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2017 ; 19
(1
): 18-27
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Symptom Science: Repurposing Existing Omics Data
#MMPMID27613438
Osier ND
; Imes CC
; Khalil H
; Zelazny J
; Johansson AE
; Conley YP
Biol Res Nurs
2017[Jan]; 19
(1
): 18-27
PMID27613438
show ga
Omics approaches, including genomics, transcriptomics, proteomics, epigenomics,
microbiomics, and metabolomics, generate large data sets. Once they have been
used to address initial study aims, these large data sets are extremely valuable
to the greater research community for ancillary investigations. Repurposing
available omics data sets provides data to address research questions, generate
and test hypotheses, replicate findings, and conduct mega-analyses. Many
well-characterized, longitudinal, epidemiological studies collected extensive
phenotype data related to symptom occurrence and severity. While the main
phenotype of interest for many of these studies was often not symptom related,
these data were collected to better understand the primary phenotype of interest.
A search for symptom data (i.e., cognitive impairment, fatigue, gastrointestinal
distress/nausea, sleep, and pain) in the database of genotypes and phenotypes
(dbGaP) revealed many studies that collected symptom and omics data. There is
thus a real possibility for nurse scientists to be able to look at symptom data
over time from thousands of individuals and use omics data to identify key
biological underpinnings that account for the development and severity of
symptoms without recruiting participants or generating any new data. The purpose
of this article is to introduce the reader to resources that provide omics data
to the research community for repurposing, provide guidance on using these
databases, and encourage the use of these data to move symptom science forward.
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