Abstract
High-throughput cell type-specific multi-omic analyses have advanced our understanding of inner ear biology in an unprecedented way. The full benefit of these data, however, is reached from their re-use. Successful re-use of data requires identifying the natural users and ensuring proper data democratization and federation for their seamless and meaningful access. Here we discuss universal challenges in access and re-use of multi-omic data, possible solutions, and introduce the gEAR (the gene Expression Analysis Resource, umgear.org)—a tool for multi-omic data visualization, sharing and access for the ear field.
Highlights
Omics data generation and analysis has undergone rapid expansion since the publication of the human and mouse genomes barely two decades ago (Craig Venter et al 2001; Waterston et al 2002)
Advancements in the last few years enable many of these interrogations to be performed at the resolution of single cells allowing us to understand the spatial and temporal dynamics at a very high resolution (Longo et al 2021)
These advances have been widely adopted in the ear field with a growing number of datasets generated and published on an annual basis
Summary
Omics data generation and analysis has undergone rapid expansion since the publication of the human and mouse genomes barely two decades ago (Craig Venter et al 2001; Waterston et al 2002). While the manuscripts highlight key findings, and may offer pertinent gene lists as attached tables, by convention, all the data, raw as well as processed, are deposited in repositories, such as the NCBI’s Sequence Read Archive (SRA) (Leinonen et al 2011b) and EMBL–EBI’s European Nucleotide Archive (ENA) (Leinonen et al 2011a) for raw sequence data, and NCBI’s Gene Expression Omnibus (GEO) (Clough and Barrett 2016) and EMBL–EBI’s ArrayExpress (Athar et al 2019), European Variant Archive (EVA; https://www.ebi.ac.uk/eva/) for gene expression and variant data It is the availability and subsequent reuse of these data by other users for new discoveries that increases their value. The adoption and adherence to such principles is critical as the volume and complexity of the data continue to increase, and their access relies on standardized computational approaches
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