The primary objective of this research is to improve automated analysis of gel-based DNA sequencing ladders, through pattern recognition-based translation of raw instrument data to DNA sequences. We emphasize neural networks, adapted to particular sequencing conditions and instruments. The performances of pattern recognition and conventional basecalling software will be evaluated: (1) as experimental errors challenge description of the natural allelic diversity of human adenoviral genomes; (2) for detection and specification of heterozygous loci in diploid template experiments; (3) for primer selection and assembly operations of large scale sequencing projects. Distributions of basecalling errors will be analyzed in the contexts of neighboring nucleotide identities and as results of different sequencing strategies. Three principal advantages are expected from pattern recognition basecalling software: (1) analysis of contextual arrays of oligomer traces improves basecalling accuracy; (2) specifically tasked, neural network and algorithmic processors support on-line signal conditioning and basecalling in real time; and (3) the signal conditioning and pattern recognition modules support objective measures of confidence for each basecall. This project will significantly and positively impact progress towards the stated goals of the human genome initiative. No incremental costs for hardware or strategic modifications are required. Cost savings can be realized through automation of labor intensive review and editing of primary data. Real-time basecalling supports higher throughput instruments, exploiting faster separation of larger parallel arrays of sequencing ladders. Objective basecall confidence parameters support overlap assignment during sequence assembly, and should facilitate sequence - match searches through expanding databases.
