Restriction-map variation with the yellow-achaete-scute region in five populations of Drosophila melanogaster

It has been proposed that the degree of recombination for a genomic region will affect the level of both nucleotide heterozygosity and the density of transposable elements. Both features of genomic diversity have been examined in a number of recent reports for regions undergoing relatively normal levels of recombination in Drosophila melanogaster. In this study the genomic variation associated with yellow-achaete- scute loci located at the tip of the X chromosome is examined by six- cutter restriction mapping. In this region, as usual for regions adjacent to telomeres, crossing-over is dramatically reduced, and published studies of visible mutants indicate extremely little restriction-map variation. Eight six-cutter restriction endonucleases were used to locate sequence variation in 14- and 16.5-kb regions in 109 lines sampled from North America, Africa, and Europe. The overall level of heterozygosity is estimated as 0.29%. Nine large insertions, all presumed to be transposable elements, were observed. Base-pair heterozygosity appears to be reduced compared with regions having normal levels of recombination. The estimated heterozygosity is much higher than reported in earlier studies of restriction-map variation among visible mutations in the complex. The incidence of large insertions is not elevated compared with that in other regions of the genome. This suggests that asymmetric synapsis and exchange is not an important mechanism for the elimination of transposable elements.      

Automatic signal classification in fluorescence in situ hybridization images

BACKGROUND: Previous systems for dot (signal) counting in fluorescence in situ hybridization (FISH) images have relied on an auto-focusing method for obtaining a clearly defined image. Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals can be left unfocused or unseen. This leads to dot counting errors, which increase with the number of probes. METHODS: The approach described here dispenses with auto-focusing, and instead relies on a neural network (NN) classifier that discriminates between in and out-of-focus images taken at different focal planes of the same field of view. Discrimination is performed by the NN, which classifies signals of each image as valid data or artifacts (due to out of focusing). The image that contains no artifacts is the in-focus image selected for dot count proportion estimation. RESULTS: Using an NN classifier and a set of features to represent signals improves upon previous discrimination schemes that are based on nonadaptable decision boundaries and single-feature signal representation. Moreover, the classifier is not limited by the number of probes. Three classification strategies, two of them hierarchical, have been examined and found to achieve each between 83% and 87% accuracy on unseen data. Screening, while performing dot counting, of in and out-of-focus images based on signal classification suggests an accurate and efficient alternative to that obtained using an auto-focusing mechanism.