We have developed and implemented a method for computational gene identification called GIN (gene identification using neural nets and homology information) that has been particularly designed to avoid false positive predictions. It thus predicts 55% of all genes tested correctly, has a specificity of 99%, but also has an overall accuracy of 92% on a benchmark set of 570 vertebrate genes constructed by Burset and Guigo. The method combines homology searches in protein and expressed sequence tag databases with several neural networks designed to recognize start codons, Poly(A) signals, stop codons, and splice sites. Predicted exons are assembled into genes using a homology-based scoring function. GIN is able to recognize multiple genes within genomic DNA as demonstrated by the identification of a globin gene (gamma-globin-1(G)) that has not been annotated as a coding region in the widely used the test set of Burset and Guigo. Furthermore, GIN identifies more than 107 other protein hits in noncoding regions and classifies them into possible pseudogenes or splice variants.

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R4, R3, R2, R1, R0, R1', R2', R3', and R4', where the acceptor at R0 is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method--Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30 = 86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.

Sequence analysis is a powerful tool to obtain structural and functional information about genes and their products. Here we show that TSG101, a gene subjected to somatic mutations in breast cancer, contains an amino terminal domain that is a homologue of ubiquitin conjugating enzymes (UBCs) and not, as previously proposed, DNA-binding domains. As the UBC active site residue is replaced in the TSG101 sequence in a similar manner to several other members of the UBC family, we propose a role for TSG101 in regulating the ubiquitination of short-lived gene products.