Background: Advances in high-throughput methods have brought new challenges for biological data analysis, often requiring many interdependent steps applied to a large number of samples. To address this challenge, workflow management systems, such as Watchdog, have been developed to support scientists in the (semi-)automated execution of large analysis workflows.
Implementation: Here, we present Watchdog 2.0, which implements new developments for module creation, reusability, and documentation and for reproducibility of analyses and workflow execution. Developments include a graphical user interface for semi-automatic module creation from software help pages, sharing repositories for modules and workflows, and a standardized module documentation format. The latter allows generation of a customized reference book of public and user-specific modules. Furthermore, extensive logging of workflow execution, module and software versions, and explicit support for package managers and container virtualization now ensures reproducibility of results. A step-by-step analysis protocol generated from the log file may, e.g., serve as a draft of a manuscript methods section. Finally, 2 new execution modes were implemented. One allows resuming workflow execution after interruption or modification without rerunning successfully executed tasks not affected by changes. The second one allows detaching and reattaching to workflow execution on a local computer while tasks continue running on computer clusters.
Conclusions: Watchdog 2.0 provides several new developments that we believe to be of benefit for large-scale bioinformatics analysis and that are not completely covered by other competing workflow management systems. The software itself, module and workflow repositories, and comprehensive documentation are freely available at https://www.bio.ifi.lmu.de/watchdog.
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http://dx.doi.org/10.1093/gigascience/giaa068 | DOI Listing |
Genome Res
January 2025
Center for Genomic Research (CRG), Universitat Pompeu Fabra (UPF), ICREA
Nanopore direct RNA sequencing (DRS) enables direct measurement of RNA molecules, including their native RNA modifications, without prior conversion to cDNA. However, commercial methods for molecular barcoding of multiple DRS samples are lacking, and community-driven efforts, such as DeePlexiCon, are not compatible with newer RNA chemistry flowcells and the latest-generation GPU cards. To overcome these limitations, we introduce SeqTagger, a rapid and robust method that can demultiplex direct RNA sequencing datasets with 99% precision and 95% recall.
View Article and Find Full Text PDFBeilstein J Nanotechnol
January 2025
Seven Past Nine GmbH, Rebacker 68, 79650 Schopfheim, Germany.
Nanosafety assessment, which seeks to evaluate the risks from exposure to nanoscale materials, spans materials synthesis and characterisation, exposure science, toxicology, and computational approaches, resulting in complex experimental workflows and diverse data types. Managing the data flows, with a focus on provenance (who generated the data and for what purpose) and quality (how was the data generated, using which protocol with which controls), as part of good research output management, is necessary to maximise the reuse potential and value of the data. Instance maps have been developed and evolved to visualise experimental nanosafety workflows and to bridge the gap between the theoretical principles of FAIR (Findable, Accessible, Interoperable and Re-usable) data and the everyday practice of experimental researchers.
View Article and Find Full Text PDFBackground: Heart Failure (HF) quality of care (QoC) is associated with clinical outcomes. Therefore, we investigated differences in HF QoC across worldwide regions (with differing national income) and the association of quality indicators with outcomes.
Methods: We examined the quality of care (QoC) in acute heart failure (HF) patients across different regions using quality indicators (QIs) from the European Society of Cardiology (ESC) and the American Heart Association (AHA) to evaluate QoC.
Sci Rep
January 2025
School of Information Engineering, Sanming University, Sanming, 365004, China.
Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability.
View Article and Find Full Text PDFRheumatology (Oxford)
January 2025
Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
Objective: This study investigates the barriers and facilitators to sustaining a decision aid (DA) tool for patients with systemic lupus erythematosus (SLE) in routine rheumatology outpatient care across the U.S. The DA was initially developed for assisting Patients with SLE in making informed medication choices by providing personalized information on their treatment process.
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