title: Data integration and reproducibility for high-throughput transcriptomics.
abstract: The rapid advances in high-throughput transcriptomics allow individual investigators to rapidly and comprehensively interrogate the transcriptome. This phenomenon has placed large volumes of gene expression data in public repositories presenting opportunities for secondary analysis, discovery, and in silico modeling. We focus here on guidelines for best practices for transcriptomics data integration and considerations for reproducibility. In addition, we discuss some considerations for multi-omic and cross-species comparisons.
authors.affiliation: Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA. Electronic address: mooneymi@ohsu.edu.
authors.forename: Michael
authors.initials: M
authors.lastname: Mooney
authors.affiliation: Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
authors.forename: Shannon
authors.initials: S
authors.lastname: McWeeney
mesh_terms: D000818:Animals; D001330:Electronic Data Processing; D015870:Gene Expression / Q000502:physiology*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D046228:Microarray Analysis; D012313:RNA / Q000235:genetics / Q000378:metabolism; D015203:Reproducibility of Results; D059467:Transcriptome / Q000502:physiology*
chemical_list: D012313:RNA
keywords: Cross-platform; Data integration; High-throughput; Microarrays; Next-generation sequencing; Reproducibility; Transcriptomics