基于功能一致性预测冠心病致病基因

目的:为了解疾病致病机理和改进临床治疗,基于功能一致性挖掘潜在的疾病致病基因.方法:本文基于功能一致性基因的共定位特性,结合蛋白质互作网络拓扑结构,获取疾病候选基因集,并通过GO及KEGG功能富集分析方法进一步筛选,预测出新的致病基因.结果:挖掘得到的59个冠心病致病基因通过文献证实绝大部分基因与疾病的发生发展存在着联系.结论:本方法具有可行性,研究者能够在此基础上很好地进行疾病致病机理的研究.     

基于整合网络和联合策略预测心肌梗死的新致病基因

目的:基于整合网络和联合策略预测心肌梗死的新致病基因.方法:从系统生物学的角度,提出基于蛋白质亚细胞定位信息,构建区域化的蛋白质互作的整合网络;通过疾病风险基因与已知致病基因的功能一致性程度和互作相关性的强度联合筛选的新策略,预测心肌梗死的新致病基因.结果:预测出10个心肌梗死的新致病基因(CCL19、CCL25、COMP、CCL11、CCL7、F2、KLKB1、HTR6、ADRB1、BDKRB2),其中8个基因(CCL 19、CCL25、CCL11、CCL7、F2、KLKB1、ADRB1、BDKRB2)经文献证实与心肌梗死的发生发展有着密切的联系;另外2个基因(COMP、HTR6)尚需实验验证.结论:基于整合网络和联合策略预测出10个心肌梗死的新致病基因,此方法为探索复疾病的致病基因提供了新的思路,有助于阐明复杂疾病的致病机理.     

男性性腺功能减退症的致病基因分析

[目的]通过全外显子组测序(WES)技术筛选男性性腺功能减退症的致病基因,并对基因突变位点进行生物信息学分析。[方法]收集5例男性性腺功能减退症患者临床及遗传学检测资料。采用WES技术筛选相关致病基因,并通过PCR扩增、Sanger测序以及生物信息学分析等验证突变位点。[结果]先证者1为PROKR2基因c.533G>C(p.W178S)纯合突变,家系验证结果发现其父母均为PROKR2基因c.533G>C(p.W178S)杂合突变携带者,符合常染色体隐性遗传。先证者2为ZFPM2基因c.1498C>G(p.Q500E)杂合突变,生物信息学分析发现,该突变位点编码的氨基酸在不同物种中高度保守,并在人类外显子数据库、参考人群千人基因组1000G、SNP数据库及人群基因组突变频率数据库中未发现该突变位点,该突变经SIFT、Polyphen2和Mutation Taster软件预测结果均为有害。[结论]PROKR2基因c.533G>C(p.W178S)和ZFPM2基因c.1498C>G(p.Q500E)突变可能是男性性腺功能减退症的致病原因。     

hsa-miR-192-3p的靶基因预测及功能分析

通过生物信息学方法预测hsa-miR-192-3p的靶基因及其靶基因的可能功能。首先通过miRbase在线数据库对hsamiR-192-3p的碱基序列及序列在各物种间的保守性进行分析,再通过miRGator v3. 0在线数据库查看hsa-miR-192-3p在各个组织器官中的表达丰富度情况;其次,应用Target Scan和miRanda在线数据库预测hsa-miR-192-3p的靶基因;最后,将预测得到的两个数据库的靶基因交集用DAVID在线数据库进行功能富集分析和信号转导通路富集分析。结果表明:hsa-miR-192-3p在人、家鼠、猕猴等生物中存在高度保守性; hsa-miR-192-3p在胃肠道、肾脏、肝胆系统、干细胞、鼻、脾、胸腺中表达丰富度较高;通过两个靶基因预测软件预测的靶基因取交集后共有190个;功能富集分析发现hsa-miR-192-3p靶基因富集在细胞质、细胞核、质膜、高尔基体等15个细胞组件(p0. 05),参与蛋白结合、GTP酶活性、锌离子跨膜转运蛋白活性等7个分子功能(p0. 05),涉及金属离子运输、RNA聚合酶II启动子的转录阳性调控、基因表达调节、钙离子跨膜运输、胚胎发育等18个生物过程(p0. 05);预测靶基因集合显著富集于癌症通路与催乳素信号通路中(p0. 05)。得出结论:hsa-miR-192-3p预测的靶基因集合富集于多个生物过程,与肿瘤密切相关,生物信息预测为今后的研究奠定了一定的理论基础,为后续实验验证提供了研究方向。     

蛇苔过氧化物酶基因的序列分析与功能预测

过氧化物酶在生物界分布极广,在细胞代谢的氧化还原过程中起重要的作用.为深入研究蛇苔过氧化物酶的理化性质及功能,对其同源性,亚细胞定位,结构和功能等进行了生物信息学预测和分析.结果表明;该序列有1 050 bp长的开放阅读框,编码349个氨基酸,与拟南芥等的POD相似性较高(相似性为71％).预测R68、F71、H72、...     

基于扩展起始节点和加权融合策略预测肺癌风险致病基因

肺癌风险致病基因预测有助于了解疾病发病机制、提高临床治疗效果.目前,以重启游走为框架的风险致病基因预测算法,普遍存在起始节点少、节点转移概率相同、信息源单一的问题.为此,本文提出一种基于扩展起始节点和加权融合策略的风险致病基因预测算法(命名为AFMFSC),并在肺癌中验证算法有效性.首先,基于增广模糊测量思想,计算疾病表型近似基因间的增广功能相似得分,从中选出重要基因与致病基因作为扩展起始节点;其次,采用节点拓扑相似度转移矩阵及基因表达差异相关性转移矩阵,分别在蛋白质网络中重启随机游走,并将两种结果加权融合排序;最后,通过富集分析排名靠前基因,得到有显著意义的风险致病基因.AFMFSC算法预测的73个肺癌风险致病基因,均与肺癌发生、发展有密切联系,生物学意义显著.与其他排序算法相比,AFMFSC算法的Top 1%、Top 5%和AUC值比较大,平均排名和受拓扑特性偏差影响程度小;融合策略排名性能优于单一转移矩阵或普通邻接矩阵游走排名.AFMFSC算法不仅能准确有效地预测肺癌风险致病基因,而且可推广预测其他疾病风险致病基因,为探索癌症致病机理提供新视角及依据.     

肌动蛋白结合蛋白2(Transgelin-2)生物学特性和功能研究进展

肌动蛋白结合蛋白是指能与肌动蛋白的单体、多聚体等结合的蛋白,肌动蛋白结合蛋白2(Transgelin-2)作为一种重要的肌动蛋白结合蛋白,可广泛分布在平滑肌细胞及非平滑肌细胞。Transgelin-2基因可广泛表达在全身各组织器官,其在亚细胞层面上有多种定位,且在不同病理生理状态下可能存在定位转移。Transgelin-2蛋白被认为参与了多种恶性肿瘤疾病,可能是特异性肿瘤标志物。本文对Transgelin-2蛋白的特性、亚细胞定位和相应生物学功能进行了综述,以期为相关机制研究提供可能的判断依据。     

基于氨基酸约化和统计特征的蛋白质亚细胞定位预测

蛋白质亚细胞定位预测对蛋白质的功能、相互作用及调控机制的研究具有重要意义。本文基于物化性质和结构性质对氨基酸的约化,描述序列局部和全局信息的"组成"、"转换"和"分布"特征,并利用氨基酸亲疏水性的数值统计特征,提出了一种新的蛋白质特征表示方法(NSBH)。分别使用三种分类器KNN、SVM及BP神经网络进行蛋白质亚细胞定位预测,比较了几种方法和特征融合方法的预测结果,显示融合特征表示及结合SVM分类器时能够达到更好的预测准确率。同时,还详细讨论了不同参数对实验结果的影响,具体的实验及比较结果显示了该方法的有效性。     

金花茶类黄酮糖基转移酶基因的克隆和功能初步研究

类黄酮糖基转移酶(UDP flavonoid glycosyltransferase, UFGT)催化黄酮醇、花青素等形成稳定的糖苷,是黄酮醇苷、花青素苷合成的最后一步反应的关键。该研究以金花茶的花瓣为材料,采用PCR扩增的方法,获得了2个金花茶转录组中筛选到的类黄酮糖基转移酶基因。结果显示：(1)CnUFGT14基因(登录号为MT370521)全长1 562 bp,开放阅读框1 380 bp,编码459个氨基酸;CnUFGT15基因(登录号为MT370520)全长1 546 bp,开放阅读框1 368 bp,编码455个氨基酸;两个蛋白序列均具有UFGT蛋白特有的 PSPG 保守区域。(2)系统进化树分析发现,CnUFGT14和CnUFGT15分别与茶树UFGT78A14和UFGT78A15亲缘关系最近。(3) 荧光定量PCR分析发现,CnUFGT14基因的表达量与多种多酚组分的含量呈正相关,CnUFGT15基因的表达量与花瓣黄酮醇、多酚等的含量相关性不显著。(4)亚细胞定位研究发现,CnUFGT14、CnUFGT15蛋白在细胞核膜、细胞质、细胞膜部位均呈现明显的定位。(5)叶盘法转化烟草发现,CnUFGT14基因表达量较高的转基因株系中总多酚含量及多种多酚组分含量升高,而CnUFGT15基因的转基因株系中黄酮、多酚组分变化不显著。研究表明,CnUFGT14基因具有促进多酚合成的作用,而CnUFGT15基因对类黄酮通路不具有明显作用。     

Predicting candidate genes based on combined network topological features: a case study in coronary artery disease

Predicting candidate genes using gene expression profiles and unbiased protein-protein interactions (PPI) contributes a lot in deciphering the pathogenesis of complex diseases. Recent studies showed that there are significant disparities in network topological features between non-disease and disease genes in protein-protein interaction settings. Integrated methods could consider their characteristics comprehensively in a biological network. In this study, we introduce a novel computational method, based on combined network topological features, to construct a combined classifier and then use it to predict candidate genes for coronary artery diseases (CAD). As a result, 276 novel candidate genes were predicted and were found to share similar functions to known disease genes. The majority of the candidate genes were cross-validated by other three methods. Our method will be useful in the search for candidate genes of other diseases.     

Identifying and prioritizing disease-related genes based on the network topological features

Identifying and prioritizing disease-related genes are the most important steps for understanding the pathogenesis and discovering the therapeutic targets. The experimental examination of these genes is very expensive and laborious, and usually has a higher false positive rate. Therefore, it is highly desirable to develop computational methods for the identification and prioritization of disease-related genes. In this study, we develop a powerful method to identify and prioritize candidate disease genes. The novel network topological features with local and global information are proposed and adopted to characterize genes. The performance of these novel features is verified based on the 10-fold cross-validation test and leave-one-out cross-validation test. The proposed features are compared with the published features, and fused strategy is investigated by combining the current features with the published features. And, these combination features are also utilized to identify and prioritize Parkinson's disease-related genes. The results indicate that identified genes are highly related to some molecular process and biological function, which provides new clues for researching pathogenesis of Parkinson's disease. The source code of Matlab is freely available on request from the authors.     

Predicting microRNA-disease association based on microRNA structural and functional similarity network

Background: Increasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases. Inferring disease-related miRNAs can be helpful in promoting disease biomarker detection for the treatment, diagnosis, and prevention of complex diseases. Methods: To improve the prediction accuracy of miRNA-disease association and capture more potential disease-related miRNAs, we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures, families, and functions. Results: We tested the network on the classical algorithms: WBSMDA and RWRMDA through the method of leave-one-out cross-validation. Eventually, AUCs of 0.8212 and 0.9657 are obtained, respectively. Also, the proposed MSFSN is applied to three cancers for breast neoplasms, hepatocellular carcinoma, and prostate neoplasms. Consequently, 82%, 76%, and 82% of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA-disease associations database miR2Disease and oncomiRDB. Conclusion: Therefore, MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models.     

Predicting potential cancer genes by integrating network properties,sequence features and functional annotations

The discovery of novel cancer genes is one of the main goals in cancer research.Bioinformatics methods can be used to accelerate cancer gene discovery,which may help in the understanding of cancer and the development of drug targets.In this paper,we describe a classifier to predict potential cancer genes that we have developed by integrating multiple biological evidence,including protein-protein interaction network properties,and sequence and functional features.We detected 55 features that were significantly different between cancer genes and non-cancer genes.Fourteen cancer-associated features were chosen to train the classifier.Four machine learning methods,logistic regression,support vector machines(SVMs),BayesNet and decision tree,were explored in the classifier models to distinguish cancer genes from non-cancer genes.The prediction power of the different models was evaluated by 5-fold cross-validation.The area under the receiver operating characteristic curve for logistic regression,SVM,Baysnet and J48 tree models was 0.834,0.740,0.800 and 0.782,respectively.Finally,the logistic regression classifier with multiple biological features was applied to the genes in the Entrez database,and 1976 cancer gene candidates were identified.We found that the integrated prediction model performed much better than the models based on the individual biological evidence,and the network and functional features had stronger powers than the sequence features in predicting cancer genes.     

Predicting potential cancer genes by integrating network properties, sequence features and functional annotations

The discovery of novel cancer genes is one of the main goals in cancer research. Bioinformatics methods can be used to accelerate cancer gene discovery, which may help in the understanding of cancer and the development of drug targets. In this paper, we describe a classifier to predict potential cancer genes that we have developed by integrating multiple biological evidence, including protein-protein interaction network properties, and sequence and functional features. We detected 55 features that were significantly different between cancer genes and non-cancer genes. Fourteen cancer-associated features were chosen to train the classifier. Four machine learning methods, logistic regression, support vector machines (SVMs), BayesNet and decision tree, were explored in the classifier models to distinguish cancer genes from non-cancer genes. The prediction power of the different models was evaluated by 5-fold cross-validation. The area under the receiver operating characteristic curve for logistic regression, SVM, Baysnet and J48 tree models was 0.834, 0.740, 0.800 and 0.782, respectively. Finally, the logistic regression classifier with multiple biological features was applied to the genes in the Entrez database, and 1976 cancer gene candidates were identified. We found that the integrated prediction model performed much better than the models based on the individual biological evidence, and the network and functional features had stronger powers than the sequence features in predicting cancer genes.     

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.     

Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity

Background: MicroRNAs (miRNAs) are a significant type of non-coding RNAs, which usually were encoded by endogenous genes with about ~22 nt nucleotides. Accumulating biological experiments have shown that miRNAs have close associations with various human diseases. Although traditional experimental methods achieve great successes in miRNA-disease interaction identification, these methods also have some limitations. Therefore, it is necessary to develop computational method to predict miRNA-disease interactions. Methods: Here, we propose a computational framework (MDVSI) to predict interactions between miRNAs and diseases by integrating miRNA topological similarity and functional similarity. Firstly, the CosRA index is utilized to measure miRNA similarity based on network topological feature. Then, in order to enhance the reliability of miRNA similarity, the functional similarity and CosRA similarity are integrated based on linear weight method. Further, the potential miRNA-disease associations are predicted by using recommendation method. In addition, in order to overcome limitation of recommendation method, for new disease, a new strategy is proposed to predict potential interactions between miRNAs and new disease based on disease functional similarity. Results: To evaluate the performance of different methods, we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art methods. The experimental result shows that MDVSI achieves an AUC of 0.91, which is at least 0.012 higher than other compared methods. Conclusions: In summary, we propose a computational framework (MDSVI) for miRNA-disease interaction prediction. The experiment results demonstrate that it outperforms other the-state-of-the-art methods. Case study shows that it can effectively identify potential miRNA-disease interactions.