Identification of false discoveries in sequencing data
Sequencing techniques are fundamental in almost all genetic research, either by sequencing the entire genome or transcriptome, by high-throughput sequencing, or by sequencing a small segment of DNA using Sanger sequencing. The biggest challenge in all techniques is identifying noise and false discovery versus real biological changes. There are not many tools available that can be used for data exploration and the identification of false discoveries. Here, we developed an algorithm on RNA-seq data to track discrepancies between the data and the statistical model design that enables optimal data analysis. In addition, we developed a Sanger sequencing analyzer that quantifies and determines if a SNP is sequencing noise.
