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ItemUnderstanding role of provenance in bioinformatics workflows and enabling interoperable computational analysis sharingKhan, Farah Zaib ( 2018)The automation of computational analyses in data-intensive domains such as genomics through scientific workflows is a widely adopted practice in many fields of research nowadays. Computationally driven data-intensive experiments using workflows enable Automation, Scaling, Adaption and Provenance support (ASAP). Provenance data collection is an essential factor for any computational workflow-centric research to achieve reproducibility, transparency and support trust in the published results. At present capture of provenance information across the plethora of workflow management systems and custom software platforms in the bioinformatics domain is not well supported and as such, there exist numerous challenges associated with the effective sharing, publication, understandability, reproducibility and repeatability of scientific workflows. This thesis focuses on providing a unified, interoperable and systematised view of provenance with specific focus on workflow environments in the bioinformatics domain. We identify and overcome the current disconnect between various workflows systems and their existing provenance representations. Through empirical analysis of complex genomic data analysis workflows using three exemplar workflow systems, we identify implicit assumptions that arise. These assumptions produce an incomplete view of provenance resulting in insufficient details that impact on workflow enactment requirements and ultimately on the reproducibility of the given analysis. We propose a set of recommendations to mitigate against such assumptions and enable workflow systems to document and capture complete provenance information that can subsequently be used for re-enacting workflows in other contexts and potentially using other workflow platforms. Based on this empirical case study and pragmatic analysis of related literature, we define a hierarchical provenance framework offering `Levels of Provenance and Resource Sharing''. Each level of this framework addresses specific provenance recommendations and supports the capture of rich provenance information, with the topmost layer enabling the sharing of comprehensive and executable workflows utilising retrospective provenance. To realise this framework, we leverage community-driven, domain-neutral, platform-independent and open-source standards to implement ``CWLProv'' - a format for the methodical representation of provenance supporting workflow enactment aggregating resources specific to the given enactment and associated workflow configuration settings. We realise CWLProv through the Common Workflow Language (CWL) for workflow definition and utilise Research Objects (ROs) for resource aggregation and PROV-Data Model (PROV-DM) to support the capture of retrospective provenance information as required for subsequent workflow enactments. To demonstrate the applicability of CWLProv, we extend an existing workflow executor (cwltool) to provide a reference implementation that generates metadata and provenance-rich interoperable workflow-centric ROs. This approach aggregates and preserves data and methods needed to support the coherent sharing of computational analyses and experiments. Evaluation of CWLProv using real-life bioinformatics pipelines is demonstrated to highlight the utility of the approach demonstrating the interoperability of workflow analyses and the benefits to research reproducibility more generally.