Online Community Detection for Large Complex Networks

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm''s running time is less than the commonly used Louvain algorithm while it gives competitive performance.     

Probing the Topological Properties of Complex Networks Modeling Short Written Texts

In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well—many informative discoveries have been made this way—but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyses performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks.     

Silencing signals in plants: a long journey for small RNAs

Recent research shows that short RNA molecules act as mobile signals that direct mRNA cleavage and DNA methylation in recipient cells.     

阿格蛋白2:RNA诱导基因沉默复合物的切片机

RNA干扰是生物体基因表达调节的一种重要方式,由RNA诱导的基因沉默复合物（RISC）来介导完成,这个复合物中除了微小RNA外,最新研究表明阿格蛋白2是其中的主要应答元件,它本身具有核酸内切酶活性,可以有效启动mRNA的剪切反应而实现对基因表达的调节,这个进展使我们对RNA干扰过程有了更为详尽的理解。     

植物病毒诱导的基因沉默在基因功能研究中的应用

传统的植物遗传转化方法周期长、工作量大、过程繁琐,不利于基因功能的快速高通量鉴定．近年来随着基因沉默机制研究的深入和不断发展,利用病毒诱导的基因沉默(Virus induced gene silencing,VIGS)进行植物功能基因组研究作为一种快速、高通量的反向遗传学工具已被广泛应用在烟草、马铃薯、番茄等植物中, 在大规模的植物基因组功能鉴定中展示了广阔的应用前景．综述了 VIGS 的作用机制、植物病毒栽体、转化方法以及在植物基因功能研究等方面的应用及前景．     

Estimating Mean First Passage Time of Biased Random Walks with Short Relaxation Time on Complex Networks

Biased random walk has been studied extensively over the past decade especially in the transport and communication networks communities. The mean first passage time (MFPT) of a biased random walk is an important performance indicator in those domains. While the fundamental matrix approach gives precise solution to MFPT, the computation is expensive and the solution lacks interpretability. Other approaches based on the Mean Field Theory relate MFPT to the node degree alone. However, nodes with the same degree may have very different local weight distribution, which may result in vastly different MFPT. We derive an approximate bound to the MFPT of biased random walk with short relaxation time on complex network where the biases are controlled by arbitrarily assigned node weights. We show that the MFPT of a node in this general case is closely related to not only its node degree, but also its local weight distribution. The MFPTs obtained from computer simulations also agree with the new theoretical analysis. Our result enables fast estimation of MFPT, which is useful especially to differentiate between nodes that have very different local node weight distribution even though they share the same node degrees.     

NEXCADE: Perturbation Analysis for Complex Networks

Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the 'robust, yet fragile' nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS) can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.     

A Vector Library for Silencing Central Carbon Metabolism Genes with Antisense RNAs in Escherichia coli

We describe here the construction of a series of 71 vectors to silence central carbon metabolism genes in Escherichia coli. The vectors inducibly express antisense RNAs called paired-terminus antisense RNAs, which have a higher silencing efficacy than ordinary antisense RNAs. By measuring mRNA amounts, measuring activities of target proteins, or observing specific phenotypes, it was confirmed that all the vectors were able to silence the expression of target genes efficiently. Using this vector set, each of the central carbon metabolism genes was silenced individually, and the accumulation of metabolites was investigated. We were able to obtain accurate information on ways to increase the production of pyruvate, an industrially valuable compound, from the silencing results. Furthermore, the experimental results of pyruvate accumulation were compared to in silico predictions, and both sets of results were consistent. Compared to the gene disruption approach, the silencing approach has an advantage in that any E. coli strain can be used and multiple gene silencing is easily possible in any combination.     

Micro-Macro Analysis of Complex Networks

Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a “classic” approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail (“micro”) to a different scale level (“macro”), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability.     

Small RNAs, RNAi and the Inheritance of Gene Silencing in Caenorhabditis elegans

Invasive nucleic acids such as transposons and viruses usually exhibit aberrant characteristics, e.g., unpaired DNA or abnormal double-stranded RNA. Organisms employ a variety of strategies to defend themselves by distinguishing self and nonself substances and disabling these invasive nucleic acids. Furthermore, they have developed ways to remember this exposure to invaders and transmit the experience to their descendants. The mechanism underlying this inheritance has remained elusive. Recent research has shed light on the initiation and maintenance of RNA-mediated inherited gene silencing. Small regulatory RNAs play a variety of crucial roles in organisms, including gene regulation, developmental timing, antiviral defense, and genome integrity, via a process termed as RNA interference (RNAi). Recent research has revealed that small RNAs and the RNAi machinery are engaged in establishing and promoting transgenerational gene silencing. Small RNAs direct the RNAi and chromatin modification machinery to the cognate nucleic acids to regulate gene expression and epigenetic alterations. Notably, these acquired small RNAs and epigenetic changes persist and are transmitted from parents to offspring for multiple generations. Thus, RNAi is a vital determinant of the inheritance of gene silencing and acts as a driving force of evolution.     

植物转基因位置依赖性沉默与位置效应

植物基因组能够识别外源基因的出现及所整合的特异位置并产生相应反应,通过分析嘧啶甲基化的信号及模式,比较不同表达水平转基因的整合位点及基因组环境差异,植物转基因位置依赖性沉默和位置效应的机理得到进一步揭示;讨论了位置效应的研究方法和克服策略。     

植物转基因沉默研究进展、对策及应用

转基因沉默 (ｔｒａｎｓｇｅｎｅｓｉｌｅｎｃｉｎｇ)是导入并整合进受体基因组中的外源基因在当代转化体或其后代中表达受到抑制的现象。自Ｐｅｅｒｂｏｌｔｅ在 1986年首次报道发现转基因沉默现象以来 ,相关报道不断发表。如今 ,转基因沉默现象已成为遗传转化技术实用化 ,商品化过程中的巨大障碍。从九十年代开始 ,人们就致力于转基因沉默方面的研究。随着研究的不断深入 ,转基因沉默的各种机理不断被揭示 ,并研究出相应的对策。1.植物转基因沉默研究进展转基因沉默的各种机理都涉及到各种核酸之间的相互作用 ,包括转录水平的基因沉默 (ｔ…     

Supervised Learning Classification Models for Prediction of Plant Virus Encoded RNA Silencing Suppressors

Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a freely accessible web server pVsupPred at http://bioinfo.icgeb.res.in/pvsup/.