title: Using a neural network for lane-tracking of DNA sequencing slab gels.
abstract: High quality lane-tracking of gel images is the first task, and thus a prerequisite, for successful trace processing and base-calling of DNA sequencing slab gels. In most approaches, it is based on statistical calculations, for instance variance and co-variance analysis between neighboring pixel columns in the image. On the basis of these statistical calculations, Kohonen's self-organization neural network model was introduced. We have found that, using several well-structured input data, Kohonen's self-organization neural network model can be trained to fulfill our task of lane-tracking. Furthermore, the quality of lane-tracking could be improved compared to algorithmic approaches.
authors.affiliation: European Molecular Biology Laboratory (EMBL), Meyerhofstr. 1, D-69117, Heidelberg, Germany.
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.forename: C
authors.initials: C
authors.lastname: Schwager
authors.forename: W
authors.initials: W
authors.lastname: Ansorge
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004591:Electrophoresis, Polyacrylamide Gel / Q000379:methods; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017422:Sequence Analysis, DNA / Q000379:methods*