J. B. Golden III, D. Torgersen, and C. Tibbetts
The massive scale of DNA sequencing for the Human Genome Initiative compels efforts to reduce the cost and increase the throughput of DNA sequencing technology. Contemporary automated DNA sequencing systems do not yet meet estimated performance requirements for cost-effective and timely completion of this project. Greater accuracy of basecalling software would minimize manual review and editing of basecalling results, and facilitate assembly of primary sequences to large contig(uous) arrays. In this report describe a neural network model for photometric signal conditioning during raw data acquisition with an automated DNA sequencer. This network supports on-line extraction and evaluation of informative arrays of oligomer separations and yields, as a feature table for accurate, real-time basecalling.