Positional clustering improves computational binding site detection and identifies novel cis-regulatory sites in mammalian GABA(A) receptor subunit genes

Understanding transcription factor (TF) mediated control of gene expression remains a major challenge at the interface of computational and experimental biology. Computational techniques predicting TF-binding site specificity are frequently unreliable. On the other hand, comprehensive experimental validation is difficult and time consuming. We introduce a simple strategy that dramatically improves robustness and accuracy of computational binding site prediction. First, we evaluate the rate of recurrence of computational TFBS predictions by commonly used sampling procedures. We find that the vast majority of results are biologically meaningless. However clustering results based on nucleotide position improves predictive power. Additionally, we find that positional clustering increases robustness to long or imperfectly selected input sequences. Positional clustering can also be used as a mechanism to integrate results from multiple sampling approaches for improvements in accuracy over each one alone. Finally, we predict and validate regulatory sequences partially responsible for transcriptional control of the mammalian type A γ-aminobutyric acid receptor (GABA(A)R) subunit genes. Positional clustering is useful for improving computational binding site predictions, with potential application to improving our understanding of mammalian gene expression. In particular, predicted regulatory mechanisms in the mammalian GABA(A)R subunit gene family may open new avenues of research towards understanding this pharmacologically important neurotransmitter receptor system

National survey of indigenous primary healthcare capacity and delivery models in Canada: the TransFORmation of IndiGEnous PrimAry HEAlthcare delivery (FORGE AHEAD) community profile survey

Background: There is a significant deficiency of national health information for Indigenous peoples in Canada. This manuscript describes the Community Profile Survey (CPS), a community-based, national-level survey designed to identify and describe existing healthcare delivery, funding models, and diabetes specific infrastructure and programs in Indigenous communities. Methods: The CPS was developed collaboratively through FORGE AHEAD and the First Nations and Inuit Health Branch of Health Canada. Regional and federal engagement and partnerships were built with Indigenous organizations to establish regionally-tailored distribution of the 8-page CPS to 440 First Nations communities. Results were collected (one survey per community) and reported in strata by region, with descriptive analyses performed on all variables. Results were shared with participating communities and regional/federal partners through tailored reports. Results: A total of 84 communities completed the survey (19% response rate). The majority of communities had a health centre/office to provide service to their patients with diabetes, with limited on-reserve hospitals for ambulatory or case-sensitive conditions. Few healthcare specialists were located on-site, with patients frequently travelling off-site (> 40 km) for diabetes-related complications. The majority of healthcare professionals on-site were Health Directors, Community Health Nurses, and Home Care Nurses. Many communities had a diabetes registry but few reported a diabetes surveillance system. Regional variation in healthcare services, diabetes programs, and funding models were noted, with most communities engaging in some type of innovative strategy to improve care for patients with diabetes. Conclusions: The CPS is the first community-based, national-level survey of its kind in Canada. Although the response rate was low, the CPS was distributed and successfully administered across a broad range of First Nations communities, and future considerations would benefit from a governance structure and leadership that strengthens community engagement, and a longitudinal research approach to increase the representativeness of the data. This type of information is important for communities and regions to inform decision making (maintain successes, and identify areas for improvement), strengthen health service delivery and infrastructure, increase accessibility to healthcare personnel, and allocate funding and/or resources to build capacity and foster a proactive chronic disease prevention and management approach for Indigenous communities across Canada. Trial registration: Current ClinicalTrial.gov protocol ID NCT02234973. Registered: September 9, 2014