Gene Retriever

An international collaboration produces the first data mining solution to retrieve all genes associated with a list of articles.

Investigators seeking novel biomarkers and clues to disease mechanisms have asked countless times: “What genes are referenced in the results of my PubMed search?” Gene Retriever, a collaboration between Acumenta Biotech, the builder of Literature Lab™, and Sidra Medicine, answers this critical question by creating gene lists from the results of any PubMed search.

Comprised of software written by the Advanced Application team in Sidra Medicine’s Biomedical Informatics department and the Literature Lab™ database and Gene Thesaurus™, Gene Retriever is an easy-to-use software application for Mac or Windows computers:

How Gene Retriever works

Gene Retriever processes a list of PubMed IDs that you submit, and produces an analysis of the genes mentioned in the title, text and MeSH tags of each record.

Results are ranked and presented in a spreadsheet that enables quick and comprehensive analysis. Hyperlinks are provided for instant review of the genes or PubMed IDs of interest.

How accurate is Gene Retriever?

Very accurate. At the core of the Literature Lab™ database is the Acumenta Biotech Gene Thesaurus™, a repository of gene, protein and pathway nomenclature gathered from the major genomic databases, and human-curated to produce searches combining high precision and high recall. The Gene Thesaurus™ is unique because it is constantly updated on the Literature™ Lab platform, and human curation resolves ambiguous terminology, alias redundancy and generic terms.

Up-to-date gene panels spur insights that speed discovery

and streamline translational research

The large number of factors involved in the pathogenesis of certain diseases makes identification of a targeted, manageable-sized list of genes involved in these interactions extremely difficult. Gene Retriever helps identify genes that are highly associated with a particular disease, allowing construction of gene panels that contain putative disease signatures. These signatures can be used as biological markers of disease to classify patients for personalized care.

An approach has been recently described in which putative novel genes associated with inflammatory bowel disease subtypes were identified using Literature Lab™ Gene Retriever, Literature Lab™ PLUS, and other Literature Lab™ tools: A literature-based approach for curating gene signatures in multifaceted diseases. Journal of Translational Medicine 18, 279 (2020). https://doi.org/10.1186/s12967-020-02408-7

Additional publications citing Gene Retriever:

Rinchai D, et al. A modular framework for the development of targeted Covid-19 blood transcript profiling panels. 2020. Journal of Translational Medicine https://doi.org/10.21203/rs.3.rs-33274/v1

Susie S.Y. Huang, Melanie Makhlouf, et al.  Differential regulation of the immune system in a brain-liver-fats organ network during short-term fasting  Molecular Metabolism https://doi.org/10.1016/j.molmet.2020.101038

How to analyze your PubMed search with Gene Retriever

1. Enter your search.

2. Click “Save” on upper-middle of the page.

3. Select “All Results” and “PMID” as shown, Click “Create file” Contact Acumenta Biotech if your search produces more than 10,000 abstracts.

4. Download the file. Copy it to your Gene Retriever Folder, click “Refresh” and select the new file.