RNA variant identification discrepancy among splice-aware alignment algorithms.

摘要: Next-generation sequencing (NGS) techniques have been generating various molecular maps, including transcriptomes via RNA-seq. Although the primary purpose of RNA-seq is to quantify expression level known genes, RNA variants are also identifiable. However, care must be taken account for RNA’s dynamic nature. In this study, we evaluated following popular splice-aware alignment algorithms in context variant-calling analysis: HISAT2, STAR, STAR (two-pass mode), Subread, and Subjunc. For this, performed with ten pieces invasive ductal carcinoma from breast tissue three adjacent normal a single patient. These data were used evaluate performance aligners. Surprisingly, number common potential editing sites (pRESs) identified by all was less than 2% total. The main cause difference mapped reads on splice junctions. addition, quality significantly affected outcome. Therefore, researchers consider these experimental bioinformatic features during variant analysis. Further investigations pRESs discovered that BDH1, CCDC137, TBC1D10A transcripts contained non-synonymous unique cancer compared tissue; thus, further clinical validation required.