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Fadlelmola, F. M.

Publications and source records attributed to Fadlelmola, F. M..

2 recordsLinked to original sources

Design considerations for workflow management systems use in production genomics research and the clinic

BackgroundThe changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. ResultsThis work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, "which WfMS should be chosen for a given bioinformatics application regardless of analysis type?". ConclusionsThe choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.

bioinformatics↗

Blended Bioinformatics Training in Resource-Limited Settings: A case study of challenges and opportunities for implementation

MotivationDelivering high quality distance-based courses in resource limited settings is a challenging task. Besides the needed infrastructure and expertise, effective delivery of a bioinformatics course could benefit from hands-on sessions, interactivity, and problem-based learning approaches.\n\nResultsIn this article, we discuss the challenges and best practices in delivering bioinformatics training in limited resource settings taking the example of hosting and running a multiple-delivery online course, Introduction to Bioinformatics (IBT), that was developed by the H3ABioNet Education and Training Working Group and delivered in 27 remote classrooms across Africa in 2017. We take the case of the University of Khartoum classroom. Believing that our local setting is similar to others in less developed countries, so we also reflect upon aspects like classroom environment and recruitment of students to maximize outcomes.\n\nSupplementary informationSupplementary data are available

scientific communication and education↗