Bioimage analysis workflows: community resources to navigate through a complex ecosystem

Abstract

Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them. One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows. The emergence of "big data” in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows. These challenges are to reproducibly run workflows in remote environments, in particular when their components come from different software packages, but also to document them and link their parameters and results by following the FAIR principles (Findable, Accessible, Interoperable, Reusable) to foster open and reproducible science. In this opinion paper, we focus on giving some directions to the reader to tackle these challenges and navigate through this complex ecosystem, in order to find and use workflows, and to compare workflows addressing the same problem. We also discuss tools to run workflows in the cloud and on High Performance Computing resources, and suggest ways to make these workflows FAIR.

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

“…For example, ensuring that data sources and computations follow the well-known principles of Findable, Accessible, Interoperable, Reusable (FAIR) [114] demands a comprehensive management system. The life sciences in particular have a rich history of workflow systems [73,115,88,69], a few of which are discussed in our sections on tools (Section 6) and case studies (Section 7).…”

“…However, the wide variety of workflow management tools and systems available today have an expanded set of concerns beyond compilation steps [98,88,91]. This can include automatic versioning and provenance concerns, deployment to computational resources, and providing components for use in workflows.…”

“…However there still remains a challenge to connect the workflow solutions present in the repository with the problem faced by the domain expert [41]. The literature discusses manual and automatic semantic extraction techniques which can assist domain experts in finding workflows [27,88]. However, enabling this atscale across multiple domains and tools will continue to be a challenge.…”

“…Crowdsourcing knowledge is also another possibility to build up the workflow community. For example, Paul-Gilloteaux et al suggest the organisation of regular "taggathons" to annotate tools, workflows, components, databases, and training materials with terms from an ontology [88].…”

Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks

“…Image analysis suffers from many of the same problems plaguing other big data fields including scalability, reproducibility, interoperability, and accessibility. In particular, Paul-Gilloteaux et al discussed the challenges of bioimage analysis and recommended bioimage analysis workflows to move towards reproducibility, interoperability, findability, and accessibility 9 . Analytical workflows for processing images are becoming increasingly complex requiring the orchestration of multiple pieces of software, which raises problems of automation and versioning.…”