A segment-based dynamic programming algorithm for predicting gene structure.

An algorithm called segment-based dynamic programming is described for predicting gene structure from a sequence of genomic DNA. The algorithm explores the space of gene structures that satisfy junctional and frame constraints and finds the gene structure that optimizes the sum of junctional and segmental scoring functions. Junctional constraints specify acceptable sites of initiation, termination, and splicing, whereas frame constraints ensure that the total exon length is a multiple of three and that no in-frame stop codons occur within exons or at exon-exon junctions. By computing over segments, segment-based dynamic programming maintains reading frame and phase information for each segment, it can assemble exons in-frame as well as score them in-frame. The algorithm is used to quantify the computational power of constraints. Experimental results show that frame constraints reduce the size of the search space by several orders of magnitude and that cardinality constraints place an asymptotic limit on the size of the search space. The algorithm is also used to compare the accuracy of different methods for assembly and scoring. A scoring scheme based on fifth-order Markov hexamer frequencies is presented and used in three objective functions, corresponding to in-frame, frame-independent, and frame-maximal scoring strategies. Experimental results show that in-frame assembly improves specificity only slightly over frame-independent assembly, whereas in-frame scoring improves specificity substantially over frame-independent and frame-maximal scoring.