Performing statistical analyses on quantitative data in Taverna workflows: An example using R and maxdBrowse to identify differentially-expressed genes from microarray data

Li, Peter; Castrillo, Juan I.; Velarde, Giles; Wassink, Ingo; Soiland‐Reyes, Stian; Owen, Stuart; Withers, David; Oinn, Tom; Pocock, Matthew; Goble, Carole; Oliver, Stephen G.; Kell, Douglas B.

doi:10.1186/1471-2105-9-334

Cited by 42 publications

(33 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2: On the left, (1) a workflow for the identification of differential genes generated as an image using Taverna. [17] On the right, (2) Same workflow represented as an interactive graph using web technologies in a mobile device.…”

Section: Discussionmentioning

confidence: 99%

Workflows and Service Discovery: A Mobile Device Approach

Holthausen

Díaz-del-Pino

Pérez-Wohlfeil

et al. 2018

Bioinformatics and Biomedical Engineering

View full text Add to dashboard Cite

Abstract. Bioinformatics has moved from command-line standalone programs to web-service based environments. Such trend has resulted in an enormous amount of online resources which can be hard to find and identify, let alone execute and exploit. Furthermore, these resources are aimed -in general-to solve specific tasks. Usually, this tasks need to be combined in order to achieve the desired results. In this line, finding the appropriate set of tools to build up a workflow to solve a problem with the services available in a repository is itself a complex exercise. Issues such as services discovering, composition and representation appear. On the technological side, mobile devices have experienced an incredible growth in the number of users and technical capabilities. Starting from this reality, in the present paper, we propose a solution for service discovering and workflow generation while distinct approaches of representing workflows in a mobile environment are reviewed and discussed. As a proof of concept, a specific use case has been developed: we have embedded an expanded version of our Magallanes search engine into mORCA, our mobile client for bioinformatics. Such composition delivers a powerful and ubiquitous solution that provides the user with a handy tool for not only generate and represent workflows, but also services, data types, operations and service types discovery.

show abstract

Section: Discussionmentioning

confidence: 99%

Workflows and Service Discovery: A Mobile Device Approach

Holthausen

Díaz-del-Pino

Pérez-Wohlfeil

et al. 2018

Bioinformatics and Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…Some of these data workflow systems are presented in [18,12,19]. These systems have been successfully used in a variety of data intensive scenarios like analyzing data from the Southern California Earthquake Center [20], data from biological domains like post genomic research [21], analysis of proteins and peptides from tandem mass spectrometry data [22], cancer research [23], meteorological phenomena [24] or used in the German grid platform [25]. In these scenarios, the systems accessed and processed petabytes of data, and we are convinced that the approach they use is the most suitable for managing the large amounts of data present in the LOD cloud.…”

Section: Implementation Of Sparql-dqp and Welldesigned Patterns Optimmentioning

confidence: 99%

Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Buil-Aranda

Arenas²,

Corcho

et al. 2013

Journal of Web Semantics

View full text Add to dashboard Cite

Given the sustained growth that we are experiencing in the number of SPARQL endpoints available, the need to be able to send federated SPARQL queries across these has also grown. To address this use case, the W3C SPARQL working group is defining a federation extension for SPARQL 1.1 which allows for combining graph patterns that can be evaluated over several endpoints within a single query. In this paper, we describe the syntax of that extension and formalize its semantics. Additionally, we describe how a query evaluation system can be implemented for that federation extension, describing some static optimization techniques and reusing a query engine used for data-intensive science, so as to deal with large amounts of intermediate and final results. Finally we carry out a series of experiments that show that our optimizations speed up the federated query evaluation process. ing complex queries by combining data from different, sometimes heterogeneous and often physically distributed datasets. SPARQL endpoints are RESTful services that accept queries over HTTP written in the SPARQL query language [1,2] adhering to the SPARQL protocol [3], as defined by the respective W3C recommendation documents. However, the current SPARQL recommendation has an important limitation in terms of defining and executing queries that span across distributed datasets, since it hides the physical distribution of data across endpoints, and has normally been used for querying isolated endpoints. Hence users willing to federate queries across a number of SPARQL endpoints have been forced to create ad-hoc extensions of the query language and protocol, to include additional information about data sources in the configuration of their SPARQL endpoint servers [4,5,6] or to devise engineering solutions where data from remote endpoints is copied into the endpoint being queried. Given the need to address these types of queries, the SPARQL working group has proposed a query federation extension for the upcoming SPARQL 1.1 language [7] which is now under discussion in order to generate a new W3C recommendation in the coming months.

show abstract

“…As of Taverna version 2.3.0 these services, as well as a variety of other life science Web services, are all grouped in the Service Catalog, a module that lists all the services available in the BioCatalogue project (31). In addition, Taverna supports the local or remote execution of RShell code (32,33), providing access to a variety of statistical functions, both for general purpose and for bioinformatics, such as those included in the Bioconductor package (34). The incorporation of R also benefits performance, as there is a framework for parallel execution of algorithms in R (35).…”

Section: Theoretical Backgroundmentioning

confidence: 99%