High-throughput quantitative DNA sequencing enables the parallel phenotyping of 
pools of thousands of mutants. However, the appropriate analytical methods and 
experimental design that maximize the efficiency of these methods while 
maintaining statistical power are currently unknown. Here, we have used Bar-seq 
analysis of the Saccharomyces cerevisiae yeast deletion library to 
systematically test the effect of experimental design parameters and sequence 
read depth on experimental results. We present computational methods that 
efficiently and accurately estimate effect sizes and their statistical 
significance by adapting existing methods for RNA-seq analysis. Using simulated 
variation of experimental designs, we found that biological replicates are 
critical for statistical analysis of Bar-seq data, whereas technical replicates 
are of less value. By subsampling sequence reads, we found that when using 
four-fold biological replication, 6 million reads per condition achieved 96% 
power to detect a two-fold change (or more) at a 5% false discovery rate. Our 
guidelines for experimental design and computational analysis enables the study 
of the yeast deletion collection in up to 30 different conditions in a single 
sequencing lane. These findings are relevant to a variety of pooled genetic 
screening methods that use high-throughput quantitative DNA sequencing, 
including Tn-seq.