct clinical application as opposed to didactic, evidence-based discussion topics (ie, topical discussions). From our knowledge hub, About Osteoporosis and Prevention were the most engaging topics, whereas shared decision making was the least active topic.

Conclusions

We showcased a novel complementary analysis method that allowed us to quantify the CFC Learning Hub’s usage data into (1) sessions, (2) activities, (3) active or passive time spent, and (4) posts made to evaluate the potential engagement features needed for an HVCoP focused on GP participants’ CPD process. Our design and evaluation methods for ongoing use and engagement in this Hub may be useful to evaluate future learning and knowledge-sharing projects for GPs and may allow for extension to other HPs’ environments. However, owing to the limited number of GP participants in this study, we suggest that further research with a larger cohort should be performed to validate and extend these findings.

Leveraging Online Resources to Prioritize Candidate Genes for Functional Analyses: Using the Fetal Testis as a Test Case

With each new microarray or RNA-seq experiment, massive quantities of transcriptomic information are generated with the purpose to produce a list of candidate genes for functional analyses. Yet an effective strategy remains elusive to prioritize the genes on these candidate lists. In this review, we outline a prioritizing strategy by taking a step back from the bench and leveraging the rich range of public databases. This in silico approach provides an economical, less biased, and more effective solution. We discuss the publicly available online resources that can be used to answer a range of questions about a gene. Is the gene of interest expressed in the system of interest (using expression databases)? Where else is this gene expressed (using added-value transcriptomic resources)? What pathways and processes is the gene involved in (using enriched gene pathway analysis and mouse knockout databases)? Is this gene correlated with human diseases (using human disease variant databases)? Using mouse fetal testis as an example, our strategies identified 298 genes annotated as expressed in the fetal testis. We cross-referenced these genes to existing microarray data and narrowed the list down to cell-type-specific candidates (35 for Sertoli cells, 11 for Leydig cells, and 25 for germ cells). Our strategies can be customized so that they allow researchers to effectively and confidently prioritize genes for functional analysis.

Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma

The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.