We validated substrates from different subclasses, which were purified with diverse methods to exclude artifacts caused by tags or treatments.
To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing.
We validated 106 SNPs from ten randomly selected genes using the Sanger method, and the accuracy of SNP calling reached 97.3%, suggesting that potential miscalling of SNPs in this study was minimal.
We validated our findings from this cohort using array data generated from a subset of our IHC cohort, for which we could define pathway status using β-catenin IHC.
We validated our hits from the primary screen using ON TARGETplus siRNA pools.
We validated BMI calculated from self-reported weight and height in relation to BMI reported in health plan clinical databases (Table 7).
To determine genotype accuracy, we validated 63 genotypes from a subset of 58 SNPs overlapping among datasets by classical Sanger sequencing.
Using a subset of the data, we validated the results from the SAS/GIS procedure against a commercially available program http://www.geocode.com using the Haversine formula, and found there to be high agreement between the two algorithms [ 8].
We validated the results from the Beaumont method by obtaining another estimate of the time of population change with another method (the hierarchical model) developed by Storz and Beaumont [ 47].
We validated the results from both high-throughput experiments by performing growth experiments in a BioScreen C instrument (Thermo Electron, Helsinki, Finland), which generates robust growth curves under more strictly controlled conditions.
