MOTIVATION: The rapid growth of biological data has intensified the need for transparent, reproducible, and well-documented computational workflows. The ability to clearly connect the steps of a workflow in the code with their description in a paper would improve workflow comprehension, support reproducibility, and facilitate reuse. This task requires the linking of bioinformatics tools in workflow code with their mentions in a published workflow description. RESULTS: We present CoPaLink, an automated approach that integrates three components: named entity recognition (NER) for identifying tool mentions in scientific text, NER for tool mentions in workflow code, and entity resolution based on word embedding similarity. We propose approaches for all three steps, achieving a high individual F1-measure (77-90) and a joint accuracy of 66 when evaluated on Nextflow workflows using Sentence-BERT. CoPaLink leverages corpora of scientific articles and workflow executable code with curated tool annotations to bridge the gap between narrative descriptions and workflow implementations. AVAILABILITY AND IMPLEMENTATION: The code is available at https://gitlab.liris.cnrs.fr/sharefair/copalink-experiments and https://gitlab.liris.cnrs.fr/sharefair/copalink. The corpora are also available: CPL-Article (https://doi.org/10.5281/zenodo.20746904), CPL-Code (https://doi.org/10.5281/zenodo.20746970) and CPL-Gold-Entity-Resolution (https://doi.org/10.5281/zenodo.20746994).

Information extraction from text is essential in data science and artificial intelligence, especially with the increase in the number of scientific articles. Robust methods are needed to structure texts and highlight key information. Named Entity Recognition (NER) identifies and classifies major elements in text, aiding in dataset structuring and tagging.Under the FAIRClinical and ShareFAIR projects, we aimed to extract information from clinical trial publications using NER models. These models have been successfully used for clinical trial information extraction. In this paper, we report on the difficulties met in reusing existing NER solutions, focusing on mandatory replicability. This paper discusses the challenges faced in replicating a notable study, the difficulties encountered and lessons learned. It compares our experiment with the feedback provided by the literature and draws conclusions.

MOTIVATION: The reproducibility crisis has highlighted the importance of improving the way bioinformatics data analyses are implemented, executed, and shared. To address this, various tools such as content versioning systems, workflow management systems, and software environment management systems have been developed. While these tools are becoming more widely used, there is still much work to be done to increase their adoption. The most effective way to ensure reproducibility becomes a standard part of most bioinformatics data analysis projects is to integrate it into the curriculum of bioinformatics Master's programs. RESULTS: In this article, we present the Reprohackathon, a Master's course that we have been running for the last 3 years at Université Paris-Saclay (France), and that has been attended by a total of 123 students. The course is divided into two parts. The first part includes lessons on the challenges related to reproducibility, content versioning systems, container management, and workflow systems. In the second part, students work on a data analysis project for 3-4 months, reanalyzing data from a previously published study. The Reprohackaton has taught us many valuable lessons, such as the fact that implementing reproducible analyses is a complex and challenging task that requires significant effort. However, providing in-depth teaching of the concepts and the tools during a Master's degree program greatly improves students' understanding and abilities in this area.

In multiple domains, large amounts of data are daily generated and combined to be analyzed. The interpretation of these analyses requires to track back the provenance of combined data with respect to initial, raw data. The correctness of the provenance is crucial in many critical domains, such as medicine to prescribe treatments. In this article, we propose the first provenance-aware extended relational algebra formalized in a proof assistant (Coq), for a non trivial subset of database queries: queries containing aggregates, null values, and correlated sub-queries. The formalization is validated by an adequacy proof with respect to standard evaluation of queries. This development is a first step towards a posteriori certification of provenance for data manipulation, with strong guaranties.

Massive biological datasets are available in public databases and can be queried using portals with keyword queries. Ranked lists of answers are obtained by users. However, properly querying such portals remains difficult since various formulations of the same query can be considered (e.g., using synonyms). Consequently, users have to manually combine several lists of hundreds of answers into one list. Rank aggregation techniques are particularly well-fitted to this context as they take in a set of ranked elements (rankings) and provide a consensus, that is, a single ranking which is the "closest" to the input rankings. However, the problem of rank aggregation is NP-hard in most cases. Using an exact algorithm is currently not possible for more than a few dozens of elements. A plethora of heuristics have thus been proposed which behaviour are, by essence, difficult to anticipate: given a set of input rankings, one cannot guarantee how far from an exact solution the consensus ranking provided by an heuristic will be. The two challenges we want to tackle in this paper are the following: (i) providing an approach based on a pre-process to decompose large data sets into smaller ones where high-quality algorithms can be run and (ii) providing information to users on the robustness of the positions of elements in the consensus ranking produced. Our approach not only lies in mathematical bases, offering guarantees on the result computed but it has also been implemented in a real system available to life science community and tested on various real use cases.

SPARQL is the standard query language used to access RDF linked data sets available on the Web. However, designing a SPARQL query can be a tedious task, even for experienced users. This is often due to imperfect knowledge by the user of the ontologies involved in the query. To overcome this problem, a growing number of query editors offer autocompletetion features. Such features are nevertheless limited and mostly focused on typo checking. In this context, our contribution is four-fold. First, we analyze several autocompletion features proposed by the main editors, highlighting the needs currently not taken into account while met by a user community we work with, scientists. Second, we introduce the first (to our knowledge) autocompletion approach able to consider snippets (fragments of SPARQL query) based on queries expressed by previous users, enriching the user experience. Third, we introduce a usable, open and concrete solution able to consider a large panel of SPARQL autocompletion features that we have implemented in an editor. Last but not least, we demonstrate the interest of our approach on real biomedical queries involving services offered by the Wikidata collaborative knowledge base.

Analyzing biological data (e.g., annotating genomes, assembling NGS data...) may involve very complex and interlinked steps where several tools are combined together. Scientific workflow systems have reached a level of maturity that makes them able to support the design and execution of such in-silico experiments, and thus making them increasingly popular in the bioinformatics community.

International audience

Scientific workflows have become a valuable tool for large-scale data processing and analysis. This has led to the creation of specialized online repositories to facilitate workflow sharing and reuse. Over time, these repositories have grown to sizes that call for advanced methods to support workflow discovery, in particular for effective similarity search. Here, we present a novel and intuitive workflow similarity measure that is based on layer decomposition. Layer decomposition accounts for the directed dataflow underlying scientific workflows, a property which has not been adequately considered in previous methods. We comparatively evaluate our algorithm using a gold standard for 24 query workflows from a repository of almost 1500 scientific workflows, and show that it a) delivers the best results for similarity search, b) has a much lower runtime than other, often highly complex competitors in structure-aware workflow comparison, and c) can be stacked easily with even faster, structure-agnostic approaches to further reduce runtime while retaining result quality.

Scientific workflows management systems are increasingly used by scientists to specify complex data processing pipelines. Workflows are represented using a graph structure, where nodes represent tasks and links represent the dataflow. However, the complexity of workflow structures is increasing over time, reducing the rate of scientific workflows reuse. Here, we introduce DistillFlow, a tool based on effective methods for workflow design, with a focus on the Taverna model. DistillFlow is able to detect "anti-patterns" in the structure of workflows (idiomatic forms that lead to over-complicated design) and replace them with different patterns to reduce the workflow's overall structural complexity. Rewriting workflows in this way is beneficial both in terms of user experience and workflow maintenance.

BACKGROUND: Scientific workflows management systems are increasingly used to specify and manage bioinformatics experiments. Their programming model appeals to bioinformaticians, who can use them to easily specify complex data processing pipelines. Such a model is underpinned by a graph structure, where nodes represent bioinformatics tasks and links represent the dataflow. The complexity of such graph structures is increasing over time, with possible impacts on scientific workflows reuse. In this work, we propose effective methods for workflow design, with a focus on the Taverna model. We argue that one of the contributing factors for the difficulties in reuse is the presence of "anti-patterns", a term broadly used in program design, to indicate the use of idiomatic forms that lead to over-complicated design. The main contribution of this work is a method for automatically detecting such anti-patterns, and replacing them with different patterns which result in a reduction in the workflow's overall structural complexity. Rewriting workflows in this way will be beneficial both in terms of user experience (easier design and maintenance), and in terms of operational efficiency (easier to manage, and sometimes to exploit the latent parallelism amongst the tasks). RESULTS: We have conducted a thorough study of the workflows structures available in Taverna, with the aim of finding out workflow fragments whose structure could be made simpler without altering the workflow semantics. We provide four contributions. Firstly, we identify a set of anti-patterns that contribute to the structural workflow complexity. Secondly, we design a series of refactoring transformations to replace each anti-pattern by a new semantically-equivalent pattern with less redundancy and simplified structure. Thirdly, we introduce a distilling algorithm that takes in a workflow and produces a distilled semantically-equivalent workflow. Lastly, we provide an implementation of our refactoring approach that we evaluate on both the public Taverna workflows and on a private collection of workflows from the BioVel project. CONCLUSION: We have designed and implemented an approach to improving workflow structure by way of rewriting preserving workflow semantics. Future work includes considering our refactoring approach during the phase of workflow design and proposing guidelines for designing distilled workflows.

With the increasing popularity of scientific workflows, public repositories are gaining importance as a means to share, find, and reuse such workflows. As the sizes of these repositories grow, methods to compare the scientific workflows stored in them become a necessity, for instance, to allow duplicate detection or similarity search. Scientific workflows are complex objects, and their comparison entails a number of distinct steps from comparing atomic elements to comparison of the workflows as a whole. Various studies have implemented methods for scientific workflow comparison and came up with often contradicting conclusions upon which algorithms work best. Comparing these results is cumbersome, as the original studies mixed different approaches for different steps and used different evaluation data and metrics. We contribute to the field (i) by disecting each previous approach into an explicitly defined and comparable set of subtasks, (ii) by comparing in isolation different approaches taken at each step of scientific workflow comparison, reporting on an number of unexpected findings, (iii) by investigating how these can best be combined into aggregated measures, and (iv) by making available a gold standard of over 2000 similarity ratings contributed by 15 workflow experts on a corpus of almost 1500 workflows and re-implementations of all methods we evaluated.

Scientific workflow systems are numerous and equipped of provenance modules able to collect data produced and consumed during workflow runs to enhance reproducibility. An increasing number of approaches have been developed to help managing provenance information. Some of them are able to process data in a polynomial time but they require workflows to have series-parallel (SP) structures. Rewriting any workflow into an SP workflow is thus particularly important. In this paper, (i) we introduce the concept of provenance-equivalent rewriting process, (ii) we review existing graph transformations, (iii) we design the provenance-equivalent SPFlow algorithm, (iv) we evaluate our approach over a thousand of real workflows.

Ever since the advent of high-throughput biology (e.g., the Human Genome Project), integrating the large number of diverse biological data sets has been considered as one of the most important tasks for advancement in the biological sciences. Whereas the early days of research in this area were dominated by virtual integration systems (such as multi-/federated databases), the current predominantly used architecture uses materialization. Systems are built using ad-hoc techniques and a large amount of scripting. However, recent years have seen a shift in the understanding of what a “data integration system” actually should do, revitalizing research in this direction. In this tutorial, we review the past and current state of data integration for the Life Sciences and discuss recent trends in detail, which all pose challenges for the database community.

MOTIVATION: High-throughput technologies provide fundamental informations concerning thousands of genes. Many of the current research laboratories daily use one or more of these technologies and end-up with lists of genes. Assessing the originality of the results obtained includes being aware of the number of publications available concerning individual or multiple genes and accessing information about these publications. Faced with the exponential growth of publications avaliable and number of genes involved in a study, this task is becoming particularly difficult to achieve. RESULTS: We introduce GeneValorization, a web-based tool that gives a clear and handful overview of the bibliography available corresponding to the user input formed by (i) a gene list (expressed by gene names or ids from EntrezGene) and (ii) a context of study (expressed by keywords). From this input, GeneValorization provides a matrix containing the number of publications with co-occurrences of gene names and keywords. Graphics are automatically generated to assess the relative importance of genes within various contexts. Links to publications and other databases offering information on genes and keywords are also available. To illustrate how helpful GeneValorization is, we will consider the gene list of the OncotypeDX prognostic marker test. AVAILABILITY: http://bioguide-project.net/gv CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Scientific workflow management systems are increasingly providing the ability to manage and query the provenance of data products. However, the problem of differencing the provenance of two data products produced by executions of the same specification has not been adequately addressed. Although this problem is NP-hard for general workflow specifications, an analysis of real scientific (and business) workflows shows that their specifications can be captured as series-parallel graphs overlaid with well-nested forking and looping. For this natural restriction, we present efficient, polynomial-time algorithms for differencing executions of the same specification and thereby understanding the difference in the provenance of their data products. We then describe a prototype called PDiffView built around our differencing algorithm. Experimental results demonstrate the scalability of our approach using collected, real workflows and increasingly complex runs.

Scientific workflow systems are becoming increasingly important for managing in-silico experiments. Such experiments are typically specified as directed flow graphs, in which the nodes represent modules and edges represent data flow between the modules. Each execution (a.k.a. run) of an experiment may vary the parameters and data inputs to the modules in the specification; furthermore, alternative paths of the workflow may be followed. In this process, the scientist's goal is to identify parameter settings and approaches which lead to good final results. Comparing workflow executions of the same specification and understanding the difference between them is thus of paramount importance for understanding the provenance of final results [4].

Workflow systems have become increasingly popular for managing experiments where many bioinformatics tasks are chained together. Due to the large amount of data generated by these experiments and the need for reproducible results, provenance has become of paramount importance. Workflow systems are therefore starting to provide support for querying provenance. However, the amount of provenance information may be overwhelming, so there is a need for abstraction mechanisms to help users focus on the most relevant information. The technique we pursue is that of "user views". Since bioinformatics tasks may themselves be complex sub-workflows, a user view determines what level of sub-workflow the user can see, and thus what data and tasks are visible in provenance queries. In this paper, we formalize the notion of user views, demonstrate how they can be used in provenance queries, and give an algorithm for generating a user view based on which tasks are relevant for the user. We then describe our prototype and give performance results. Although presented in the context of scientific workflows, the technique applies to other data-oriented workflows.

International audience

Abstract ZOOM* UserViews presents a model of provenance for scientific workflows that is simple, generic, and yet sufficiently expressive to answer questions of data and step provenance that have been encountered in a large variety of scientific case studies. In addition, ZOOM builds on the concept of composite step‐classes—or sub‐workflows—which is present in many scientific workflow systems to develop a notion of user views. This paper discusses the design and implementation of ZOOM in the context of the queries posed by the provenance challenge, and shows how user views affect the level of granularity at which provenance information can be seen and reasoned about. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions. Copyright © 2007 John Wiley & Sons, Ltd.

UNLABELLED: Biologists are frequently faced with the problem of integrating information from multiple heterogeneous sources with their own experimental data. Given the large number of public sources, it is difficult to choose which sources to integrate without assistance. When doing this manually, biologists differ in their preferences concerning the sources to be queried as well as the strategies, i.e. the querying process they follow for navigating through the sources. In response to these findings, we have developed BioGuide to assist scientists search for relevant data within external sources while taking their preferences and strategies into account. In this article, we present BioGuideSRS, a user-friendly system which automatically retrieves instances of data by using BioGuide on top of the sequence retrieval system (SRS). BioGuideSRS is an Applet that can be run from its web page on any system with Java 5.0. AVAILABILITY: http://www.bioguide-project.net.