Preprocessing of Public RNA-Sequencing Datasets to Facilitate Downstream Analyses of Human Diseases

Data

Brett E. Pickett¹  Kennedy T. L. Gifford¹  Benjamin R. Hinatsu¹  Ethan J. Beausoleil¹  Naomi Rapier-Sharman¹  John Krapohl¹  Curtis S. Hoffmann¹  Makayla Komer¹  Tiana M. Scott¹ 

[1] Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA;

关键词: transcriptomics;    RNA-sequencing;    autoimmune diseases;    cancer;    pathogens;    bacteria;   

DOI  :  10.3390/data6070075

来源: DOAJ

【 摘 要 】

Publicly available RNA-sequencing (RNA-seq) data are a rich resource for elucidating the mechanisms of human disease; however, preprocessing these data requires considerable bioinformatic expertise and computational infrastructure. Analyzing multiple datasets with a consistent computational workflow increases the accuracy of downstream meta-analyses. This collection of datasets represents the human intracellular transcriptional response to disorders and diseases such as acute lymphoblastic leukemia (ALL), B-cell lymphomas, chronic obstructive pulmonary disease (COPD), colorectal cancer, lupus erythematosus; as well as infection with pathogens including Borrelia burgdorferi, hantavirus, influenza A virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Streptococcus pneumoniae, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We calculated the statistically significant differentially expressed genes and Gene Ontology terms for all datasets. In addition, a subset of the datasets also includes results from splice variant analyses, intracellular signaling pathway enrichments as well as read mapping and quantification. All analyses were performed using well-established algorithms and are provided to facilitate future data mining activities, wet lab studies, and to accelerate collaboration and discovery.

【 授权许可】

Unknown