Statistical inference of differential RNA-editing sites from RNA-sequencing data by hierarchical modeling | Zendy

Stephen Tran | Zendy; Qing Zhou | Zendy; Xinshu Xiao | Zendy

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Statistical inference of differential RNA-editing sites from RNA-sequencing data by hierarchical modeling

Author(s) -

Stephen Tran,

Qing Zhou,

Xinshu Xiao

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa066

Subject(s) - computer science , false positive paradox , rna editing , false positives and false negatives , rna , negative binomial distribution , identification (biology) , inference , nominal level , data mining , artificial intelligence , statistics , biology , poisson distribution , genetics , mathematics , confidence interval , botany , gene

RNA-sequencing (RNA-seq) enables global identification of RNA-editing sites in biological systems and disease. A salient step in many studies is to identify editing sites that statistically associate with treatment (e.g. case versus control) or covary with biological factors, such as age. However, RNA-seq has technical features that incumbent tests (e.g. t-test and linear regression) do not consider, which can lead to false positives and false negatives.

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