Many researchers have used microarray gene expression data to investigate gene regulatory networks in specific life stages. In these analyses,Bayesian network was widely applied to regulatory network building from expression profiles because of its solid mathematical foundation and its robust analysis ability in noisy data. However,the building of Bayesian network is time consuming and the searching space is really large. Considering the biological feature of transcription factors (TFs) and targets (TGs),the regulatory network is possible to be separated into core TFs networks and the interactions from TFs to TGs. We developed an R package named ModuleNet which used Bayesian network model to the inner TFs network building and genetic algorithm on TF-TG interactions prediction. With determined number of transcription factors,the searching space and time requirements of ModuleNet is linear increasing according to the number of targets. After application to yeast cell-cycle expression profile,the results demonstrated the prediction accuracy of ModuleNet. Furthermore,significantly enriched Gene Ontology (GO) terms with similar expression behaviors were detected automatically by ModuleNet from expression profile,and the relationships from TFs to GO terms were figured out. The source code is available by asking for the author.
Avian influenza A viruses could get across the species barrier and be fatal to humans. Highly patho-genic avian influenza H5N1 virus was an example. The mechanism of interspecies transmission is not clear as yet. In this research,the protein sequences of 237 influenza A viruses with different subtypes were transformed into pseudo-signals. The energy features were extracted by the method of wavelet packet decomposition and used for virus classification by the method of hierarchical clustering. The clustering results showed that five patterns existed in avian influenza A viruses,which associated with the phenotype of interspecies transmission,and that avian viruses with patterns C and E could across species barrier and those with patterns A,B and D might not have the abilities. The results could be used to construct an early warning system to predict the transmissibility of avian influenza A viruses to humans.
This research analyzed amino acid sequence similarity between non-self T cell epitopes recognized by mouse antibodies and mouse proteins. Using sequence alignment,we found that only 8 of 1 108 epitopes are highly similar to mouse protein sequences. The result shows that non-self T cell epitopes are not similar or have little similarity to mouse protein sequences. Furthermore,reviewing the related literature,we also found that the eight epitopes would trigger immune responses in some particular environment,which are ignored by T cells in normal condition. The result suggests that no or low-similarity peptide vaccines can reduce the chance of collateral cross-reactions and enhance the antigen-specific immune response to vaccine.
