The objective of database AsMamDB is to facilitate the systematic study of alternatively spliced genes of mammals. Version 1.0 of AsMamDB contains 1563 alternatively spliced genes of human, mouse and rat, each associated with a cluster of nucleotide sequences. The main information provided by AsMamDB includes gene alternative splicing patterns, gene structures, locations in chromosomes, products of genes and tissues where they express. Alternative splicing patterns are represented by multiple alignments of various gene transcripts and by graphs of their topological structures. Gene structures are illustrated by exon, intron and various regulatory elements distributions. There are 4204 DNAs, 3977 mRNAs, 8989 CDSs and 126 931 ESTs in the current database. More than 130000 GenBank entries are covered and 4443 MEDLINE records are linked. DNA, mRNA, exon, intron and relevant regulatory element sequences are provided in FASTA format. More information can be obtained by using the web-based multiple alignment tool Asalign and various category lists. AsMamDB can be accessed at http://166.111.30.65/ASMAMDB. html.

-loop structure and have an important influence on protein functions, as shown by the predicted 3D structure of human IL-4δ2. The observed VSAS events can be classified into two groups depending on whether they insert new structure domains in the proteins, and they might be of different evolutionary status.

Schizophrenia is a common mental disorder with a complex pattern of inheritance. Despite a large number of studies in the past decades, its molecular etiology remains unknown. In this study, we proposed a 'system-thinking' strategy in seeking the combined effect of susceptibility genes for a complex disorder by using paranoid schizophrenia as an example. We genotyped 85 reported single-nucleotide polymorphisms (SNPs) present in 23 genes for the dopamine (DA) metabolism pathway among 83 paranoid schizophrenics and 108 normal controls with detailed clinical and genetic information. We developed two novel multilocus approaches-the potential effective SNP combination pattern and potential effective dynamic effects analysis, by which three susceptibility genotype combinations were found to be associated with schizophrenia. These results were also validated in a family-based cohort consisting of 95 family trios of paranoid schizophrenia. The present findings suggest that the COMT and ALDH3 combination may be the most common type involved in predisposing to schizophrenia. Since the combination blocks the whole pathways for the breakdown of DA and noradrenaline, it is very likely to play a central role in developing paranoid schizophrenia.

A general evolutionary trend is the generation of organisms of increasing complexity, notwithstanding that reduction and simplification phenomena do occur in the evolutionary process. This paper proposes an evolutionary model incorporating the mechanisms of gene amplification and deletion. The evolutionary process leading to genomic complexity and the coexistence of simpler organisms with complicated ones were both simulated using the proposed model. The model was also used to investigate the influence of various factors on the evolution of complexity. The simulations indicated that the evolution of complexity is largely influenced by adaptation to complicated environments. Nevertheless, complex organisms require relatively more resources for survival and replication, which limits the on going tendency towards complexity. Moreover, the analysis showed that if the environment varies rapidly and the profit obtained from complexity is greater than the resources consumed, selection will tend to favor complexity. However, high living cost will tend to limit the trend of complexity and if the environment is relatively stable, reduction and simplification will become the dominant trends.

G-protein coupled receptors (GPCRs) play a key role in different biological processes, such as regulation of growth, death and metabolism of cells. They are major therapeutic targets of numerous prescribed drugs. However, the ligand specificity of many receptors is unknown and there is little structural information available. Bioinformatics may offer one approach to bridge the gap between sequence data and functional knowledge of a receptor. In this paper, we use a bagging classification tree algorithm to predict the type of the receptor based on its amino acid composition. The prediction is performed for GPCR at the sub-family and sub-sub-family level. In a cross-validation test, we achieved an overall predictive accuracy of 91.1% for GPCR sub-family classification, and 82.4% for sub-sub-family classification. These results demonstrate the applicability of this relative simple method and its potential for improving prediction accuracy.