title: Study Design for Sequencing Studies.
abstract: Once a biochemical method has been devised to sample RNA or DNA of interest, sequencing can be used to identify the sampled molecules with high fidelity and low bias. High-throughput sequencing has therefore become the primary data acquisition method for many genomics studies and is being used more and more to address molecular biology questions. By applying principles of statistical experimental design, sequencing experiments can be made more sensitive to the effects under study as well as more biologically sound, hence more replicable.
authors.affiliation: Department of Biology, The Pennsylvania State University, USDA ARS, Tree Fruit Res Lab, 1104 N Western Ave, Wenatchee, WA, 98801, USA.
authors.forename: Loren A
authors.initials: LA
authors.lastname: Honaas
authors.affiliation: Department of Statistics and Huck Institutes of Life Sciences, The Pennsylvania State University, 312 Thomas Building, University Park, PA, 16802-2111, USA. nsa1@psu.edu.
authors.forename: Naomi S
authors.initials: NS
authors.lastname: Altman
authors.affiliation: Canada's Michael Smith Genome Sciences Centre, Vancouver, BC, Canada.
authors.forename: Martin
authors.initials: M
authors.lastname: Krzywinski
mesh_terms: D000818:Animals; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods / Q000592:standards; D006801:Humans; D012107:Research Design; D017421:Sequence Analysis / Q000379:methods / Q000592:standards; D017422:Sequence Analysis, DNA / Q000379:methods / Q000592:standards; D017423:Sequence Analysis, RNA / Q000379:methods / Q000592:standards
keywords: Block design; Multiplexing; Pilot study; Pooling; Precision; Randomization; Replication; Reproducible research; Sample size; Sequencing depth