Jianming Xie

Prediction of RNA‐binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature

The identification of RNA‐binding residues in proteins is important in several areas such as protein function, posttranscriptional regulation and drug design. We have developed PRBR (Prediction of RNA Binding Residues), a novel method for identifying RNA‐binding residues from amino acid sequences. Our method combines a hybrid feature with the enriched random forest (ERF) algorithm. The hybrid feature is composed of predicted secondary structure information and three novel features: evolutionary information combined with conservation information of the physicochemical properties of amino acids and the information about dependency of amino acids with regards to polarity‐charge and hydrophobicity in the protein sequences. Our results demonstrate that the PRBR model achieves 0.5637 Matthews correlation coefficient (MCC) and 88.63% overall accuracy (ACC) with 53.70% sensitivity (SE) and 96.97% specificity (SP). By comparing the performance of each feature we found that all three novel features contribute to the improved predictions. Area under the curve (AUC) statistics from receiver operating characteristic curve analysis was compared between PRBR model and other models. The results show that PRBR achieves the highest AUC value (0.8675) which represents that PRBR attains excellent performance on predicting the RNA‐binding residues in proteins. The PRBR web‐server implementation is freely available at http://www.cbi.seu.edu.cn/PRBR/. Proteins 2011;

Origin and evolution of a placental-specific microRNA family in the human genome

BackgroundMicroRNAs (miRNAs) are a class of short regulatory RNAs encoded in the genome of DNA viruses, some single cell organisms, plants and animals. With the rapid development of technology, more and more miRNAs are being discovered. However, the origin and evolution of most miRNAs remain obscure. Here we report the origin and evolution dynamics of a human miRNA family.ResultsWe have shown that all members of the miR-1302 family are derived from MER53 elements. Although the conservation scores of the MER53-derived pre-miRNA sequences are low, we have identified 36 potential paralogs of MER53-derived miR-1302 genes in the human genome and 58 potential orthologs of the human miR-1302 family in placental mammals. We suggest that in placental species, this miRNA family has evolved following the birth-and-death model of evolution. Three possible mechanisms that can mediate miRNA duplication in evolutionary history have been proposed: the transposition of the MER53 element, segmental duplications and Alu-mediated recombination. Finally, we have found that the target genes of miR-1302 are over-represented in transportation, localization, and system development processes and in the positive regulation of cellular processes. Many of them are predicted to function in binding and transcription regulation.ConclusionsThe members of miR-1302 family that are derived from MER53 elements are placental-specific miRNAs. They emerged at the early stage of the recent 180 million years since eutherian mammals diverged from marsupials. Under the birth-and-death model, the miR-1302 genes have experienced a complex expansion with some members evolving by segmental duplications and some by Alu-mediated recombination events.

Sequence-Based Prediction of DNA-Binding Residues in Proteins with Conservation and Correlation Information

The recognition of DNA-binding residues in proteins is critical to our understanding of the mechanisms of DNA-protein interactions, gene expression, and for guiding drug design. Therefore, a prediction method DNABR (DNA Binding Residues) is proposed for predicting DNA-binding residues in protein sequences using the random forest (RF) classifier with sequence-based features. Two types of novel sequence features are proposed in this study, which reflect the information about the conservation of physicochemical properties of the amino acids, and the correlation of amino acids between different sequence positions in terms of physicochemical properties. The first type of feature uses the evolutionary information combined with the conservation of physicochemical properties of the amino acids while the second reflects the dependency effect of amino acids with regards to polarity-charge and hydrophobic properties in the protein sequences. Those two features and an orthogonal binary vector which reflect the characteristics of 20 types of amino acids are used to build the DNABR, a model to predict DNA-binding residues in proteins. The DNABR model achieves a value of 0.6586 for Matthews correlation coefficient (MCC) and 93.04 percent overall accuracy (ACC) with a 68.47 percent sensitivity (SE) and 98.16 percent specificity (SP), respectively. The comparisons with each feature demonstrate that these two novel features contribute most to the improvement in predictive ability. Furthermore, performance comparisons with other approaches clearly show that DNABR has an excellent prediction performance for detecting binding residues in putative DNA-binding protein. The DNABR web-server system is freely available at http://www.cbi.seu.edu.cn/DNABR/.

Prediction of autism susceptibility genes based on association rules

Autism is a complex neuropsychiatric disorder with high heritability and an unclear etiology. The identification of key genes related to autism may elucidate its etiology. The current study provides an approach to predicting autism susceptibility genes. Genes are first extracted from the biomedical literature, and some autism susceptibility genes are then recognized as seeds by the prior knowledge. As candidates, the remaining genes are predicted by creating association rules between the seeds and candidates. In an evaluated data set, 27 autism susceptibility genes (type “Y”) are extracted and 43 possible autism susceptibility genes (type “P”) are predicted. The sum of “Y” and “P” genes accounts for 93.3% of the data set that are not contained in the typical database of autism susceptibility genes. Our approach can effectively extract and predict autism susceptibility genes from the biomedical literature. These predicted results complement the typical database of autism susceptibility genes. The web portal for the predicted results, which is freely available at http://biolab.hyit.edu.cn/ar, can be a valuable resource in studies of diseases related to genes.

SVM-Based Approach for Predicting DNA-Binding Residues in Proteins from Amino Acid Sequences

Protein-DNA interactions are vitally important in a wide range of biological processes such as gene regulation and DNA replication and repair. We predict DNA-binding residues in proteins from amino acid sequences by support vector machine (SVM) with a novel hybrid feature which incorporates evolutionary information of amino acid sequences and four physical–chemical properties, including the side chain pKa value, hydrophobicity index, molecular mass and lone electron pairs of amino acids. The classifier achieves 79.12% total accuracy with 74.19% sensitivity and 79.20% specificity, respectively. Moreover, an alternative classifier using random forest (RF) is also constructed. Further analysis proves that the hybrid feature shows obvious contribution to our excellent prediction performance, and the evolutionary information contributes most to the prediction improvement.

Comparability of gene expression in human blood, immune and carcinoma cells☆

The tissue-specific pattern of gene expression can provide important clues for clinical diagnosis and prognosis. Though high-density microarray technology offers the opportunity to examine patterns of mRNA expression on a genomic scale, accessible tissue is still a problem. We conducted secondary data analysis on transcriptional profiles of 79 human tissues using Affymetrix U133a microarrays. We found that expression among tissues belonging to the central nervous system shows distinctly similar patterns. The organ-free immune cells in peripheral blood are more comparable with leukemia/lymphoma at the transcriptome level, and differential co-expression patterns appear between tissue groups of organ-free immune and leukemia/lymphoma cells.

An interpolated Markov model polishes Gibbs sampling's ability in detecting regulatory elements

Microarray techniques provide new methods to find coregulated genes based on their coexpression profiles. Under the assumption that coregulated genes share cis acting regulatory elements, it is important to investigate the upstream sequences controlling the transcription of these genes. A modified Gibbs sampling algorithm with background interpolated Markov model (IMM) has been developed to detect regulatory elements in the upstream regions of translation start site of coexpressed genes. Simulated data are used to test our algorithm successfully. Results show that the improved Gibbs sampling has better performance in extracting less-conserved elements than algorithms with single nucleotide independent model and fixed higher-order Markov models. Then, upstream sequences of two clusters of coexpressed genes from Saccharomyces cerevisiae under diauxic shift conditions are analyzed, several putative motifs that may be involved in the pathway are found.

Patterns of Nucleosome and Chromatin Modifications and Their Effects on Chromatin States

Chromatin packages eukaryotic DNA, it can affect accessibility of DNA, thus chromatin has important roles in gene transcription regulation, DNA replication, DNA repair, and so on. The basic unit of chromatin is nucleosome consisting of ~147 DNA which sharply bends and tightly wraps on surface of octamer of histones. Between two nucleosomes is linker DNA. Histones can be modified by methylation and acetylation. Chromatin state(euchromatin and heterochromatin) is determined by the combinatorial effects of nucleosome positioning, histone modifications, and chromatin-binding proteins. Recently, by virtue of high throughput sequencing technique, the distributions of the chromatin marks were genome-widely determined in various cells. Results indicated that the distribution mode is specific at key sites of genome and is dynamically changed with both cell types and environment stimulus, showing a complex plot. In this paper, we detailedly reviewed the distribution modes of chromatin marks, biological meanings of the modes, interactions of the chromatin marks(nucleosome, DNA and histone modifications, histone variants, chromatin-binding proteins), techniques that were used to determine the marks, and the computational analysis for chromatin states. This work is important in understanding epigenetic regulation mechanism.