Abstract
Citizen science is the collaboration of public participation with scientific research in efforts to advance scientific knowledge, address societal needs, and increase science literacy and attitudes in the public. Volunteers collect, process, and analyze data that would otherwise be too time consuming and costly for researchers to handle. One genre of citizen science projects are citizen science games, or games with a purpose. Through gamification, the complexity of problems can be reduced to that of which the general public can understand. Foldit, EyeWire, and Phylo are examples of gamifying the complex problems of folding proteins, mapping neurons, and aligning genome sequences, respectively. In this thesis, a new citizen science game, nucleoSLIDE, is developed to help solve the motif finding problem, where a motif can be defined as some meaningful, unknown pattern of length k hidden in a DNA sequence. The motif finding problem is important for genetic research and involves finding a collection of motifs, one from each sequence, in which the variation in motifs is minimal. This problem is NP-complete, meaning there exists no algorithm with a guaranteed optimal solution at this time. Furthermore, the size of sequences and lack of conservation of motifs in real-world data further complicates the problem. Like other citizen science games, nucleoSLIDE takes advantage of natural human skills, like pattern recognition, to overcome these complications. This thesis demonstrates how the complexity of the motif finding problem, a non-game context, can be reduced through the use of common game design elements, including but not limited to goals, rules, play, and user interface.