Computational tools are widely used for ranking and prioritizing variants for characterizing their disease relevance. Since numerous tools have been developed, they have to be properly assessed before being applied. Critical Assessment of Genome Interpretation (CAGI) experiments have significantly contributed toward the assessment of prediction methods for various tasks. Within and outside the CAGI, we have addressed several questions that facilitate development and assessment of variation interpretation tools. These areas include collection and distribution of benchmark datasets, their use for systematic large-scale method assessment, and the development of guidelines for reporting methods and their performance. For us, CAGI has provided a chance to experiment with new ideas, test the application areas of our methods, and network with other prediction method developers. In this article, we discuss our experiences and lessons learned from the various CAGI challenges. We describe our approaches, their performance, and impact of CAGI on our research. Finally, we discuss some of the possibilities that CAGI experiments have opened up and make some suggestions for future experiments.
We have participated in various challenges in the four CAGI experiments. We used predictions from PON-P and PON-P2 and additional details to develop models for addressing the challenges. Here, we summarized our approaches and their performances in challenge assessments. We discussed the types of lessons we earned from CAGI, impacts of CAGI on our research and development of predictor with improved performance. We also make some suggestions for future experiments.