Fig. 5: Impact of bioinformatics factors on variant detection.
From: Augmenting precision medicine via targeted RNA-Seq detection of expressed mutations
a The number of variants detected in each individual library, including known positive, false positive, and uncharacterized calls, comparing single-library and merged-library approaches, with ROCR2 as an example. MR1 + 2 represents the library prepared by merging replicates 1 and 2 of ROCR2. b Comparison of the average number of calls detected by individual callers used in this study in AGLR2 and ROCR2 without and with controlling the FPR to 5 per million bases. c The average numbers of total and known positive calls detected by pipelines based on different aligners in each panel. The error bars represent the variability across four replicated libraries, calculated as the standard deviation of the data. d The number of variants (excluding FP calls) detected by different pipelines after controlling the FPR to 5 per million bases. An impressive number of variants were found to be aligner-specific or shared by two aligners across all panels.
