Liguo Wang, Jinfu Nie, Hugues Sicotte, Ying Li, Jeanette E Eckel-Passow, Surendra Dasari, Peter T Vedell, Poulami Barman, Liewei Wang, Richard Weinshiboum, Jin Jen, Haojie Huang, Manish Kohli, Jean-Pierre A Kocher
Author Information
Liguo Wang: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. wang.liguo@mayo.edu.
Jinfu Nie: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. nie.jinfujeff@mayo.edu.
Hugues Sicotte: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Sicotte.Hugues@mayo.edu.
Ying Li: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Li.Ying@mayo.edu.
Jeanette E Eckel-Passow: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. eckelpassow.jeanette@mayo.edu.
Surendra Dasari: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Dasari.Surendra@mayo.edu.
Peter T Vedell: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Vedell.Peter@mayo.edu.
Poulami Barman: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. Barman.Poulami@mayo.edu.
Liewei Wang: Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA. Wang.Liewei@mayo.edu.
Richard Weinshiboum: Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA. Weinshilboum.Richard@mayo.edu.
Jin Jen: Department of laboratory medicine and pathology, Mayo Clinic, Rochester, MN, 55905, USA. Jen.Jin@mayo.edu.
Haojie Huang: Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA. Huang.Haojie@mayo.edu.
Manish Kohli: Department of Oncology, Mayo Clinic, Rochester, MN, 55905, USA. kohli.manish@mayo.edu.
Jean-Pierre A Kocher: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA. kocher.jeanpierre@mayo.edu.
BACKGROUND: Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substantially degraded. RNA degradation distorts the RNA-seq read coverage in a gene-specific manner, and has profound influences on whole-genome gene expression profiling. RESULT: We developed the transcript integrity number (TIN) to measure RNA degradation. When applied to 3 independent RNA-seq datasets, we demonstrated TIN is a reliable and sensitive measure of the RNA degradation at both transcript and sample level. Through comparing 10 prostate cancer clinical samples with lower RNA integrity to 10 samples with higher RNA quality, we demonstrated that calibrating gene expression counts with TIN scores could effectively neutralize RNA degradation effects by reducing false positives and recovering biologically meaningful pathways. When further evaluating the performance of TIN correction using spike-in transcripts in RNA-seq data generated from the Sequencing Quality Control consortium, we found TIN adjustment had better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91, accuracy = 0.90), as compared to gene expression analysis results without TIN correction (sensitivity = 0.98, specificity = 0.50, accuracy = 0.86). CONCLUSION: TIN is a reliable measurement of RNA integrity and a valuable approach used to neutralize in vitro RNA degradation effect and improve differential gene expression analysis.
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