CpG序列的免疫激活机理及应用

ＣｐＧ序列是一些在动物体内具有强烈的免疫激活功能的寡聚脱氧核苷酸。本文探讨了ＣｐＧ 序列在动物体内激活免疫反应的作用机理,概述了ＣｐＧ应用研究的进展,展望了ＣｐＧ 序列的应用前景     

Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space

The generation of genomic binding or accessibility data from massively parallel sequencing technologies such as ChIP-seq and DNase-seq continues to accelerate. Yet state-of-the-art computational approaches for the identification of DNA binding motifs often yield motifs of weak predictive power. Here we present a novel computational algorithm called MotifSpec, designed to find predictive motifs, in contrast to over-represented sequence elements. The key distinguishing feature of this algorithm is that it uses a dynamic search space and a learned threshold to find discriminative motifs in combination with the modeling of motifs using a full PWM (position weight matrix) rather than k-mer words or regular expressions. We demonstrate that our approach finds motifs corresponding to known binding specificities in several mammalian ChIP-seq datasets, and that our PWMs classify the ChIP-seq signals with accuracy comparable to, or marginally better than motifs from the best existing algorithms. In other datasets, our algorithm identifies novel motifs where other methods fail. Finally, we apply this algorithm to detect motifs from expression datasets in C. elegans using a dynamic expression similarity metric rather than fixed expression clusters, and find novel predictive motifs.     

BEAM: a beam search algorithm for the identification of cis-regulatory elements in groups of genes.

The identification of potential protein binding sites (cis-regulatory elements) in the upstream regions of genes is key to understanding the mechanisms that regulate gene expression. To this end, we present a simple, efficient algorithm, BEAM (beam-search enumerative algorithm for motif finding), aimed at the discovery of cis-regulatory elements in the DNA sequences upstream of a related group of genes. This algorithm dramatically limits the search space of expanded sequences, converting the problem from one that is exponential in the length of motifs sought to one that is linear. Unlike sampling algorithms, our algorithm converges and is capable of finding statistically overrepresented motifs with a low failure rate. Further, our algorithm is not dependent on the objective function or the organism used. Limiting the space of candidate motifs enables the algorithm to focus only on those motifs that are most likely to be biologically relevant and enables the algorithm to use direct evaluations of background frequencies instead of resorting to probabilistic estimates. In addition, limiting the space of candidate motifs makes it possible to use computationally expensive objective functions that are able to correctly identify biologically relevant motifs.     

AT-hook蛋白的最新研究进展

AT-hook是一类新的DNA结合蛋白基序,与其他功能已知的DNA结合蛋白基序不同。AT-hook蛋白具有AT-hook基序和PPC(plants and prokaryotes conserved domain,DUF296)两个特殊功能域。AT-hook广泛存在于不同物种的DNA结合蛋白中,在植物生长发育、器官构建、逆境胁迫和激素信号应答中发挥重要的调节作用;对基因克隆、细胞间特异性结合、染色体结构调节以及转录因子调节具有重要的调控作用。通过调节AT-hook蛋白进而改变生物某些不理想的生理生化调控通路,提高生物某些优良性状具有重要研究价值及意义。本综述主要从AT-hook蛋白的结构特征、分类及其依据、功能调节机制、生物学功能以及研究价值等方面进行相关阐述及总结。     

Tyrosine-kinase Wzc from Escherichia coli possesses an ATPase activity regulated by autophosphorylation

The catalytic mechanism of bacterial tyrosine-kinases (PTK) is poorly understood. These enzymes possess Walker A and B ATP-binding motifs, which are effectively required for their autophosphorylation whereas these motifs are usually found in ATP-binding proteins but not in eukaryotic protein-kinases. It was previously shown that the PTK Wzc in Escherichia coli undergoes intra- and interphosphorylation. In this work, it is shown that, in addition to its kinase activity, Wzc produces free inorganic phosphate. It is demonstrated that this ATPase activity is increased significantly by intraphosphorylation of Wzc. The fact that intraphosphorylation of Wzc does not affect Wzc affinity for ATP was also demonstrated and it was suggested that it could rather modify the local environment of the ATP molecule in the catalytic site so as to render Wzc more liable to catalyze ATP hydrolysis and interphosphorylation. These results should contribute to better understanding of the catalytic mechanism of this particular class of tyrosine-kinases, which seems, so far, restricted to bacteria.     

Mining protein sequences for motifs.

We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. The algorithm assumes that a motif is constituted by the presence of a "good" combination of residues in appropriate locations of the motif. The algorithm attempts to compile such good combinations into a "pattern dictionary" by processing an aligned training set of protein sequences. The dictionary is subsequently used to detect motifs in new protein sequences. Statistical significance of the detection results are ensured by statistically determining the various parameters of the algorithm. Based on this approach, we have implemented a program called GYM. The Helix-Turn-Helix motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with existing programs. In addition, the GYM program provides a lot of useful information about a given protein sequence.     

Ribokinase family evolution and the role of conserved residues at the active site of the PfkB subfamily representative, Pfk-2 from Escherichia coli

Phosphofructokinase-2 (Pfk-2) belongs to the ribokinase family and catalyzes the ATP-dependent phosphorylation of fructose-6-phosphate, showing allosteric inhibition by a second ATP molecule. Several structures have been deposited on the PDB for this family of enzymes. A structure-based multiple sequence alignment of a non-redundant set of these proteins was used to infer phylogenetic relationships between family members with different specificities and to dissect between globally conserved positions and those common to phosphosugar kinases. We propose that phosphosugar kinases appeared early in the evolution of the ribokinase family. Also, we identified two conserved sequence motifs: the TR motif, not described previously, present in phosphosugar kinases but not in other members of the ribokinase family, and the globally conserved GXGD motif. Site-directed mutagenesis of R90 and D256 present in these motifs, indicate that R90 participates in the binding of the phosphorylated substrate and that D256 is involved in the phosphoryl transfer mechanism.     

MUSA: a parameter free algorithm for the identification of biologically significant motifs

MOTIVATION: The ability to identify complex motifs, i.e. non-contiguous nucleotide sequences, is a key feature of modern motif finders. Addressing this problem is extremely important, not only because these motifs can accurately model biological phenomena but because its extraction is highly dependent upon the appropriate selection of numerous search parameters. Currently available combinatorial algorithms have proved to be highly efficient in exhaustively enumerating motifs (including complex motifs), which fulfill certain extraction criteria. However, one major problem with these methods is the large number of parameters that need to be specified. RESULTS: We propose a new algorithm, MUSA (Motif finding using an UnSupervised Approach), that can be used either to autonomously find over-represented complex motifs or to estimate search parameters for modern motif finders. This method relies on a biclustering algorithm that operates on a matrix of co-occurrences of small motifs. The performance of this method is independent of the composite structure of the motifs being sought, making few assumptions about their characteristics. The MUSA algorithm was applied to two datasets involving the bacterium Pseudomonas putida KT2440. The first one was composed of 70 sigma(54)-dependent promoter sequences and the second dataset included 54 promoter sequences of up-regulated genes in response to phenol, as suggested by quantitative proteomics. The results obtained indicate that this approach is very effective at identifying complex motifs of biological significance. AVAILABILITY: The MUSA algorithm is available upon request from the authors, and will be made available via a Web based interface.     

Motif identification method based on Gibbs sampling and genetic algorithm

The regulation of gene expression is the key of organism genetic mechanism. Motif identification is an important step in constructing expression regulatory network. Based on Gibbs sampling method, this work constructed position weight matrix, thereby proposing motif recognition method based on genetic algorithm. Scoring function is defined to update the population and obtain the convergence matrix of position weight, achieving the identification of motifs with different length. Simulation and experimental data sets were utilized to verify the accuracy and execution time of the algorithm.     

Systematic identification of mammalian regulatory motifs' target genes and functions

We developed an algorithm, Lever, that systematically maps metazoan DNA regulatory motifs or motif combinations to sets of genes. Lever assesses whether the motifs are enriched in cis-regulatory modules (CRMs), predicted by our PhylCRM algorithm, in the noncoding sequences surrounding the genes. Lever analysis allows unbiased inference of functional annotations to regulatory motifs and candidate CRMs. We used human myogenic differentiation as a model system to statistically assess greater than 25,000 pairings of gene sets and motifs or motif combinations. We assigned functional annotations to candidate regulatory motifs predicted previously and identified gene sets that are likely to be co-regulated via shared regulatory motifs. Lever allows moving beyond the identification of putative regulatory motifs in mammalian genomes, toward understanding their biological roles. This approach is general and can be applied readily to any cell type, gene expression pattern or organism of interest.     

用网络方法识别生物序列motif

生物序列motif的识别是后基因组时代的一个核心问题。本文首先回顾了识别motif的几种主要算法,然后根据motif的重要性和随机性介绍了利用网络识别motif的两种具有代表性的方法：一种是建立一个随机网络混合模型,利用EM算法识别其中随机的网络motif;另一种用修正的参数流算法过滤出其中的最大密度予图,即为生物序列motif,并指出这两种方法的优劣,最后还对今后研究方向给出了讨论。     

MAVisto: a tool for the exploration of network motifs

SUMMARY: MAVisto is a tool for the exploration of motifs in biological networks. It provides a flexible motif search algorithm and different views for the analysis and visualization of network motifs. These views help to explore interesting motifs: the frequency of motif occurrences can be compared with randomized networks, a list of motifs along with information about structure and number of occurrences depending on the reuse of network elements shows potentially interesting motifs, a motif fingerprint reveals the overall distribution of motifs of a given size and the distribution of a particular motif in the network can be visualized using an advanced layout algorithm. AVAILABILITY: MAVisto is platform independent and available free of charge as a Java webstart application at http://mavisto.ipk-gatersleben.de/ CONTACT: schwoebb@ipk-gatersleben.de SUPPLEMENTARY INFORMATION: Can be found at http://mavisto.ipk-gatersleben.de/     

Protein motif extraction with neuro-fuzzy optimization

MOTIVATION: It is attempted to improve the speed and flexibility of protein motif identification. The proposed algorithm is able to extract both rigid and flexible protein motifs. RESULTS: In this work, we present a new algorithm for extracting the consensus pattern, or motif, from a group of related protein sequences. This algorithm involves a statistical method to find short patterns with high frequency and then neural network training to optimize the final classification accuracies. Fuzzy logic is used to increase the flexibility of protein motifs. C2H2 Zinc Finger Protein and epidermal growth factor protein sequences are used to demonstrate the capability of the proposed algorithm in finding motifs. AVAILABILITY: This program is freely available for academic use by request.     

An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets

With the recent exponential increase in protein phosphorylation sites identified by mass spectrometry, a unique opportunity has arisen to understand the motifs surrounding such sites. Here we present an algorithm designed to extract motifs from large data sets of naturally occurring phosphorylation sites. The methodology relies on the intrinsic alignment of phospho-residues and the extraction of motifs through iterative comparison to a dynamic statistical background. Results show the identification of dozens of novel and known phosphorylation motifs from recently published serine, threonine and tyrosine phosphorylation studies. When applied to a linguistic data set to test the versatility of the approach, the algorithm successfully extracted hundreds of language motifs. This method, in addition to shedding light on the consensus sequences of identified and as yet unidentified kinases and modular protein domains, may also eventually be used as a tool to determine potential phosphorylation sites in proteins of interest.     

微卫星及其应用

微卫星是广泛分布于真核生物基因组中的短串联重复序列（1-5bp),具有突变速率快、多态性高等特性,已被广泛应用于生物遗传作图、群体遗传研究、个体间亲缘关系鉴定等方面。简要论述了微卫星的突变、位点的分离、数据的收集、在生物学中的应用及其存在的缺陷。     

Regulatory motif discovery using a population clustering evolutionary algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences.