Expanding and Remixing the Metadata Landscape

Abstract

Genomic data sharing accelerates research. Data are most valuable when they are accompanied by detailed metadata. To date, metadata are often human-annotated descriptions of samples and their handling. We discuss how machine learning-derived elements complement such descriptions to enhance the research ecosystem around genomic data.

Keywords: data sharing; genomics; machine learning; metadata.

Figure 1.. A Schematic of Sample Characteristics and How They Relate to Metadata for a Hypothetical Piece of Tumor Tissue.

Metadata can include elements curated from records that describe a patient or those related to the process of sample collection and quantification. They can also include elements estimated from sequencing data, which may include elements like biological age that are not directly observable or extractable from existing records. Abbreviation: PC, principal components.