Using a neural network for lane-tracking of DNA sequencing slab gels

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.