Data integration and reproducibility for high-throughput transcriptomics

Michael Mooney; Shannon McWeeney

doi:10.1016/B978-0-12-801105-8.00003-5

Data integration and reproducibility for high-throughput transcriptomics

Int Rev Neurobiol. 2014;116:55-71. doi: 10.1016/B978-0-12-801105-8.00003-5.

Authors

Michael Mooney¹, Shannon McWeeney²

Affiliations

¹ Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA. Electronic address: mooneymi@ohsu.edu.
² Division of Bioinformatics & Computational Biology, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.

PMID: 25172471
DOI: 10.1016/B978-0-12-801105-8.00003-5

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.

Keywords: Cross-platform; Data integration; High-throughput; Microarrays; Next-generation sequencing; Reproducibility; Transcriptomics.

Publication types

Review

MeSH terms

Animals
Electronic Data Processing*
Gene Expression / physiology*
High-Throughput Nucleotide Sequencing*
Humans
Microarray Analysis
RNA / genetics
RNA / metabolism
Reproducibility of Results
Transcriptome / physiology*

Substances

RNA