Browsing by keyword "Workflow"
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DolphinNext: a distributed data processing platform for high throughput genomicsBACKGROUND: The emergence of high throughput technologies that produce vast amounts of genomic data, such as next-generation sequencing (NGS) is transforming biological research. The dramatic increase in the volume of data, the variety and continuous change of data processing tools, algorithms and databases make analysis the main bottleneck for scientific discovery. The processing of high throughput datasets typically involves many different computational programs, each of which performs a specific step in a pipeline. Given the wide range of applications and organizational infrastructures, there is a great need for highly parallel, flexible, portable, and reproducible data processing frameworks. Several platforms currently exist for the design and execution of complex pipelines. Unfortunately, current platforms lack the necessary combination of parallelism, portability, flexibility and/or reproducibility that are required by the current research environment. To address these shortcomings, workflow frameworks that provide a platform to develop and share portable pipelines have recently arisen. We complement these new platforms by providing a graphical user interface to create, maintain, and execute complex pipelines. Such a platform will simplify robust and reproducible workflow creation for non-technical users as well as provide a robust platform to maintain pipelines for large organizations. RESULTS: To simplify development, maintenance, and execution of complex pipelines we created DolphinNext. DolphinNext facilitates building and deployment of complex pipelines using a modular approach implemented in a graphical interface that relies on the powerful Nextflow workflow framework by providing 1. A drag and drop user interface that visualizes pipelines and allows users to create pipelines without familiarity in underlying programming languages. 2. Modules to execute and monitor pipelines in distributed computing environments such as high-performance clusters and/or cloud 3. Reproducible pipelines with version tracking and stand-alone versions that can be run independently. 4. Modular process design with process revisioning support to increase reusability and pipeline development efficiency. 5. Pipeline sharing with GitHub and automated testing 6. Extensive reports with R-markdown and shiny support for interactive data visualization and analysis. CONCLUSION: DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.
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Electronic Bedside Documentation and Nurse-Patient Communication: A DissertationNurses are often the first members of the health care team with whom patients interact. The initial impression of the nurses’ receptiveness to the patients’ needs influences the patients’ views of their overall care. Researchers have suggested that understanding communication between individuals can provide the human link, or social element, to the successful implementation and use of electronic health records, including documentation (Lanham, Leykum, & McDaniel, 2012). Zadvinskis, Chipps, and Yen (2014) identified that the helpful features of bedside documentation systems were offset by the mismatch between the system and nurse’s workflow. The purpose of this micro-ethnography study was to explore the culture of nurse-patient interaction associated with electronic documentation at the bedside. Data were collected through passive participant observation, audio-taping of the nurse-patient interactions, and informal and semi-structured interviews with the nurses. A total of twenty-six observations were conducted on three nursing units at an urban healthcare facility in New England. These three units were occupied by similar patient populations and all patients required cardiac monitoring. Three themes consistently emerged from qualitative data analysis: the nurses paused during verbal communication, the nurses played a game of tag between the patient and the computer, and the nurses performed automatic or machine-like actions. The participants described these themes in the informal and semi-structured interviews. The nurses’ actions were observed during passive participant observation, and the audio-taped interactions supported these themes. Understanding the adaptation of caregiving necessitated by bedside electronic documentation will have a positive impact on developing systems that interface seamlessly with the nurses’ workflow and encourage patients’ active participation in their care.
