Application of RNA-seq for single nucleotide variation identification patients with hereditary diseases: analysis of trasncriptomic data and perspective use

Background: Various DNA sequencing methods, including particular genes sequencing, whole genome sequencing or exome sequencing, are presently used to identify single nucleotide variations (SNVs) associated with inherited diseases. Most of the known mutations and polymorphisms associated with hereditary diseases are localized in the coding regions and cause an amino-acid substitution, appearance of new stop codon or open reading frame shifts. These types of mutations can be identified using transcriptome sequencing. Full transcriptome sequencing is widely used to study diseases. The results of such investigations are usually available in public databases. This data could be tapped for identification of novel mutations and disease-associated variants. In this study, we are analyzing the potential use of full transcriptome sequencing for SNV identification, using RNA-seq of myocardium of patients with HCMP as an example. The goal of the current study is development of pipeline for SNV identification from RNA-seq data and application of this pipeline to identification of putative pathogenically significant SNV. Results: The algorithm of identification of SNV based on transcriptomic sequencing data has been developed. The algorithm was evaluated and the optimal quality threshold was determined based on allelic discrimination for the rs397516037 mutation (MYBPC3 c.3697C>T) among patients. A quality threshold of 75 was selected, at which false-positive and false-negative results for the rs397516037 variant were excluded. 43587 total SNVs with 75 and higher quality was identified in 48 transcriptomes of HCMP patients. In order to further verify the results obtained using the algorithm, 8 SNVs were selected in the MYBPC3 and MYH7 genes. Each of these 8 SNVs was confirmed by Sanger sequencing. Conclusions: The developed method has proven its effectiveness in identifying putative pathogenic variants in RNA-seq data. Its use will be especially useful in the context of large volumes of accumulated transcriptomic data that have not been analyzed in this way before. However, it is necessary to consider the limitations and scope of this method. The study was conducted on the basis of a disease and mode of inheritance are well suited for this method, but in other cases its applicability should be assessed individually.