Ten simple rules for providing effective bioinformatics research support

Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support. Author summary The article we wrote draws from our experience in core support facilities and highlights 10 best practices that individuals who apply information technology approaches to biological, medical, and health research should consider when providing support to individuals who generate data for this research in the lab. As interdisciplinary approaches are increasingly being utilized within the biological and medical sciences, effective collaboration and support between the aforementioned parties is crucial to promote the quality and integrity of research. These practices highlight the importance of quality control, comprehensive reporting, effective communication, and more in the production of quality data as well as the promotion of effective collaboration.