Performance and scaling behavior of bioinformatic applications in virtualization environments to create awareness for the efficient use of compute resources.

The large amount of biological data available in the current times, makes it necessary to use tools and applications based on sophisticated and efficient algorithms, developed in the area of bioinformatics. Further, access to high performance computing resources is necessary, to achieve results in reasonable time. To speed up applications and utilize available compute resources as efficient as possible, software developers make use of parallelization mechanisms, like multithreading. Many of the available tools in bioinformatics offer multithreading capabilities, but more compute power is not always helpful. In this study we investigated the behavior of well-known applications in bioinformatics, regarding their performance in the terms of scaling, different virtual environments and different datasets with our benchmarking tool suite BOOTABLE. The tool suite includes the tools BBMap, Bowtie2, BWA, Velvet, IDBA, SPAdes, Clustal Omega, MAFFT, SINA and GROMACS. In addition we added an application using the machine learning framework TensorFlow. Machine learning is not directly part of bioinformatics but applied to many biological problems, especially in the context of medical images (X-ray photographs). The mentioned tools have been analyzed in two different virtual environments, a virtual machine environment based on the OpenStack cloud software and in a Docker environment. The gained performance values were compared to a bare-metal setup and among each other. The study reveals, that the used virtual environments produce an overhead in the range of seven to twenty-five percent compared to the bare-metal environment. The scaling measurements showed, that some of the analyzed tools do not benefit from using larger amounts of computing resources, whereas others showed an almost linear scaling behavior. The findings of this study have been generalized as far as possible and should help users to find the best amount of resources for their analysis. Further, the results provide valuable information for resource providers to handle their resources as efficiently as possible and raise the user community's awareness of the efficient usage of computing resources.

Genome-wide Association and Meta-analysis of Age at Onset in Parkinson Disease: Evidence From the COURAGE-PD Consortium.

BACKGROUND AND OBJECTIVES: Considerable heterogeneity exists in the literature concerning genetic determinants of the age at onset (AAO) of Parkinson disease (PD), which could be attributed to a lack of well-powered replication cohorts. The previous largest genome-wide association studies (GWAS) identified SNCA and TMEM175 loci on chromosome (Chr) 4 with a significant influence on the AAO of PD; these have not been independently replicated. This study aims to conduct a meta-analysis of GWAS of PD AAO and validate previously observed findings in worldwide populations. METHODS: A meta-analysis was performed on PD AAO GWAS of 30 populations of predominantly European ancestry from the Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease (COURAGE-PD) Consortium. This was followed by combining our study with the largest publicly available European ancestry dataset compiled by the International Parkinson Disease Genomics Consortium (IPDGC). RESULTS: The COURAGE-PD Consortium included a cohort of 8,535 patients with PD (91.9%: Europeans and 9.1%: East Asians). The average AAO in the COURAGE-PD dataset was 58.9 years (SD = 11.6), with an underrepresentation of females (40.2%). The heritability estimate for AAO in COURAGE-PD was 0.083 (SE = 0.057). None of the loci reached genome-wide significance (p < 5 × 10-8). Nevertheless, the COURAGE-PD dataset confirmed the role of the previously published TMEM175 variant as a genetic determinant of the AAO of PD with Bonferroni-corrected nominal levels of significance (p < 0.025): (rs34311866: ß(SE)COURAGE = 0.477(0.203), p COURAGE = 0.0185). The subsequent meta-analysis of COURAGE-PD and IPDGC datasets (Ntotal = 25,950) led to the identification of 2 genome-wide significant association signals on Chr 4, including the previously reported SNCA locus (rs983361: ß(SE)COURAGE+IPDGC = 0.720(0.122), p COURAGE+IPDGC = 3.13 × 10-9) and a novel BST1 locus (rs4698412: ß(SE)COURAGE+IPDGC = -0.526(0.096), p COURAGE+IPDGC = 4.41 × 10-8). DISCUSSION: Our study further refines the genetic architecture of Chr 4 underlying the AAO of the PD phenotype through the identification of BST1 as a novel AAO PD locus. These findings open a new direction for the development of treatments to delay the onset of PD.

de.NBI Cloud federation through ELIXIR AAI.

The academic de.NBI Cloud offers compute resources for life science research in Germany.  At the beginning of 2017, de.NBI Cloud started to implement a federated cloud consisting of five compute centers, with the aim of acting as one resource to their users. A federated cloud introduces multiple challenges, such as a central access and project management point, a unified account across all cloud sites and an interchangeable project setup across the federation. In order to implement the federation concept, de.NBI Cloud integrated with the ELIXIR authentication and authorization infrastructure system (ELIXIR AAI) and in particular Perun, the identity and access management system of ELIXIR. The integration solves the mentioned challenges and represents a backbone, connecting five compute centers which are based on OpenStack and a web portal for accessing the federation.This article explains the steps taken and software components implemented for setting up a federated cloud based on the collaboration between de.NBI Cloud and ELIXIR AAI. Furthermore, the setup and components that are described are generic and can therefore be used for other upcoming or existing federated OpenStack clouds in Europe.