rCASC implementation in Laniakea: porting containerization-based-reproducibility to a cloud Galaxy on-demand platform

Integrating rCASC in Laniakea: rCASC, Cluster Analysis of Single Cells [Alessandri et al. BioRxiv], is part of the reproducible-bioinformatics.org project and provides single cell analysis functionalities within the reproducible rules described by Sandve et al. [PLoS Comp Biol. 2013]. Laniakea [Tangaro et al. BioRxiv Bioinformatics] provides the possibility to automate the creation of Galaxy-based virtualized environments through an easy setup procedure, providing an on-demand workspace ready to be used by life scientists and bioinformaticians. The final goal is to offer rCASC as a Galaxy flavor in the Laniakea Galaxy on-demand environment

Laniakea : an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures

Background: While the popular workflow manager Galaxy is currently made available through several publicly accessible servers, there are scenarios where users can be better served by full administrative control over a private Galaxy instance, including, but not limited to, concerns about data privacy, customisation needs, prioritisation of particular job types, tools development, and training activities. In such cases, a cloud-based Galaxy virtual instance represents an alternative that equips the user with complete control over the Galaxy instance itself without the burden of the hardware and software infrastructure involved in running and maintaining a Galaxy server. Results: We present Laniakea, a complete software solution to set up a \u201cGalaxy on-demand\u201d platform as a service. Building on the INDIGO-DataCloud software stack, Laniakea can be deployed over common cloud architectures usually supported both by public and private e-infrastructures. The user interacts with a Laniakea-based service through a simple front-end that allows a general setup of a Galaxy instance, and then Laniakea takes care of the automatic deployment of the virtual hardware and the software components. At the end of the process, the user gains access with full administrative privileges to a private, production-grade, fully customisable, Galaxy virtual instance and to the underlying virtual machine (VM). Laniakea features deployment of single-server or cluster-backed Galaxy instances, sharing of reference data across multiple instances, data volume encryption, and support for VM image-based, Docker-based, and Ansible recipe-based Galaxy deployments. A Laniakea-based Galaxy on-demand service, named Laniakea@ReCaS, is currently hosted at the ELIXIR-IT ReCaS cloud facility. Conclusions: Laniakea offers to scientific e-infrastructures a complete and easy-to-use software solution to provide a Galaxy on-demand service to their users. Laniakea-based cloud services will help in making Galaxy more accessible to a broader user base by removing most of the burdens involved in deploying and running a Galaxy service. In turn, this will facilitate the adoption of Galaxy in scenarios where classic public instances do not represent an optimal solution. Finally, the implementation of Laniakea can be easily adapted and expanded to support different services and platforms beyond Galaxy

Laniakea: a Galaxy-on-demand Provider Platform Through Cloud Technologies

Galaxy is rapidly becoming the de facto standard workflow manager for bioinformatics. Although several Galaxy public services are currently available, the usage of a private Galaxy instance is still mandatory or preferable for several use cases, including heavy workloads, data privacy concerns or particular customization needs. In this context, cloud computing technologies and infrastructures can provide a powerful and scalable solution to avoid the onerous deployment and maintenance of a local hardware and software infrastructure. Laniakea is a software framework that facilitates the provisioning of on-demand Galaxy instances as a cloud service over e-infrastructures, by leveraging on the open source software catalogue developed by the INDIGO-DataCloud H2020 project, which aimed to make cloud e-infrastructures more accessible by scientific communities. End-users interact with Laniakea through a web front-end that allows a general setup of a Galaxy instance. The deployment of the virtual hardware and of the Galaxy software ecosystem is subsequently performed by the INDIGO Platform as a Service layer. At the end of the process, the user gains access to a private, production-grade, fully customizable, Galaxy virtual instance. Laniakea features the deployment of a stand-alone or cluster backed Galaxy instances, shared reference data volumes, encrypted data volumes and rapid development of novel Galaxy flavours for specific tasks. We present here the latest development iteration of Laniakea, introducing a novel and strongly configurable web interface that facilitates a more straightforward customisation of the user experience through human readable YAML syntax and a reworked encryption procedure that exploits Hashicorp Vault as encryption keys management system

Laniakea@ReCaS: an ELIXIR-ITALY Galaxyon-demand cloud service

Although several Galaxy public services are available, a private Galaxy instance is still mandatory or preferable for several use cases including heavy workloads, data privacy concerns or particular customization needs. Cloud computing technologies provide a viable way to deploy Galaxy private instances, freeing users from the onerous deployment and maintenance of local IT infrastructures. In the last few years, ELIXIR-IT led the development of Laniakea, a software framework that facilitates the provisioning of on-demand Galaxy instances as a cloud service over e-infrastructures. The user interacts with a Laniakea service through a web front-end that allows to configure and launch a production-grade Galaxy instance in a straightforward way. Through the interface, the user can deploy Galaxy instances over single VMs or virtual clusters, link them to shared reference data volumes and plain or encrypted volumes for storing data. A selection of \u201cflavours\u201d, that is Galaxy instances pre-configured with sets of tools for specific tasks, is also available. When the users is satisfied, Laniakea takes oved and deploys the desired Galaxy instance over the cloud, providing a public IP and full administrative privileges over the new instance. In Dec-2018, we launched the beta-test phase of the first Laniakea-based Galaxy on-demand ELIXIR-IT service: Laniakea@ReCaS. After six months of helpful testing, we are now ready to announce the production phase of this service. Access to the service will be provided on a per-project basis through an open-ended call defining terms and conditions, project proposals will be evaluated by a scientific and technical board. Accepted proposals will be granted a package of computational resources for running on-demand Galaxy instances for a duration compatible with the project requirements

VINYL: Variant prIoritizatioN bY survivaL analysis

Motivation: Clinical applications of genome re\uadsequencing technologies typically generate large amounts of data that need to be carefully annotated and interpreted to identify genetic variants associated with pathological conditions. In this context, accurate and reproducible methods for the functional annotation and prioritization of genetic variants are of fundamental importance, especially when large volumes of data \uad like those produced by modern sequencing technologies \uad are involved. Results: In this paper, we present VINYL, a highly accurate and fully automated system for the functional annotation and prioritization of genetic variants in large scale clinical studies. Extensive analyses of both real and simulated datasets suggest that VINYL show higher accuracy and sensitivity when compared to equivalent state of the art methods, allowing the rapid and systematic identification of potentially pathogenic variants in different experimental settings

Laniakea: an open solution to provide Galaxy "on-demand" instances over heterogeneous cloud infrastructures

Background: Galaxy is rapidly becoming the de facto standard among workflow managers for bioinformatics. A rich feature set, its overall flexibility, and a thriving community of enthusiastic users are among the main factors contributing to the popularity of Galaxy and Galaxy based applications. One of the main advantages of Galaxy consists in providing access to sophisticated analysis pipelines, e.g., involving numerous steps and large data sets, even to users lacking computer proficiency, while at the same time improving reproducibility and facilitating teamwork and data sharing among researchers. Although several Galaxy public services are currently available, these resources are often overloaded with a large number of jobs and offer little or no customization options to end users. Moreover, there are scenarios where a private Galaxy instance still constitutes a more viable alternative, including, but not limited to, heavy workloads, data privacy concerns or particular needs of customization. In such cases, a cloud-based virtual Galaxy instance can represent a solution that overcomes the typical burdens of managing the local hardware and software infrastructure needed to run and maintain a production-grade Galaxy service. Results: Here we present Laniakea, a robust and feature-rich software suite which can be deployed on any scientific or commercial Cloud infrastructure in order to provide a "Galaxy on demand" Platform as a Service (PaaS). Laying its foundations on the INDIGO-DataCloud middleware, which has been developed to accommodate the needs of a large number of scientific communities, Laniakea can be deployed and provisioned over multiple architectures by private or public e-infrastructures. The end user interacts with Laniakea through a front-end that allows a general setup of the Galaxy instance, then Laniakea takes charge of the deployment both of the virtual hardware and all the software components. At the end of the process the user has access to a private, production-grade, yet fully customizable, Galaxy virtual instance. Laniakea's supports the deployment of plain or cluster backed Galaxy instances, shared reference data volumes, encrypted data volumes and rapid development of novel Galaxy flavours, that is Galaxy configurations tailored for specific tasks. As a proof of concept, we provide a demo Laniakea instance hosted at an ELIXIR-IT Cloud facility. Conclusions: The migration of scientific computational services towards virtualization and e-infrastructures is one of the most visible trends of our times. Laniakea provides Cloud administrators with a ready-to-use software suite that enables them to offer Galaxy, a popular workflow manager for bioinformatics, as an on-demand PaaS to their users. We believe that Laniakea can concur in making the many advantages of using Galaxy more accessible to a broader user base by removing most of the burdens involved in running a private instance. Finally, Laniakea's design is sufficiently general and modular that could be easily adapted to support different services and platforms beyond Galaxy

Chronic kidney disease progression and outcome according to serum phosphorus in mild-to-moderate kidney dysfunction

BACKGROUND AND OBJECTIVES: Several factors might alter serum phosphate homeostasis and induce hyperhosphatemia in patients with chronic kidney disease (CKD) not requiring dialysis. However, whether and to what extent hyperphosphatemia is associated with a poor prognosis in different CKD patient groups remain to be elucidated. DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS: We utilized the "Prevenzione Insufficienza Renale Progressiva" (PIRP) database, a large project sponsored by the Emilia-Romagna Health Institute. PIRP is a collaborative network of nephrologists and general practitioners located in the Emilia-Romagna region, Italy, aimed at increasing awareness of CKD complications and optimizing CKD patient care. We identified 1716 patients who underwent a GFR and serum phosphorous assessment between 2004 and 2007. We tested whether phosphate levels 654.3 mg/dl are associated with the risk of CKD progression or all causes of death. RESULTS: Older age and male sex were associated with lower phosphate levels. Instead, higher phosphate levels were noted in patients with diabetes. Patients with phosphate levels 654.3 mg/dl were at an increased risk of starting dialysis or dying (hazard ratio 2.04; 95% confidence interval [1.44, 2.90]). Notably, subgroup analyses revealed that the magnitude of the risk associated with hyperphosphatemia varied depending on age, sex, diabetes, and different stages of CKD. CONCLUSIONS: These analyses lend support to the hypothesis that phosphorous abnormalities might have a negative effect on the residual renal function and prognosis in different groups of CKD patients. However, the risk associated with hyperphosphatemia might vary in specific CKD patient subgroups

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency.

Aims- The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods- This was an observational study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients\u2019 medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO 2 /FiO 2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. Results- A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth \u201cboosted mixed model\u201d included 20 variables was selected from the model 3, achieved the best predictive performance (AUC=0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. Conclusion- This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels

Effect of SGLT2 inhibitors on stroke and atrial fibrillation in diabetic kidney disease: Results from the CREDENCE trial and meta-analysis

BACKGROUND AND PURPOSE: Chronic kidney disease with reduced estimated glomerular filtration rate or elevated albuminuria increases risk for ischemic and hemorrhagic stroke. This study assessed the effects of sodium glucose cotransporter 2 inhibitors (SGLT2i) on stroke and atrial fibrillation/flutter (AF/AFL) from CREDENCE (Canagliflozin and Renal Events in Diabetes With Established Nephropathy Clinical Evaluation) and a meta-Analysis of large cardiovascular outcome trials (CVOTs) of SGLT2i in type 2 diabetes mellitus. METHODS: CREDENCE randomized 4401 participants with type 2 diabetes mellitus and chronic kidney disease to canagliflozin or placebo. Post hoc, we estimated effects on fatal or nonfatal stroke, stroke subtypes, and intermediate markers of stroke risk including AF/AFL. Stroke and AF/AFL data from 3 other completed large CVOTs and CREDENCE were pooled using random-effects meta-Analysis. RESULTS: In CREDENCE, 142 participants experienced a stroke during follow-up (10.9/1000 patient-years with canagliflozin, 14.2/1000 patient-years with placebo; hazard ratio [HR], 0.77 [95% CI, 0.55-1.08]). Effects by stroke subtypes were: ischemic (HR, 0.88 [95% CI, 0.61-1.28]; n=111), hemorrhagic (HR, 0.50 [95% CI, 0.19-1.32]; n=18), and undetermined (HR, 0.54 [95% CI, 0.20-1.46]; n=17). There was no clear effect on AF/AFL (HR, 0.76 [95% CI, 0.53-1.10]; n=115). The overall effects in the 4 CVOTs combined were: Total stroke (HRpooled, 0.96 [95% CI, 0.82-1.12]), ischemic stroke (HRpooled, 1.01 [95% CI, 0.89-1.14]), hemorrhagic stroke (HRpooled, 0.50 [95% CI, 0.30-0.83]), undetermined stroke (HRpooled, 0.86 [95% CI, 0.49-1.51]), and AF/AFL (HRpooled, 0.81 [95% CI, 0.71-0.93]). There was evidence that SGLT2i effects on total stroke varied by baseline estimated glomerular filtration rate (P=0.01), with protection in the lowest estimated glomerular filtration rate (45 mL/min/1.73 m2]) subgroup (HRpooled, 0.50 [95% CI, 0.31-0.79]). CONCLUSIONS: Although we found no clear effect of SGLT2i on total stroke in CREDENCE or across trials combined, there was some evidence of benefit in preventing hemorrhagic stroke and AF/AFL, as well as total stroke for those with lowest estimated glomerular filtration rate. Future research should focus on confirming these data and exploring potential mechanisms

Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to 300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m 2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years