Pipeline: Data Generation

Data generation for large-scale genomics research projects can be a resource-intensive activity. A carefully designed data generation model is essential during the planning phase of the project. An evaluation should be made on the types of data to be generated and the level of curation, the technological approach, and the individual(s) generating the data (single vs multiple institutions). Afterward, a data generation pipeline should be developed. In each step of the pipeline, quality control checks should be implemented to ensure high-quality data production. Proper tracking and management of data generation and, if required, submission to a central repository is also important in the pipeline design. For large-scale data production, a Laboratory Information Management System is the best tracking tool.