The development of high-throughput technologies has changed the conduct of biological experiments in the last decade. From single gene studies, research has shifted to measuring gene signatures at the transcriptome level. The dramatic decrease in the financial expenses of next generation sequencing techniques has enabled their routine implementation. However, very often, economic constraints restrict the number of samples and sequence quality. Careful planning and design may overcome this limitation, and attain the maximum information from a given experiment.Among the factors that affect the quality and quantity of data resulting from next generation sequencing experiments are sample size and the number of replicates, sequence depth and coverage, randomization, and batches. Here, we discuss the design of high-throughput experiments, while focusing on RNA-sequencing experiments. We suggest critical rules of thumb, from biological, statistical, and bioinformatics points of view, aimed to obtain a successful experiment, beyond the economic constraints.
Keywords: Batch; Differentially expressed genes (DGEs); Experiment design; High-throughput (HT); Next generation sequencing (NGS); RNA-Seq; Sample size; Sample variability; Sequence coverage; Sequence depth.