A content-based literature recommendation system for datasets to improve data reusability – A case study on Gene Expression Omnibus (GEO) datasets

The reuse of public datasets in the life sciences: potential risks and rewards

“…There are already numerous public repositories for genomic and gene-expression data, such as the Sequence Read Archive (SRA)/European Nucleotide Archive (ENA) and Gene Expression Omnibus (GEO), respectively. Recently, GEO has been used in a case study to improve dataset reusability with a literature recommendation system (Patra et al, 2020) and is highly recommended over other databases for the submission of RNA-Seq datasets (Bhandary et al, 2018). In medical research, information loss stems from large amounts of gathered data remaining inaccessible for reuse by a wider audience (sometimes even the authors) after the initial publication (Wade, 2014).…”

“…In metagenomics, where datasets tend to be in the gigabyte range, appropriate archiving of workflow intermediates for reuse can decrease the costs of re-analysis (Ten Hoopen et al, 2017). Additionally, many datasets deposited in sequence repositories like GEO were collected at enormous effort and used only once and so reusing them greatly increases their utility (Patra et al, 2020). The labour efficiency of reuse is also illustrated by the famous eGFP browser (Winter et al, 2007) which presents the content of RNA-Seq datasets to biologists in a simple way.…”

“…Precise documentation of the workflow is crucial to clearly indicate limitations of the generated data product (Soranno et al, 2015). Despite this, the peer-review process rarely reaches the datasets and their descriptions (Patra et al, 2020). There is also a trade-off between the collection of detailed metadata during submission and high submission numbers.…”

“…Additionally, methods for reconstructing existing databases are already being investigated. This can be done by curating existing metadata or extracting more of it through natural language processing techniques (Patra et al, 2020) and through metadata predicting frameworks (Posch et al, 2016). Many sequence databases, such as ENA, are handling this elegantly and submitting users can provide a very basic set of metainformation or provide comprehensive details about their study.…”

“…There are also easy-to-follow instructions for the submission process (European Nucleotide Archive (ENA), 2020). Despite such incentives, many datasets still lack descriptions that would allow them to be re-sorted according to their metadata (Patra et al, 2020).…”