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HMM-ASE: A Hidden Markov algorithm for Ascertaining cSNP Genotypes from RNA Sequence Data in Presence of Allelic Imbalance
RNA-seq is a revolutionizing technology for transcriptone analysis, which is being increasingly used for nucleotide-centric inference. Allelic specific expression provides promising information on relating gene expression with phenotypic variation. The commonly used ASE testing requires a prior ascertainment of the cSNP genotypes for all individuals. In realizing these needs, we propose a hidden Markov method (HMM-ASE) to call SNPs from RNA sequence data. The proposed method can accommodate ASE in the RNA data. Simulation and real data applications results demonstrate that our proposed HMM-ASE has an improved accuracy and sensitivity in SNP calling. Moreover, HMM-ASE is advanced in calling cSNP from low-coverage RNA-seq data comparing to some existing methods.
Keywords: Hidden Markov model, RNA-seq, SNPs