基于EM算法的遗传重组率估计方法

EM算法是在不完全信息资料下实现参数极大似然估计的一种通用方法．本文导出了双位点不同标记类型,包括共显性-共显性,共显性-显性和显性-显性3种模式下,估计遗传重组率的EM算法,以及获得重组率抽样方差的Bootstrap方法;并将之推广到部分个体缺失标记基因型(未检测到电泳谱带)下的重组率估计．通过大量Monte Carlo模拟研究发现: (1)连锁紧密时,样本容量对重组率的估计影响不大;连锁松散时,需要较大样本容量才可检测到连锁以及实现重组率的较精确估计．(2)用包含缺失标记的所有个体估计重组率比仅用其中的非缺失标记个体估计更准确,且可显著提高连锁检测的统计功效．     

Predicting secretory protein signal sequence cleavage sites by fusing the marks of global alignments

Summary. A newly synthesized secretory protein in cells bears a special sequence, called signal peptide or sequence, which plays the role of “address tag” in guiding the protein to wherever it is needed. Such a unique function of signal sequences has stimulated novel strategies for drug design or reprogramming cells for gene therapy. To realize these new ideas and plans, however, it is important to develop an automated method for fast and accurately identifying the signal sequences or their cleavage sites. In this paper, a new method is developed for predicting the signal sequence of a query secretory protein by fusing the results from a series of global alignments through a voting system. The very high success rates thus obtained suggest that the novel approach is very promising, and that the new method may become a useful vehicle in identifying signal sequence, or at least serve as a complementary tool to the existing algorithms of this field.     

A de novo designed protein protein interface

As an approach to both explore the physical/chemical parameters that drive molecular self-assembly and to generate novel protein oligomers, we have developed a procedure to generate protein dimers from monomeric proteins using computational protein docking and amino acid sequence design. A fast Fourier transform-based docking algorithm was used to generate a model for a dimeric version of the 56-amino-acid beta1 domain of streptococcal protein G. Computational amino acid sequence design of 24 residues at the dimer interface resulted in a heterodimer comprised of 12-fold and eightfold variants of the wild-type protein. The designed proteins were expressed, purified, and characterized using analytical ultracentrifugation and heteronuclear NMR techniques. Although the measured dissociation constant was modest ( approximately 300 microM), 2D-[(1)H,(15)N]-HSQC NMR spectra of one of the designed proteins in the absence and presence of its binding partner showed clear evidence of specific dimer formation.     

New cerebellar phenotypes in YAC transgenic mouse in vivo library of human Down syndrome critical region-1

Down syndrome (DS) is the most frequent genetic cause of mental retardation (MR) associated with neurological alterations. To allow a genetic dissection of DS phenotype, we studied eight transgenic mouse lines carrying YACs containing human DNA fragments covering DS critical region (DCR-1), as an in vivo library. Herein, we found an increased brain size in the 152F7-mice containing DYRK1A gene. We also identified a new cerebellar alteration in two independent lines carrying 230E8-YAC. These mice showed significant elongation of the cerebellar antero-posterior axis (p < 0.001), determined by increased length of rostral folia of the vermis (lobule II-V, p < 0.0001; lobule VI, p < 0.001). In addition, we identified a major neurological defect in culmen and declivus lobules in the 230E8-mice. We analyzed P30, P12, and P9 stages and detected high significant increased lengths of anterior lobules (II-VI) of 230E8-mice at P30 and P12 (lobule II-V, p < 0.0001; lobule VI, p < 0.05), but not at P9, indicating that this new phenotype appears between P9 and P12. Interestingly, 230E8-mice also present increased cortical cell density and mild learning defects. 230E8-YAC contains seven genes, some of which could be potentially responsible for this phenotype. Between them, we proposed DOPEY2 as potential candidate gene for these cerebellar alterations considering its high expression in the brain and that its homologous genes in yeast, Caenorhabditis elegans and Drosophila are involved in morphogenesis, suggesting a conserved role of DOPEY2 as a patterning gene.     

A segmentation/clustering model for the analysis of array CGH data

Microarray-CGH (comparative genomic hybridization) experiments are used to detect and map chromosomal imbalances. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not provide a biological status for the detected segments. We propose a new model for this segmentation/clustering problem, combining a segmentation model with a mixture model. We present a new hybrid algorithm called dynamic programming-expectation maximization (DP-EM) to estimate the parameters of the model by maximum likelihood. This algorithm combines DP and the EM algorithm. We also propose a model selection heuristic to select the number of clusters and the number of segments. An example of our procedure is presented, based on publicly available data sets. We compare our method to segmentation methods and to hidden Markov models, and we show that the new segmentation/clustering model is a promising alternative that can be applied in the more general context of signal processing.     

Estimation of optimal feeding strategies for fed-batch bioprocesses

A generic methodology for feeding strategy optimization is presented. This approach uses a genetic algorithm to search for optimal feeding profiles represented by means of artificial neural networks (ANN). Exemplified on a fed-batch hybridoma cell cultivation, the approach has proven to be able to cope with complex optimization tasks handling intricate constraints and objective functions. Furthermore, the performance of the method is compared with other previously reported standard techniques like: (1) optimal control theory, (2) first order conjugate gradient, (3) dynamical programming, (4) extended evolutionary strategies. The methodology presents no restrictions concerning the number or complexity of the state variables and therefore constitutes a remarkable alternative for process development and optimization. This revised version was published online in June 2005 with corrections to the Appendix.     

序列搜索算法在多肽质谱解析中的应用

序列搜索算法由三部分组成：搜索过程、搜索得到多肽的各氨基酸残基的评分及两端（N端、C端）搜索得到的多肽的合并过程.通过若干实际多肽质谱的解析,结果表明,该算法对多种序列专一性离子并存的未知多肽质谱的解析,可获得较满意结果.尤其是它的评分方式及标准,比较适合多肽质谱图的实际情况,可最大限度地判断解析结果的准确度,为从事用质谱测定多肽一级结构的分析工作者提供了一比较简便且可靠的手段.也为质谱法快速测定蛋白质或多肽序列及其在生物学中的普及提供了一条方便之路.     

夜间光照对褪黑激素抑制的量化计算

作为人体的重要激素之一,褪黑激素具备重要的生理功能．不适当的夜间光照会造成人体生物钟节律的异常,进而导致褪黑激素内分泌的抑制．夜间光照引起的褪黑激素抑制与光的波长和色温关系密切．此前尚仍缺乏一个夜间光照对褪黑激素抑制效果的量化计算方案．提出了一种夜间光照对褪黑激素抑制的量化算法,拟合了人体血液褪黑激素抑制率的相对光谱灵敏度归一化曲线,建立了夜间光照与褪黑激素抑制量化计算的算法模型．研究结果为室内安全光照环境的设计提供了理论依据和计算方法,并可在光污染的控制、夜间安全环境照明标准的制定等方面得到应用．     

On sparse Fisher discriminant method for microarray data analysis

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calculate a weight for each gene and use the weight values as an indicator to identify the subsets of relevant genes that categorize patient and normal samples. A l(2) - l(1) norm minimization method is implemented to the discriminant process to automatically compute the weights of all genes in the samples. The experiments on two microarray data sets have shown that the new algorithm can generate classification results as good as other classification methods, and effectively determine relevant genes for classification purpose. In this study, we demonstrate the gene selection's ability and the computational effectiveness of the proposed algorithm. Experimental results are given to illustrate the usefulness of the proposed model.