tranSMART

Bierkens, Mariska; Linden, Wim van der; Bochove, Kees van; Weistra, Ward; Fijneman, Remond J.A.; Azevedo, Rita; Boiten, Jan-Willem; Beliën, Jeroen; Meijer, Gerrit A.

doi:10.1186/2043-9113-5-s1-s9

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…127 The first iteration of tranSMART was developed by Johnson & Johnson (JnJ) in 2010 to serve as an internal research data warehouse, and Janssen R&D, Inc., a JnJ company, made it open source in 2012. 128,129 The Allotrope Foundation is another non-PPP collaborative platform that brings together players from industry and aims at developing ontologies, data models, and a universal format for storing experimental data. 130 Two additional non-PPP foundations relevant to the pharmaceutical sector are Lhasa Limited which focuses on data sharing mainly related to safety, and the Cambridge Crystallographic Data Centre (CCDC), which specializes in crystallographic data and related software development.…”

Section: Data and Intellectual Property Sharingmentioning

confidence: 99%

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development

et al. 2021

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Section: Data and Intellectual Property Sharingmentioning

confidence: 99%

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development

et al. 2021

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“…The platform features data interoperability connectors, including clinical information collection, 76 imaging data, 77 visual analytics, 78 or bioinformatics workflow management. 79 Finally, tranSMART features built-in data mining and analysis applications based on open-source systems, such as i2b2 and GenePattern, 28 and provides plugins to external tools, such as Dalliance Genome Browser, 80 or APIs for statistical packages, such as R. 81…”

Section: Approachmentioning

confidence: 99%

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Satagopam

Eifes

et al. 2016

Big Data

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Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services—tranSMART, a Galaxy Server, and a MINERVA platform—are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

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“…These data are often accessible through public resources, such as CCLE [ 1 , 2 ], TCGA Research Network [ 3 ], ExpO [ 4 ], and ICGC [ 5 ], to name a few. As a consequence, a number of initiatives, often at global scale, have taken advantage of this unprecedented opportunity, such as the Cancer Therapeutics Response Portal (CTRP) [ 6 – 8 ], linking publicly available cancer cell line features to small-molecule sensitivity for the discovery of patient-matched cancer therapeutics, or the i2b2 (Informatics for Integrating Biology and the Bedside)-tranSMART Foundation, a platform and a community aimed at integrating clinical and translational research data, providing “open-source, open-data” resources for precision medicine [ 9 , 10 ]. In translational research, treatment sensitivity biomarkers are key to decision-making, for the identification and definition of patient populations susceptible to therapy benefits.…”

Section: Introductionmentioning

confidence: 99%

PATRI, a Genomics Data Integration Tool for Biomarker Discovery

Ukmar

Melloni

Raddrizzani

et al. 2018

BioMed Research International

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The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.

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tranSMART

Cited by 6 publications

References 1 publication

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

PATRI, a Genomics Data Integration Tool for Biomarker Discovery

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