Incidences of problematic cell lines are lower in papers that use RRIDs to identify cell lines

• Published in: eLife Vol. 8
• Main Authors: Babic, Zeljana, Capes-Davis, Amanda, Martone, Maryann E, Bairoch, Amos, Ozyurt, I Burak, Gillespie, Thomas H, Bandrowski, Anita E
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 29-01-2019; eLife Sciences Publications, Ltd
• Subjects: Algorithms; authentication; Bibliometrics; Bioinformatics; Biomedical Research - standards; Cancer; Cell Line; Cell Line Authentication - statistics & numerical data; Cell lines; Computational and Systems Biology; Data Mining - methods; Datasets; Humans; Hypotheses; Laboratories; Medical research; Meta-Research; Neurosciences; Open access; Periodicals as Topic; PubMed; Reproducibility; Researchers; rigor; Software; text mining; Geneva Switzerland; California; United Kingdom > UK; United States > US; Switzerland; computational biology; systems biology; rigor; software; reproducibility; text mining; none; cell line; authentication
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.41676

The use of misidentified and contaminated cell lines continues to be a problem in biomedical research. Research Resource Identifiers (RRIDs) should reduce the prevalence of misidentified and contaminated cell lines in the literature by alerting researchers to cell lines that are on the list of problematic cell lines, which is maintained by the International Cell Line Authentication Committee (ICLAC) and the Cellosaurus database. To test this assertion, we text-mined the methods sections of about two million papers in PubMed Central, identifying 305,161 unique cell-line names in 150,459 articles. We estimate that 8.6% of these cell lines were on the list of problematic cell lines, whereas only 3.3% of the cell lines in the 634 papers that included RRIDs were on the problematic list. This suggests that the use of RRIDs is associated with a lower reported use of problematic cell lines.