Document Type

Article

Publication Date

1-1-2019

Keywords

JMG

JAX Source

Database (Oxford) 2019 Jan 1; 2019:baz007

PMID

30715275

DOI

https://doi.org/10.1093/database/baz007

Abstract

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.

Comments

Open access under the terms of the Creative Commons Attribution License

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