Dataset Information

Qsubsec: a lightweight template system for defining sun grid engine workflows.

ABSTRACT: The Sun Grid Engine (SGE) high-performance computing batch queueing system is commonly used in bioinformatics analysis. Creating re-usable scripts for the SGE is a common challenge. The qsubsec template language and interpreter described here allow researchers to easily create generic template definitions that encapsulate a particular computational job, effectively separating the process logic from the specific run details. At submission time, the generic template is filled in with specific values. This system provides an intermediate level between simple scripting and complete workflow management tools.Qsubsec is open-source and is available at https://github.com/alastair-droop/qsubseca.p.droop@leeds.ac.ukSupplementary data are available at Bioinformatics online.

SUBMITTER: Droop AP

PROVIDER: S-EPMC4824124 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

qsubsec: a lightweight template system for defining sun grid engine workflows.

Droop Alastair P AP

Bioinformatics (Oxford, England) 20151203 8

<h4>Unlabelled</h4>The Sun Grid Engine (SGE) high-performance computing batch queueing system is commonly used in bioinformatics analysis. Creating re-usable scripts for the SGE is a common challenge. The qsubsec template language and interpreter described here allow researchers to easily create generic template definitions that encapsulate a particular computational job, effectively separating the process logic from the specific run details. At submission time, the generic template is filled in ...[more]

PMID: 26635140

Similar Datasets

Project description:PURPOSE:Standard radiological/topographical classifications of gliomas often do not reflect the real extension of the tumor within the lobar-cortical anatomy. Furthermore, these systems do not provide information on the relationship between tumor growth and the subcortical white matter architecture. We propose the use of an anatomically standardized grid system (the Brain-Grid) to merge serial morphological magnetic resonance imaging (MRI) scans with a representative tractographic atlas. Two illustrative cases are presented to show the potential advantages of this classification system. METHODS:MRI scans of 39 patients (WHO grade II and III gliomas) were analyzed with a standardized grid created by intersecting longitudinal lines on the axial, sagittal, and coronal planes. The anatomical landmarks were chosen from an average brain, spatially normalized to the Montreal Neurological Institute (MNI) space and the Talairach space. Major white matter pathways were reconstructed with a deterministic tracking algorithm on a reference atlas and analyzed using the Brain-Grid system. RESULTS:In all, 48 brain grid voxels (areas defined by 3 coordinates, axial (A), coronal (C), sagittal (S) and numbers from 1 to 4) were delineated in each MRI sequence and on the tractographic atlas. The number of grid voxels infiltrated was consistent, also in the MNI space. The sub-cortical insula/basal ganglia (A3-C2-S2) and the fronto-insular region (A3-C2-S1) were most frequently involved. The inferior fronto-occipital fasciculus, anterior thalamic radiation, uncinate fasciculus, and external capsule were the most frequently associated pathways in both hemispheres. CONCLUSIONS:The Brain-Grid based classification system provides an accurate observational tool in all patients with suspected gliomas, based on the comparison of grid voxels on a morphological MRI and segmented white matter atlas. Important biological information on tumor kinetics including extension, speed, and preferential direction of progression can be observed and even predicted with this system. This novel classification can easily be applied to both prospective and retrospective cohorts of patients and increase our comprehension of glioma behavior.

Project description:BackgroundEHR systems are widely used in hospitals and primary care centres but it is usually difficult to share information and to collect patient data for clinical research. This is partly due to the different proprietary information models and inconsistent data quality. Our objective was to provide a more flexible solution enabling the clinicians to define which data to be recorded and shared for both routine documentation and clinical studies. The data should be possible to reuse through a common set of variable definitions providing a consistent nomenclature and validation of data. Another objective was that the templates used for the data entry and presentation should be possible to use in combination with the existing EHR systems.MethodsWe have designed and developed a template based system (called Julius) that was integrated with existing EHR systems. The system is driven by the medical domain knowledge defined by clinicians in the form of templates and variable definitions stored in a common data repository. The system architecture consists of three layers. The presentation layer is purely web-based, which facilitates integration with existing EHR products. The domain layer consists of the template design system, a variable/clinical concept definition system, the transformation and validation logic all implemented in Java. The data source layer utilizes an object relational mapping tool and a relational database.ResultsThe Julius system has been implemented, tested and deployed to three health care units in Stockholm, Sweden. The initial responses from the pilot users were positive. The template system facilitates patient data collection in many ways. The experience of using the template system suggests that enabling the clinicians to be in control of the system, is a good way to add supplementary functionality to the present EHR systems.ConclusionThe approach of the template system in combination with various local EHR systems can facilitate the sharing and reuse of validated clinical information from different health care units. However, future system developments for these purposes should consider using the openEHR/CEN models with shareable archetypes.

Dataset Information

Qsubsec: a lightweight template system for defining sun grid engine workflows.

Publications

qsubsec: a lightweight template system for defining sun grid engine workflows.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets