Data Warehousing with TargetMine for Omics Data Analysis

Yi-An Chen; Lokesh P Tripathi; Kenji Mizuguchi

doi:10.1007/978-1-4939-9442-7_3

Data Warehousing with TargetMine for Omics Data Analysis

Methods Mol Biol. 2019:1986:35-64. doi: 10.1007/978-1-4939-9442-7_3.

Authors

Yi-An Chen¹, Lokesh P Tripathi², Kenji Mizuguchi³

Affiliations

¹ Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan.
² Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan. lokesh@nibiohn.go.jp.
³ Laboratory of Bioinformatics, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Osaka, Japan. kenji@nibiohn.go.jp.

PMID: 31115884
DOI: 10.1007/978-1-4939-9442-7_3

Abstract

Most biological processes including diseases are multifactorial and determined by a complex interplay of various genetic and environmental factors. This chapter aims to provide a user guide to data querying, analysis, and visualization with TargetMine and the associated auxiliary toolkit. We have also discussed some of the commonly used data queries for the researchers who are interested in gene set analysis within a data warehouse framework. Overall, TargetMine provides a convenient web browser-based interface that enables the discovery of new hypotheses interactively, by performing analysis of omics data using complicated searches without any scripting and programming efforts on the part of the user and also by providing the results in an easy-to-comprehend output format.

Keywords: Data integration; Data mining; Data warehouse; Drug discovery; Gene prioritization; Knowledge discovery; Multi-omics data analysis.

MeSH terms

Data Analysis*
Data Mining*
Data Warehousing*
Gene Regulatory Networks
Genomics / methods*
Humans
Molecular Sequence Annotation
Software*