RNA-seq dataset of subcutaneous adipose tissue: Transcriptional differences between obesity and healthy women

In this data article, we present the dataset from the RNA-Seq analysis of subcutaneous adipose tissue collected from 5 healthy normal weight women (NW, age 37 ± 6.7 years, BMI 24.3 ± 0.9 kg/m2) and 5 obese women (OBF, age 41 ± 12.5 years, BMI 38.2 ± 4.6 kg/m2). Raw data obtained from Illumina NextSe...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Letizia Messa, Federica Rey, Cecilia Pandini, Bianca Barzaghini, Giancarlo Micheletto, Manuela Teresa Raimondi, Simona Bertoli, Cristina Cereda, Gianvincenzo Zuccotti, Raffaella Cancello, Stephana Carelli
Formato: article
Lenguaje:EN
Publicado: Elsevier 2021
Materias:
UMI
Acceso en línea:https://doaj.org/article/540cbf1bbd654eb084fb33e9dcb2610e
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:In this data article, we present the dataset from the RNA-Seq analysis of subcutaneous adipose tissue collected from 5 healthy normal weight women (NW, age 37 ± 6.7 years, BMI 24.3 ± 0.9 kg/m2) and 5 obese women (OBF, age 41 ± 12.5 years, BMI 38.2 ± 4.6 kg/m2). Raw data obtained from Illumina NextSeq 500 sequencer were processed through BlueBee® Genomics Platform while differential expression analysis was performed with the DESeq2 R package and deposited in the GEO public repository with GSE166047 as accession number. Specifically, 20 samples divided between NW (control), OBF (obese women), OBM (obese male) and OBT2D (obese women with diabetes) are deposited in the GSE166047. We hereby describe only 10 samples (5 healthy normal weight women reported as NW and 5 obese women reported as OBF) because we refer to the data published in the article “Transcriptional characterization of Subcutaneous Adipose Tissue in obesity affected women highlights metabolic dysfunction and implications for lncRNAs” (DOI: 10.1016/j.ygeno.2021.09.014). Pathways analyses were performed on g:Profiler, Enrichr, ClueGO and GSEA to gain biological insights on gene expression. Raw data reported in GEO database along with detailed methods description reported in this data article could be reused for comparisons with other datasets on the topic to obtain transcriptional differences in a wider co-hort. Moreover, detailed pathways analysis along with cross-referenced data with other datasets will allow to identify novel dysregulated pathways and genes responsible for this regulation. The biological interpretation of this dataset, along with related in vitro experiments, is reported by Rey et al., in Genomics (DOI: 10.1016/j.ygeno.2021.09.014).