巢湖西半湖富营养化时空变化趋势与成因分析

收集整理了巢湖西半湖6个国控监测点1983~2008年（26年）主要富营养化指标TP、TN、CODmn、Chla的监测数据,计算了6个监测点和西半湖总体26年的综合营养状态指数（∑TLI图示）时空变化情况。并用Spearm an秩相关系数分析检验了西半湖总体和6个监测点26年∑TLI年变化趋势。结果表明：按总平均∑TLI排列,6个监测点富营养化由重到轻依次为：南淝河入湖区（66.64）〉塘西（64.93）〉十五里河入湖区（63.35）〉派河入湖区（61.38）〉新河入湖区（59.51）〉西半湖湖心（59.18）;在显著水平0.05和0.01各点∑TLI均有上升趋势,其中十五里河入湖区（R=0.715）、新河入湖区（R=0.824）和西半湖湖心（R=0.811）以及西半湖总体（R=0.512）∑TLI有显著上升趋势,而南淝河入湖区（R=0.192）、塘西（R=0.045）和派河入湖区（R=0.325）上升趋势均不显著。最后在上述研究的基础上,对巢湖西半湖富营养化时空变化的成因进行了简要分析。     

2008年夏季青岛近海浒苔无机元素含量分析

2008年夏季对青岛近海栈桥、汇泉湾和五四广场三个海域的漂浮浒苔进行了样本采集,针对其12种无机元素进行了含量分析与比较,并与2007年夏季三个海域的浒苔的无机元素含量进行了比较。结果表明,2008年采自汇泉湾海域的漂浮浒苔的Ca,Cu,N,Na和P含量在三个海域中最高,而采自五四广场的漂浮浒苔的Fe,Mn,Pb和Zn含量最高,Cd,K和Mg的含量在三个海域的水平相差不大。与2007年相同海域比较,2008年的漂浮浒苔更富含Fe,K,Mg,Mn,Na和P。另外,与海带和紫菜比较,浒苔中的Fe,Mg和Na含量较高,而P和Zn含量较低,Ca和K含量,低于海带而高于紫菜中的含量;有害元素Cd和Pb含量远低于相应的藻类制品卫生标准（GB19643—2005）和无公害产品海藻（NY5056-005）中的限量要求。结果从无机元素角度为浒苔的综合利用提供数据支持。     

弗氏志贺菌磷酸化蛋白的检测

目的:检测弗氏志贺菌全菌蛋白中被磷酸化修饰的蛋白。方法:制取弗氏2a志贺菌2457T野生株全菌磷酸化蛋白样品时加入磷酸化酶抑制剂,随后对样品进行双向电泳,以抗磷酸丝氨酸/苏氨酸/酪氨酸抗体为免疫探针,通过Western印迹找到被磷酸化的蛋白,并进行胶内酶解及MALDI-TOF质谱分析。结果与结论:共检测到13个磷酸化蛋白,其中9个为代谢途径中的酶。     

Polymorphism of SARS-CoV Genomes

In this work, severe acute respiratory syndrome associated coronavirus (SARS-CoV) genome BJ202 (AY864806) was completely sequenced. The genome was directly accessed from the stool sample of a patient in Beijing. Comparative genomics methods were used to analyze the sequence variations of 116 SARS-CoV genomes (including BJ202) available in the NCBI Gen-Bank. With the genome sequence of GZ02 as the reference, there were 41 polymorphic sites identified in BJ202 and a total of 278 polymorphic sites present in at least two of the 116 genomes. The distribution of the polymorphic sites was biased over the whole genome. Nearly half of the variations (50.4%, 140/278) clustered in the one third of the whole genome at the 3′ end (19.0 kb-29.7 kb). Regions encoding Orf10-11, Orf3/4, E, M and S protein had the highest mutation rates. A total of 15 PCR products (about 6.0 kb of the genome) including 11 fragments containing 12 known polymorphic sites and 4 fragments without identified polymorphic sites were cloned and sequenced. Results showed that 3 unique polymorphic sites of BJ202 (positions 13 804, 15 031 and 20 792) along with 3 other polymorphic sites (26 428, 26 477 and 27 243) all contained 2 kinds of nucleotides. It is interesting to find that position 18379 which has not been identified to be polymorphic in any of the other 115 published SARS-CoV genomes is actually a polymorphic site. The nucleotide composition of this site is A (8) to G (6). Among 116 SARS-CoV genomes, 18 types of deletions and 2 insertions were identified. Most of them were related to a 300 bp region (27 700-28 000) which encodes parts of the putative ORF9 and ORF10-11. A phylogenetic tree illustrating the divergence of whole BJ202 genome from 115 other completely sequenced SARS-CoVs was also constructed. BJ202 was phylogeneticly closer to BJ01 and LLJ-2004.     

Predicting protein structural class with AdaBoost Learner

The structural class is an important feature in characterizing the overall topological folding type of a protein or the domains therein. Prediction of protein structural classification has attracted the attention and efforts from many investigators. In this paper a novel predictor, the AdaBoost Learner, was introduced to deal with this problem. The essence of the AdaBoost Learner is that a combination of many 'weak' learning algorithms, each performing just slightly better than a random guessing algorithm, will generate a 'strong' learning algorithm. Demonstration thru jackknife cross-validation on two working datasets constructed by previous investigators indicated that AdaBoost outperformed other predictors such as SVM (support vector machine), a powerful algorithm widely used in biological literatures. It has not escaped our notice that AdaBoost may hold a high potential for improving the quality in predicting the other protein features as well, such as subcellular location and receptor type, among many others. Or at the very least, it will play a complementary role to many of the existing algorithms in this regard.     

Prediction of protease types in a hybridization space

Regulating most physiological processes by controlling the activation, synthesis, and turnover of proteins, proteases play pivotal regulatory roles in conception, birth, digestion, growth, maturation, ageing, and death of all organisms. Different types of proteases have different functions and biological processes. Therefore, it is important for both basic research and drug discovery to consider the following two problems. (1) Given the sequence of a protein, can we identify whether it is a protease or non-protease? (2) If it is, what protease type does it belong to? Although the two problems can be solved by various experimental means, it is both time-consuming and costly to do so. The avalanche of protein sequences generated in the post-genetic era has challenged us to develop an automated method for making a fast and reliable identification. By hybridizing the functional domain composition and pseudo-amino acid composition, we have introduced a new method called "FunD-PseAA predictor" that is operated in a hybridization space. To avoid redundancy and bias, demonstrations were performed on a dataset where none of the proteins has >or=25% sequence identity to any other. The overall success rate thus obtained by the jackknife cross-validation test in identifying protease and non-protease was 92.95%, and that in identifying the protease type was 94.75% among the following six types: (1) aspartic, (2) cysteine, (3) glutamic, (4) metallo, (5) serine, and (6) threonine. Demonstration was also made on an independent dataset, and the corresponding overall success rates were 98.36% and 97.11%, respectively, suggesting the FunD-PseAA predictor is very powerful and may become a useful tool in bioinformatics and proteomics.     

Predicting protein oxidation sites with feature selection and analysis approach

Protein oxidation is a ubiquitous post-translational modification that plays important roles in various physiological and pathological processes. Owing to the fact that protein oxidation can also take place as an experimental artifact or caused by oxygen in the air during the process of sample collection and analysis, and that it is both time-consuming and expensive to determine the protein oxidation sites purely by biochemical experiments, it would be of great benefit to develop in silico methods for rapidly and effectively identifying protein oxidation sites. In this study, we developed a computational method to address this problem. Our method was based on the nearest neighbor algorithm in which, however, the maximum relevance minimum redundancy and incremental feature selection approaches were incorporated. From the initial 735 features, 16 features were selected as the optimal feature set. Of such 16 optimized features, 10 features were associated with the position-specific scoring matrix conservation scores, three with the amino acid factors, one with the propensity of conservation of residues on protein surface, one with the side chain count of carbon atom deviation from mean, and one with the solvent accessibility. It was observed that our prediction model achieved an overall success rate of 75.82%, indicating that it is quite encouraging and promising for practical applications. Also, the 16 optimal features obtained through this study may provide useful clues and insights for in-depth understanding the action mechanism of protein oxidation.     

Otterlace软件在猪全基因组序列人工注释分析中的应用

2009年11月,美、英等国科学家宣布首次绘制出家猪的基因组草图。近两年,随着全基因组序列陆续释放,越来越多的测序片段得到正确拼接组装,从全基因组水平上对猪功能基因进行注释分析显得尤为迫切。文章以丝切蛋白1(Cofilin 1,CFL1)基因的注释过程为例,介绍了运用Sanger研究所开发的Otterlace软件对猪全基因组的免疫基因序列进行人工分析与注释。通过详细说明Zmap、Blixem和Dotter 3个注释工具的使用方法,并给出了注释过程的主要步骤,以期对Otterlace的应用起一个抛砖引玉的作用。运用Otterlace软件对243个免疫相关基因进行分析,其中180个基因得到完整或部分注释,这为后续深入开展这些基因的功能研究奠定了基础。     

Analysis of metabolic pathway using hybrid properties

Given a compounds-forming system, i.e., a system consisting of some compounds and their relationship, can it form a biologically meaningful pathway? It is a fundamental problem in systems biology. Nowadays, a lot of information on different organisms, at both genetic and metabolic levels, has been collected and stored in some specific databases. Based on these data, it is feasible to address such an essential problem. Metabolic pathway is one kind of compounds-forming systems and we analyzed them in yeast by extracting different (biological and graphic) features from each of the 13,736 compounds-forming systems, of which 136 are positive pathways, i.e., known metabolic pathway from KEGG; while 13,600 were negative. Each of these compounds-forming systems was represented by 144 features, of which 88 are graph features and 56 biological features. "Minimum Redundancy Maximum Relevance" and "Incremental Feature Selection" were utilized to analyze these features and 16 optimal features were selected as being able to predict a query compounds- forming system most successfully. It was found through Jackknife cross-validation that the overall success rate of identifying the positive pathways was 74.26%. It is anticipated that this novel approach and encouraging result may give meaningful illumination to investigate this important topic.