Web Services are interfaces that describe a collection of operations that are network-accessible through standardized web protocols. When a required operation is not found, several services can be compounded to get a composite service that performs the desired task. To find this composite service a search process in a, generally, huge search space must be performed. The algorithm that composes the services must select the adequate atomic processes and, also, must choose the correct way to combine them using the different available control structures. In this paper a genetic programming algorithm for web services composition is presented. The algorithm has a context-free grammar to generate the valid structures of the composite services and, also, it includes a method to update the attributes of each node. Moreover, the proposal tries to minimize the number of services, and looks for compositions with the minimum execution path. A full experimental validation with four different repositories with up to 1,090 web services has been done, showing a great performance in all the tests as the algorithm finds a valid solution with a short execution path.

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.