Dataset Information

Identification of an active ID-like group of SINEs in the mouse.

ABSTRACT: The mouse genome consists of five known families of SINEs: B1, B2, B4/RSINE, ID, and MIR. Using RT-PCR we identified a germ-line transcript that demonstrates 92.7% sequence identity to ID (excluding primer sequence), yet a BLAST search identified numerous matches of 100% sequence identity. We analyzed four of these elements for their presence in orthologous genes in strains and subspecies of Mus musculus as well as other species of Mus using a PCR-based assay. All four analyzed elements were identified either only in M. musculus or exclusively in both M. musculus and M. domesticus, indicative of recent integrations. In conjunction with the identification of transcripts, we present an active ID-like group of elements that is not derived from the proposed BC1 master gene of ID elements. A BLAST of the rat genome indicated that these elements were not in the rat. Therefore, this family of SINEs has recently evolved, and since it has thus far been observed mainly in M. musculus, we refer to this family as MMIDL.

SUBMITTER: Kass DH

PROVIDER: S-EPMC2034284 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature

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Identification of an active ID-like group of SINEs in the mouse.

Kass David H DH Jamison Nicole N

Genomics 20070614 3

The mouse genome consists of five known families of SINEs: B1, B2, B4/RSINE, ID, and MIR. Using RT-PCR we identified a germ-line transcript that demonstrates 92.7% sequence identity to ID (excluding primer sequence), yet a BLAST search identified numerous matches of 100% sequence identity. We analyzed four of these elements for their presence in orthologous genes in strains and subspecies of Mus musculus as well as other species of Mus using a PCR-based assay. All four analyzed elements were ide ...[more]

PMID: 17572061

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Project description: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.

Dataset Information

Identification of an active ID-like group of SINEs in the mouse.

Publications

Identification of an active ID-like group of SINEs in the mouse.

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