Figure 1.
Schematic representation of the automated pipeline for ASE detection. Raw reads were aligned by STAR (RNA-seq) or bwa (exome sequencing [exome-seq]). SNVs were called with an ensemble of programs and annotated based on function, population frequency, and NGS statistics. This allowed the subsequent filtering of variants that were both real and informative. For every SNV, the variant allele frequency (VAF) at the DNA and RNA levels was computed, and SNV information was aggregated at the gene level. Finally, ASE was determined based on frequency of the minor allele (MAF) <0.35 and false discovery rate (FDR) <0.05 in a χ2 test.

Schematic representation of the automated pipeline for ASE detection. Raw reads were aligned by STAR (RNA-seq) or bwa (exome sequencing [exome-seq]). SNVs were called with an ensemble of programs and annotated based on function, population frequency, and NGS statistics. This allowed the subsequent filtering of variants that were both real and informative. For every SNV, the variant allele frequency (VAF) at the DNA and RNA levels was computed, and SNV information was aggregated at the gene level. Finally, ASE was determined based on frequency of the minor allele (MAF) <0.35 and false discovery rate (FDR) <0.05 in a χ2 test.

Close Modal

or Create an Account

Close Modal
Close Modal