A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks

Background

We previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm.

Results

We describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.

Conclusions

The DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.     

Extracting gene networks for low-dose radiation using graph theoretical algorithms

Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., “guilt-by-association”). We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response.     

Disease gene identification by using graph kernels and Markov random fields

Genes associated with similar diseases are often functionally related. This principle is largely supported by many biological data sources, such as disease phenotype similarities, protein complexes, protein-protein interactions, pathways and gene expression profiles. Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases. To capture the gene-disease associations based on biological networks, a kernel-based MRF method is proposed by combining graph kernels and the Markov random field (MRF) method. In the proposed method, three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks, respectively, and a novel weighted MRF method is developed to integrate those data. In addition, an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm, achieving an AUC score of 0.771 when integrating all those biological data in our experiments, which indicates that our proposed method is very promising compared with many existing methods.     

Discovering statistically significant biclusters in gene expression data

In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).     

Shortest path analysis using partial correlations for classifying gene functions from gene expression data

MOTIVATION: Gaussian graphical models (GGMs) are a popular tool for representing gene association structures. We propose using estimated partial correlations from these models to attach lengths to the edges of the GGM, where the length of an edge is inversely related to the partial correlation between the gene pair. Graphical lasso is used to fit the GGMs and obtain partial correlations. The shortest paths between pairs of genes are found. Where terminal genes have the same biological function intermediate genes on the path are classified as having the same function. We validate the method using genes of known function using the Rosetta Compendium of yeast (Saccharomyces Cerevisiae) gene expression profiles. We also compare our results with those obtained using a graph constructed using correlations. RESULTS: Using a partial correlation graph, we are able to classify approximately twice as many genes to the same level of accuracy as when using a correlation graph. More importantly when both methods are tuned to classify a similar number of genes, the partial correlation approach can increase the accuracy of the classifications.     

鲍曼不动杆菌无痕基因敲除及hcp基因相关功能

【背景】鲍曼不动杆菌耐药严重,基因敲除是研究细菌毒力与耐药的重要方式。但现有的大部分细菌基因敲除方法基于抗生素抗性筛选,导致不适用于多重耐药菌株的基因敲除。【目的】旨在建立一种非依赖于抗生素抗性筛选的方法,用于敲除多重耐药鲍曼不动杆菌基因。【方法】运用同源重组和自杀载体pMo130-TelR对亚碲酸钾的抗性,使用两步筛选法,构建鲍曼不动杆菌VI型分泌系统溶血素共调节蛋白(Hemolysin-Coregulated Protein,Hcp)基因敲除突变体,并对缺失株的生长能力、细菌竞争能力以及血清抵抗能力进行测试。【结果】通过构建含同源片段的重组pMo130-TelR载体,成功敲除了鲍曼不动杆菌标准株ATCC 17978中的hcp基因,获得了ATCC 17978 hcp基因缺失突变体。突变体生长能力没有显著改变,但细菌竞争能力显著下降,血清抵抗能力显著升高。【结论】pMo130-TelR可成功用于鲍曼不动杆菌无痕基因敲除,对于研究鲍曼不动杆菌的耐药机制等相关问题具有深远意义。     

Annotating gene function by combining expression data with a modular gene network

MOTIVATION: A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks. RESULTS: We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.     

Deleteagene: a fast neutron deletion mutagenesis-based gene knockout system for plants

Deleteagene(trade mark) (Delete-a-gene) is a deletion-based gene knockout system for plants. To obtain deletion mutants for a specific gene, random deletion libraries created by fast neutron mutagenesis are screened by polymerase chain reaction (PCR) using primers flanking the target gene. By adjusting the PCR extension time to preferentially amplify the deletion alleles, deletion mutants can be identified in pools of DNA samples with each sample representing more than a thousand mutant lines. In Arabidopsis, knockout plants for greater than 80% of targeted genes have been obtained from a population of 51 840 lines. A large number of deletion mutants have been identified and multiple deletion alleles are often recovered for targeted loci. In Arabidopsis, the method is very useful for targeting small genes and can be used to find deletion mutants mutating two or three tandem homologous genes. In addition, the method is demonstrated to be effective in rice as a deletion mutant for a rice gene was obtained with a similar approach. Because fast neutron mutagenesis is applicable to all plant genetic systems, Deleteagene(trade mark) has the potential to enable reverse genetics for a wide range of plant species.