Incorporating computational resources in a cancer research program

Abstract

Recent technological advances have transformed cancer genetics research. These advances have served as the basis for the generation of a number of richly annotated datasets relevant to the cancer geneticist. In addition, many of these technologies are now within reach of smaller laboratories to answer specific biological questions. Thus, one of the most pressing issues facing an experimental cancer biology research program in genetics is incorporating data from multiple sources to annotate, visualize, and analyze the system under study. Fortunately, there are several computational resources to aid in this process. However, a significant effort is required to adapt a molecular biology-based research program to take advantage of these datasets. Here, we discuss the lessons learned in our laboratory and share several recommendations to make this transition effectively. This article is not meant to be a comprehensive evaluation of all the available resources, but rather highlight those that we have incorporated into our laboratory and how to choose the most appropriate ones for your research program.

Enhancing research informatics core user satisfaction through agile practices

“…In biomedical science, despite the potential advantages of agile software development, programming guidance in the literature focuses on software engineering tools like version control, testing, and task tracking, which are important but only scratch the surface of current best practices. 15 , 27 , 28 We found only passing reference to the product owner concept in the biomedical literature. 20 Database development, which is common in research informatics cores, has a different culture that has adopted few of the tools of modern software engineering, even the basics like version control, though they likely would have similar benefits.…”