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Background As high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource). Results NASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology. Conclusions NASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

MotivationHigh-throughput analysis in the current era of systems biology encompasses a range of analysis workflows for different applications, such as transcriptomics, epigenetics, variant discovery, de novo genome assembly, etc. Many research institutes house a genomics core facility that is responsible for generating high-throughput sequencing data and often includes a core bioinformatics team that carries out data analysis. Core teams must both keep track of data and maintain numerous software packages and databases – which must be scalable according to ever-changing research needs – and they must allocate computational resources to them. Typical data analysis pipelines involve multiple software packages and require the ability to install, execute, and maintain software stacks on a variety of hardware/operating system configurations, including high-performance computing (HPC) facilities, stand-alone servers, and Cloud services. At the same time, individual researchers need to analyze their data and share their analysis steps with collaborators. Having to rely on ad hoc scripts and rigid internally developed pipelines is inefficient, difficult to track and maintain, and ultimately, limits the ability to adapt and evolve as research methods progress.ResultsHere, we present BioSAILs (Bioinformatics Standardized Analysis Information Layers), a scientific workflow management system (WMS) developed by the Core Bioinformatics team at NYU Abu Dhabi. BioSAILs comprises two central components, BioX command and HPCRunner command, supported by BioStacks software stacks. BioX structures executable workflows that may be either run directly on a desktop or lab server, or submitted by HPCRunner to HPC infrastructure or the Cloud. BioSAILs is supported by a range of pre-configured, customizable BioStacks software stacks for various applications (e.g. RNA-seq, de novo genome/transcriptome assembly, variant discovery, etc.) Software stacks are built using Conda and BioConda (6), have been pre-packaged into modules using EasyBuild, and can be deployed with or without BioSAILs. Within the BioSAILs WMS, users can also invoke commands that take advantage of containerized software in Docker (10) and Singularity (8). Finally, the BioSAILs web resource provides documentation, blog posts related to various BioSAILs analysis tasks, forums, knowledge base & FAQs, as well as an interactive web-based workflow editor/creator. BioSAILs is production-level software that has been in use as the main WMS at NYU Abu Dhabi for the past 2 years and is continuously developed and maintained by the core bioinformatics team.AvailabilityBioSAILs is open source software, available under the GNU license. Online documentation, support and guides can be found at biosails.abudhabi.nyu.edu. The public Github project is available at Github – BioSAILs. HPC-Runner and BioX-Workflow can be installed through Conda using the BioConda channel.

We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multiplatform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.

Context: In most ethnicities at least a quarter of all cases with diabetes is assumed to be undiagnosed. Screening for diabetes using saliva has been suggested as an effective approach to identify affected individuals. Objective: The objective of the study was to identify a noninvasive metabolic marker of type 2 diabetes in saliva. Design and Setting: In a case-control study of type 2 diabetes, we used a clinical metabolomics discovery study to screen for diabetes-relevant metabolic readouts in saliva, using blood and urine as a reference. With a combination of three metabolomics platforms based on nontargeted mass spectrometry, we examined 2178 metabolites in saliva, blood plasma, and urine samples from 188 subjects with type 2 diabetes and 181 controls of Arab and Asian ethnicities. Results: We found a strong association of type 2 diabetes with 1,5-anhydroglucitol (1,5-AG) in saliva (P = 3.6 x 10(-13)). Levels of 1,5-AG in saliva highly correlated with 1,5-AG levels in blood and inversely correlated with blood glucose and glycosylated hemoglobin levels. These findings were robust across three different non-Caucasian ethnicities (Arabs, South Asians, and Filipinos), irrespective of body mass index, age, and gender. Conclusions: Clinical studies have already established 1,5-AG in blood as a reliable marker of short-term glycemic control. Our study suggests that 1,5-AG in saliva can be used in national screening programs for undiagnosed diabetes, which are of particular interest for Middle Eastern countries with young populations and exceptionally high diabetes rates.

Background Advanced glycation end products (AGEs) have been shown to be a predictor of cardiovascular risk in Caucasian subjects. In this study we examine whether the existing reference values are useable for non-Caucasian ethnicities. Furthermore, we assessed whether gender and smoking affect AGEs. Methods AGEs were determined by a non-invasive method of skin auto-fluorescence (AF). AF was measured in 200 Arabs, 99 South Asians, 35 Filipinos and 14 subjects of other/mixed ethnicity in the Qatar Metabolomics Study on Diabetes (QMDiab). Using multivariate linear regression analysis and adjusting for age and type 2 diabetes, we assessed whether ethnicity, gender and smoking were associated with AF. Results The mean AF was 2.27 arbitrary units (AU) (SD: 0.63). Arabs and Filipinos had a significant higher AF than the South Asian population (0.25 arbitrary units (AU) (95% CI: 0.11‒0.39), p = 0.001 and 0.34 (95% CI: 0.13‒0.55), p = 0.001 respectively). Also, AF was significantly higher in females (0.41 AU (95% CI: 0.29‒0.53), p < 0.001). AF associated with smoking (0.21 AU (95% CI: 0.01‒0.41), p = 0.04) and increased with the number of pack-years smoked (p = 0.02). Conclusions This study suggests that the existing reference values should take ethnicity, gender and smoking into account. Larger studies in specific ethnicities are necessary to create ethnic- and gender-specific reference values.

Background and Objectives: The prevalence of type 2 diabetes (T2D) in Qatar is the highest in the world. It is estimated that about one quarter of the T2D patients in Qatar are still undiagnosed. We set out to examine determinants of pre-diabetes (PD) or undiagnosed T2D (UT2D). Furthermore, we examined risk factors for glucose regulation in patients with known T2D. Methods: We examined 178 patients with known T2D and 196 controls. Anthropometrics and HbA1c were measured. Socio-demographic (age, gender, ethnicity and educational level) and health information were assessed through questionnaires. Results: Twenty-six (13.3%) and twelve (6.1%) participants in the control group were PD and UT2D, respectively. Control participants from South Asian descent were at a greater risk of being PD or UT2D than Arab controls (Odds Ratio: 5.27 (95% confidence interval: 2.10, 13.22). Being obese was also associated with an increased risk of PD or UT2D (Odds Ratio: 2.94 (95% confidence interval: 1.31, 6.59). Of control participants from South Asian descent, 38% were actually PD or UT2D. In patients with known T2D, insulin use and a longer duration of T2D were both related to higher HbA1c levels (both p