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A compendium of monocyte transcriptome datasets to foster biomedical knowledge discovery.
Rinchai, Darawan; Boughorbel, Sabri; Presnell, Scott; Quinn, Charlie; Chaussabel, Damien.
Afiliação
  • Rinchai D; Systems Biology Department, Sidra Medical and Research Center, Doha, Qatar.
  • Boughorbel S; Biomedical informatics, Sidra Medical and Research Center, Doha, Qatar.
  • Presnell S; Benaroya Research Institute at Virginia Mason, Seattle, USA.
  • Quinn C; Benaroya Research Institute at Virginia Mason, Seattle, USA.
  • Chaussabel D; Systems Biology Department, Sidra Medical and Research Center, Doha, Qatar.
F1000Res ; 5: 291, 2016.
Article em En | MEDLINE | ID: mdl-27158451
ABSTRACT
Systems-scale profiling approaches have become widely used in translational research settings. The resulting accumulation of large-scale datasets in public repositories represents a critical opportunity to promote insight and foster knowledge discovery. However, resources that can serve as an interface between biomedical researchers and such vast and heterogeneous dataset collections are needed in order to fulfill this potential. Recently, we have developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). This tool can be used to overlay deep molecular phenotyping data with rich contextual information about analytes, samples and studies along with ancillary clinical or immunological profiling data. In this note, we describe a curated compendium of 93 public datasets generated in the context of human monocyte immunological studies, representing a total of 4,516 transcriptome profiles. Datasets were uploaded to an instance of GXB along with study description and sample annotations. Study samples were arranged in different groups. Ranked gene lists were generated based on relevant group comparisons. This resource is publicly available online at http//monocyte.gxbsidra.org/dm3/landing.gsp.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2016 Tipo de documento: Article