Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions

We present a probabilistic logic program to generate an educational puzzle that introduces the basic principles of next generation sequencing, gene finding and the translation of genes to proteins following the central dogma in biology. In the puzzle, a secret ”protein word” must be found by assembling DNA from fragments (reads), locating a gene in this sequence and translating the gene to a protein. Sampling using this program generates random instance of the puzzle, but it is possible constrain the difficulty and to customize the secret protein word. Because of these constraints and the randomness of the generation process, sampling may fail to generate a satisfactory puzzle. To avoid failure we employ a strategy using adaptive probabilities which change in response to previous steps of generative process, thus minimizing the risk of failure.