ArticleBMC genomics2019
CeL-ID: cell line identification using RNA-seq data.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Molecular and Cell Biological Characterization of Patient-Derived Head and Neck Squamous Carcinoma Cell Lines.Cancer science · 2026Article
- Cell line authentication using optical genome mapping.BMC genomics · 2025Article
- Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.BMC research notes · 2025Article
- RNA-seq-derived sequence variations are excellent features for cell line identification.Computational and structural biotechnology journal · 2025Article
- Comprehensive identification of onco-exaptation events in bladder cancer cell lines revealed L1PA2-SYT1 as a prognosis-relevant event.iScience · 2023Article
- GenoPipe: identifying the genotype of origin within (epi)genomic datasets.Nucleic acids research · 2023Article
- CCLHunter: An efficient toolkit for cancer cell line authentication.Computational and structural biotechnology journal · 2023Article
- CNpare: matching DNA copy number profiles.Bioinformatics (Oxford, England) · 2022Article
- Establishing Institutional Scores With the Rigor and Transparency Index: Large-scale Analysis of Scientific Reporting Quality.Journal of medical Internet research · 2022Article
- Multifocal Organoid Capturing of Colon Cancer Reveals Pervasive Intratumoral Heterogenous Drug Responses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Transposable element profiles reveal cell line identity and loss of heterozygosity in Drosophila cell culture.Genetics · 2021Article
- An introduction to new robust linear and monotonic correlation coefficients.BMC bioinformatics · 2021Article
- Transcriptomes of Major Proximal Tubule Cell Culture Models.Journal of the American Society of Nephrology : JASN · 2021Article
- Reversible promoter methylation determines fluctuating expression of acute phase proteins.eLife · 2020Article
- Approaching RNA-seq for Cell Line Identification.Bio-protocol · 2020Article
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics with bigger data and wider applications.BMC genomics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundCell lines form the cornerstone of cell-based experimentation studies into understanding the underlying mechanisms of normal and disease biology including cancer. However, it is commonly acknowledged that contamination of cell lines is a prevalent problem affecting biomedical science and available methods for cell line authentication suffer from limited access as well as being too daunting and time-consuming for many researchers. Therefore, a new and cost effective approach for authentication and quality control of cell lines is needed.
resultsWe have developed a new RNA-seq based approach named CeL-ID for cell line authentication. CeL-ID uses RNA-seq data to identify variants and compare with variant profiles of other cell lines. RNA-seq data for 934 CCLE cell lines downloaded from NCI GDC were used to generate cell line specific variant profiles and pair-wise correlations were calculated using frequencies and depth of coverage values of all the variants. Comparative analysis of variant profiles revealed that variant profiles differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and are highly correlated (ρ > 0.9). Our benchmarking studies revealed that CeL-ID method can identify a cell line with high accuracy and can be a valuable tool of cell line authentication in biomedical science. Finally, CeL-ID estimates the possible cross contamination using linear mixture model if no perfect match was detected.
conclusionsIn this study, we show the utility of an RNA-seq based approach for cell line authentication. Our comparative analysis of variant profiles derived from RNA-seq data revealed that variant profiles of each cell line are distinct and overall share low variant identity with other cell lines whereas identical or synonymous cell lines show significantly high variant identity and hence variant profiles can be used as a discriminatory/identifying feature in cell authentication model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.