Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data.
Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approache...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 9 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/14/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273938&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273938 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/14/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273938 109273938 109273938 10.1155/2015/958302 109273938 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data. aug: au: Friedrich, Andreas Kenar, Erhan Kohlbacher, Oliver Nahnsen, Sven affil: Applied Bioinformatics, Center for Bioinformatics, Quantitative Biology Center (QBiC) and Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany sug: subj: World Wide Web Data Management Bioinformatics Funding Source Human Research, Medical Study Design ab: Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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