MOTIVATION: Mapping the antigenic and genetic evolution pathways of influenza A is of critical importance in the vaccine development and drug design of influenza virus. In this article, we have analyzed more than 4000 A/H3N2 hemagglutinin (HA) sequences from 1968 to 2008 to model the evolutionary path of the influenza virus, which allows us to predict its future potential drifts with specific mutations. RESULTS: The mutual information (MI) method was used to design a site transition network (STN) for each amino acid site in the A/H3N2 HA sequence. The STN network indicates that most of the dynamic interactions are positioned around the epitopes and the receptor binding domain regions, with strong preferences in both the mutation sites and amino acid types being mutated to. The network also shows that antigenic changes accumulate over time, with occasional large changes due to multiple co-occurring mutations at antigenic sites. Furthermore, the cluster analysis by subdividing the STN into several subnetworks reveals a more detailed view about the features of the antigenic change: the characteristic inner sites and the connecting inter-subnetwork sites are both responsible for the drifts. A novel five-step prediction algorithm based on the STN shows a reasonable accuracy in reproducing historical HA mutations. For example, our method can reproduce the 2003-2004 A/H3N2 mutations with approximately 70% accuracy. The method also predicts seven possible mutations for the next antigenic drift in the coming 2009-2010 season. The STN approach also agrees well with the phylogenetic tree and antigenic maps based on HA inhibition assays. AVAILABILITY: All code and data are available at http://ibi.zju.edu.cn/birdflu/.

MOTIVATION: With the recent progress made in large-scale plant functional genome sequencing projects, a great amount of EST (express sequence tag) data is becoming available. With the help of complete genomic sequence information of model plants (rice and Arabidopsis), it is possible to predict the joints between adjacent exons after splicing (or termed 'intron positions' for short) in homologous ESTs of other plants. This would allow developing potential intron polymorphism (PIP) markers in these plants by designing primers in exons flanking the target intron. RESULTS: We have extracted a total of 57,658 PIP markers in 59 plant species and created a web-based database platform named PIP to provide detailed information of these PIP markers and homologous relationships among PIP markers from different species. The platform also provides a function of online designing of PIP markers based on cDNA/EST sequences submitted by users. With evaluations performed in silico, we have found that the intron position prediction is highly reliable and the polymorphism level of PIP markers is high enough for practical need. AVAILABILITY: http://ibi.zju.edu.cn/pgl/pip/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.