Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

Abstract

Thanks to recent advance of next generation sequencing techniques, RNA-seq enabled to have an unprecedented opportunity to identify transcript variants with isoform diversity and allelic imbalance (Anders et al., 2012) by different transcriptional rates. To date, it is well known that those features might be associated with the aberrant patterns of disease complexity such as tissue (Anders and Huber, 2010;Anders et al., 2012;Nariai et al., 2014) specific differential expression at isoform levels or tissue specific allelic imbalance in mal-functionality of disease processes, etc. Nevertheless, the knowledge of post-transcriptional modification and AI in transcriptomic and genomic areas has been little known in the traditional platforms due to the limitation of technology and insufficient resolution. We here stress the potential of isoform variability and allelic specific expression that are relevant to the abnormality of disease mechanisms in transcriptional genetic regulatory networks. In addition, we systematically review how robust Bayesian approaches in RNA-seq have been developed and utilized in this regard in the field.