A Segment-based Dynamic Programing Algorithm for Parsing Gene Structure (Running Head: Segment-based Dynamic Programming)

Note: This version is a preliminary draft. Comments and suggestions are welcome. Abstract Predicting gene structure requires search within a combinatorially large space of possible gene structures. The search space may be narrowed by two types of computational tools: optimality criteria and consistency constraints. Consistency constraints are requirements concerning reading frame and stop codons, namely: the total exon length must be a multiple of three; exons may not contain internal stop codons in their reading frame; and exon-exon junctions may not form stop codons in their reading frame. I present a segment-based dynamic programming algorithm that explores the space of globally consistent gene structures, and finds the optimally scoring gene structure within that space. The algorithm may be modified to provide an arbitrary number of near-optimal solutions and to allow cardinality constraints that limit the number of exons in the gene structure. The algorithm maintains reading frame information that may be used to improve scoring estimates of the likelihood of exons. Segment-based dynamic programming has a running time that is expected to be between linear and quadratic with respect to sequence length, depending on the scoring scheme used. I use the algorithm to explore the power of various constraints by comparing their search spaces. The results show that consistency and cardinality constraints reduce the search space by large orders of magnitude.