Chemical composition is maintained in poorly conserved intrinsically disordered regions and suggests a means for their classification.

Intrinsically disordered regions in proteins are known to evolve rapidly while maintaining their function. However, given their lack of structure and sequence conservation, the means through which they stay functional is not clear. Poor sequence conservation also hampers the classification of these regions into functional groups. We studied the sequence conservation of a large number of predicted and experimentally determined intrinsically disordered regions from the human proteome in 7 other eukaryotes. We determined the chemical composition of disordered regions by calculating the fraction of positive, negative, polar, hydrophobic and special (Pro, Gly) residues, and studied its maintenance in orthologous proteins. A significant number of disordered regions with low sequence conservation showed considerable similarity in their chemical composition between orthologs. Clustering disordered regions based on their chemical composition resulted in functionally distinct groups. Finally, disordered regions showed location preference within the proteins that was dependent on their chemical composition. We conclude that preserving the overall chemical composition is one of the ways through which intrinsically disordered regions maintain their flexibility and function through evolution. We propose that the chemical composition of disordered regions can be used to classify them into functional groups and, together with conservation and location, may be used to define a general classification scheme.

CELLPEDIA: a repository for human cell information for cell studies and differentiation analyses.

CELLPEDIA is a repository database for current knowledge about human cells. It contains various types of information, such as cell morphologies, gene expression and literature references. The major role of CELLPEDIA is to provide a digital dictionary of human cells for the biomedical field, including support for the characterization of artificially generated cells in regenerative medicine. CELLPEDIA features (i) its own cell classification scheme, in which whole human cells are classified by their physical locations in addition to conventional taxonomy; and (ii) cell differentiation pathways compiled from biomedical textbooks and journal papers. Currently, human differentiated cells and stem cells are classified into 2260 and 66 cell taxonomy keys, respectively, from which 934 parent-child relationships reported in cell differentiation or transdifferentiation pathways are retrievable. As far as we know, this is the first attempt to develop a digital cell bank to function as a public resource for the accumulation of current knowledge about human cells. The CELLPEDIA homepage is freely accessible except for the data submission pages that require authentication (please send a password request to cell-info@cbrc.jp). Database URL: http://cellpedia.cbrc.jp/

Efficient determination of cluster boundaries for analysis of gene expression profile data using hierarchical clustering and wavelet transform.

The existing methods for clustering of gene expression profile data either require manual inspection and other biological knowledge or require some cut-off value which can not be directly calculated from the given data set. Thus, the problem of systematic and efficient determination of cluster boundaries of clusters in gene expression profile data still remains demanding. In this context, we have developed a procedure for automatic and systematic determination of the boundaries of clusters in the hierarchical clustering of gene expression data based on the ratio of with-in class variance and between-class variance, which can be fully calculated from the given expression data. After the determination of dendrogram based on agglomerative hierarchical clustering, this ratio is used to determine the cluster boundary. Except this ratio which can be completely calculated from the given expression profile data, unlike other existing approaches, our approach does not require any manual inspection or biological knowledge. Our results are favorably comparable and in some of cases better than existing method which does not utilize prior information or manual inspection. Moreover, gene expression profile data are often contaminated with various type of noise and in order to reduce this noise content, we have also applied image enhancing technique called discrete wavelet transform. We tested a number of mother wavelet functions to smooth the noise in the gene expression data set and obtained some improvements in the quality of the results.