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KEGG spider: interpretation of genomics data in the context of the global gene metabolic network
Alexey V Antonov, Sabine Dietmann, Hans W Mewes
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-12-r179
Abstract: In the post-genomic era the targets of many experimental studies are complex cell disorders [1-6]. A standard experimental strategy is to compare the genetic/proteomics signatures of cells in normal and anomalous states. As a result, a set of genes with differential activity is delivered. In the next step, the interpretation of identified genes in a model context is required. A widely accepted strategy is to infer biological processes that are most relevant to the analyzed gene list. The inference is based on prior knowledge of individual gene properties, such as gene biological functions or interactions. This common approach is usually referred to as enrichment analysis [7-16].The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for the networks of genes and metabolic compounds. The major component of KEGG is the PATHWAY database, which consists of graphical diagrams of biochemical pathways, including most of the known metabolic pathways. Several available public tools, such as GenMAPP/MAPPfinder [17], PathwayProcessor, and PathwayMiner [18], make use of standard enrichment analysis to find overrepresented global pathways within a gene list. However, for statistical evaluation these tools use only information about gene pathway membership, while information about pathway topology is largely discarded. Additionally, several tools provide visualizations of pathways reported to be enriched [19-21]. Some tools provide visualizations of a gene list in the context of the global metabolic network [22,23], providing, however, no quantitative or statistical analyses. Visual analyses of the graphical representation of the genes on the global metabolic network give only an intuitive feeling that genes are related. Taking into account the density of metabolic networks, one must not underestimate the value of a statistical treatment. Even for randomly generated gene lists, it is possible to connect many of the genes into a metabolic subnetwork through one or t
Identifying the Genetic Variation of Gene Expression Using Gene Sets: Application of Novel Gene Set eQTL Approach to PharmGKB and KEGG  [PDF]
Ryan Abo, Gregory D. Jenkins, Liewei Wang, Brooke L. Fridley
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0043301
Abstract: Genetic variation underlying the regulation of mRNA gene expression in humans may provide key insights into the molecular mechanisms of human traits and complex diseases. Current statistical methods to map genetic variation associated with mRNA gene expression have typically applied standard linkage and/or association methods; however, when genome-wide SNP and mRNA expression data are available performing all pair wise comparisons is computationally burdensome and may not provide optimal power to detect associations. Consideration of different approaches to account for the high dimensionality and multiple testing issues may provide increased efficiency and statistical power. Here we present a novel approach to model and test the association between genetic variation and mRNA gene expression levels in the context of gene sets (GSs) and pathways, referred to as gene set – expression quantitative trait loci analysis (GS-eQTL). The method uses GSs to initially group SNPs and mRNA expression, followed by the application of principal components analysis (PCA) to collapse the variation and reduce the dimensionality within the GSs. We applied GS-eQTL to assess the association between SNP and mRNA expression level data collected from a cell-based model system using PharmGKB and KEGG defined GSs. We observed a large number of significant GS-eQTL associations, in which the most significant associations arose between genetic variation and mRNA expression from the same GS. However, a number of associations involving genetic variation and mRNA expression from different GSs were also identified. Our proposed GS-eQTL method effectively addresses the multiple testing limitations in eQTL studies and provides biological context for SNP-expression associations.
MINE: Module Identification in Networks
Kahn Rhrissorrakrai, Kristin C Gunsalus
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-192
Abstract: MINE outperforms MCODE, CFinder, NEMO, SPICi, and MCL in identifying non-exclusive, high modularity clusters when applied to the C. elegans protein-protein interaction network. The algorithm generally achieves superior geometric accuracy and modularity for annotated functional categories. In comparison with the most closely related algorithm, MCODE, the top clusters identified by MINE are consistently of higher density and MINE is less likely to designate overlapping modules as a single unit. MINE offers a high level of granularity with a small number of adjustable parameters, enabling users to fine-tune cluster results for input networks with differing topological properties.MINE was created in response to the challenge of discovering high quality modules of gene products within highly interconnected biological networks. The algorithm allows a high degree of flexibility and user-customisation of results with few adjustable parameters. MINE outperforms several popular clustering algorithms in identifying modules with high modularity and obtains good overall recall and precision of functional annotations in protein-protein interaction networks from both S. cerevisiae and C. elegans.Many types of molecular and functional associations, such as protein-protein or genetic interactions, can be usefully combined and represented as networks using graphical models. Understanding how molecular complexes and groups of functionally related gene products, or "modules", are organized within molecular interaction networks - both physically and in terms of functional dependencies - can lead to a better understanding of how cellular and developmental processes are coordinated. Because gene products within complexes or modules are expected to physically interact more frequently and to show stronger functional dependencies with each other than with other molecules in their environment, they are expected to share many more linkages in any network representation of functional associatio
MiRank: A bioinformatics tool for gene/miRNA ranking and pathway profiling with TCGA-KEGG data sets  [PDF]
Siddharth G. Reddy,Weimin Xiao,Preethi H. Gunaratne
Quantitative Biology , 2012,
Abstract: The Cancer Genome Atlas (TCGA) provides researchers with clinicopathological data and genomic characterizations of various carcinomas. These data sets include expression microarrays for genes and microRNAs -- short, non-coding strands of RNA that downregulate gene expression through RNA interference -- as well as days_to_death and days_to_last_followup fields for each tumor sample. Our aim is to develop a software tool that screens TCGA data sets for genes/miRNAs with functional involvement in specific cancers. Furthermore, our computational pipeline is intended to produce a set of visualizations, or profiles, that place our screened outputs in a pathway-centric context. We accomplish our 'screening' by ranking genes/miRNAs by the correlation of their expression misregulation with differential patient survival. In other words, if a gene/miRNA is consistently misregulated in patients with poor survival rates and, on the other hand, is expressed more 'normally' in patients with longer survival rates, then it is ranked highly; if its misregulation has no such correlation with good/bad survival in patients, then its rank is low. Our pathway profiling pipeline produces several outputs, which allow us to examine the functional roles played by highly ranked genes discovered by our screening. Running the OV (ovarian serous cystadenocarcinoma) data set through our analysis pipeline, we find that several highly ranked pathways and functional groups of genes (VEGF, Jun, Fos, etc.) have already been shown to play some part in the development of epithelial ovarian carcinomas. We also observe that the dysfunction of the Wnt signaling pathway, which regulates cell-fate specification and progenitor cell differentiation, has a disproportionate impact on the survival of ovarian cancer patients.
Integrative analysis of gene expression and phenotype data  [PDF]
Min Xu
Quantitative Biology , 2015,
Abstract: The linking genotype to phenotype is the fundamental aim of modern genetics. We focus on study of links between gene expression data and phenotype data through integrative analysis. We propose three approaches. 1) The inherent complexity of phenotypes makes high-throughput phenotype profiling a very difficult and laborious process. We propose a method of automated multi-dimensional profiling which uses gene expression similarity. Large-scale analysis show that our method can provide robust profiling that reveals different phenotypic aspects of samples. This profiling technique is also capable of interpolation and extrapolation beyond the phenotype information given in training data. It can be used in many applications, including facilitating experimental design and detecting confounding factors. 2) Phenotype association analysis problems are complicated by small sample size and high dimensionality. Consequently, phenotype-associated gene subsets obtained from training data are very sensitive to selection of training samples, and the constructed sample phenotype classifiers tend to have poor generalization properties. To eliminate these obstacles, we propose a novel approach that generates sequences of increasingly discriminative gene cluster combinations. Our experiments on both simulated and real datasets show robust and accurate classification performance. 3) Many complex phenotypes, such as cancer, are the product of not only gene expression, but also gene interaction. We propose an integrative approach to find gene network modules that activate under different phenotype conditions. Using our method, we discovered cancer subtype-specific network modules, as well as the ways in which these modules coordinate. In particular, we detected a breast-cancer specific tumor suppressor network module with a hub gene, PDGFRL, which may play an important role in this module.
Relativity of Gene Expression and Co-regulated Gene Patterns in Feature KEGG Pathways
特征代谢通路上的基因表达相关性及共调控表达模式

Lin Hu,Weiying Zheng,Hong Liu,Hui Lin,Lei Gao,
华琳
,郑卫英,刘红,林慧,高磊

生物工程学报 , 2008,
Abstract: We revealed the feature pathways by computing the classification error rates of out-of-bag (OOB) by random forests combined with pathway analysis. At each feature pathway, the relativity of gene expression was studied and the co-regulated gene patterns under different experiment conditions were analyzed by MAP (Mining attribute profile) algorithm. The discovered patterns were also clustered by the average-linkage hierarchical clustering technique. The results showed that the expression of genes at the same pathway was similar. The co-regulated patterns were found in two feature pathways of which one contained 108 patterns and the other contained 1 pattern. The results of clusters showed that the smallest Pearson coefficient of the clusters was more than 0.623, indicating that the co-regulated patterns in different experiment conditions were more similar at the same KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway. The methods can provide biological insight into the study of microarray data.
Construction of comprehensive gene network for human mitochondria
Jie Li,Song Gao,Jin Wang,ChenYu Zhang
Chinese Science Bulletin , 2010, DOI: 10.1007/s11434-010-3028-4
Abstract: By combining the knowledge from KEGG database and literatures, we construct a comprehensive gene network for human mitochondria. The network comprises 2442 genes of 9 functional categories, including metabolism, development, immune, and apoptosis, etc. Topological analysis reveals that the network is scale free. The hubs of high degrees are mostly the genes of apoptosis. Three big modules are found in the network, which represent development and cell growth, metabolism, immune and apoptosis respectively, suggesting the multiplicity of functions and the complexity of gene regulation in mitochondria.
Analysis of Gene expression in soybean (Glycine max) roots in response to the root knot nematode Meloidogyne incognita using microarrays and KEGG pathways
Heba MM Ibrahim, Parsa Hosseini, Nadim W Alkharouf, Ebtissam HA Hussein, Abd El Kader Y Gamal El-Din, Mohammed AM Aly, Benjamin F Matthews
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-220
Abstract: We examined the expression of soybean (Glycine max) genes in galls formed in roots by the root-knot nematode, Meloidogyne incognita, 12 days and 10 weeks after infection to understand the effects of infection of roots by M. incognita. Gene expression was monitored using the Affymetrix Soybean GeneChip containing 37,500 G. max probe sets. Gene expression patterns were integrated with biochemical pathways from the Kyoto Encyclopedia of Genes and Genomes using PAICE software. Genes encoding enzymes involved in carbohydrate and cell wall metabolism, cell cycle control and plant defense were altered.A number of different soybean genes were identified that were differentially expressed which provided insights into the interaction between M. incognita and soybean and into the formation and maintenance of giant cells. Some of these genes may be candidates for broadening plants resistance to root-knot nematode through over-expression or silencing and require further examination.Plant parasitic nematodes cause about US $100 billion in crop losses annually [1,2]. Root-knot nematodes (RKN; Meloidogyne spp.) are sedentary endoparasites. The most economically important species are Meloidogyne incognita and M. arenaria. Both are widespread and are considered as major crop pathogens worldwide. The RKN can be easily recognized by the "knots" or "galls" that form where they feed on roots [3,4]. These nematodes cause dramatic morphological and physiological changes in plant cells. Some plant genes are subverted by nematodes to establish feeding cells, and transcripts of several nematode genes were identified during infection [5]. Root-knot nematode damage to soybean (Glycine max) can be severe, especially when fields previously planted in cotton are rotated into soybean [6].The RKN life cycle is complex [for review see: [3-5,7]]. The egg is laid in the soil or in plant tissues. The first stage juvenile develops inside the egg and molts one time to the second-stage juvenile (J2). When
Precise generation of systems biology models from KEGG pathways
Clemens Wrzodek, Finja Büchel, Manuel Ruff, Andreas Dr?ger, Andreas Zell
BMC Systems Biology , 2013, DOI: 10.1186/1752-0509-7-15
Abstract: Here, we present a precise method for processing and converting KEGG pathways into initial metabolic and signaling models encoded in the standardized community pathway formats SBML (Levels 2 and 3) and BioPAX (Levels 2 and 3). This method involves correcting invalid or incomplete KGML content, creating complete and valid stoichiometric reactions, translating relations to signaling models and augmenting the pathway content with various information, such as cross-references to Entrez Gene, OMIM, UniProt ChEBI, and many more. Finally, we compare several existing conversion tools for KEGG pathways and show that the conversion from KEGG to BioPAX does not involve a loss of information, whilst lossless translations to SBML can only be performed using SBML Level 3, including its recently proposed qualitative models and groups extension packages.Building correct BioPAX and SBML signaling models from the KEGG database is a unique characteristic of the proposed method. Further, there is no other approach that is able to appropriately construct metabolic models from KEGG pathways, including correct reactions with stoichiometry. The resulting initial models, which contain valid and comprehensive SBML or BioPAX code and a multitude of cross-references, lay the foundation to facilitate further modeling steps.
Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis
Jin Gu, Yang Chen, Shao Li, Yanda Li
BMC Systems Biology , 2010, DOI: 10.1186/1752-0509-4-47
Abstract: ClustEx, a two-step method based on the new formulation, was developed and applied to identify the responsive gene modules of human umbilical vein endothelial cells (HUVECs) in inflammation and angiogenesis models by integrating the time-course microarray data and genome-wide PPI data. It shows better performance than several available module identification tools by testing on the reference responsive gene sets. Gene set analysis of KEGG pathways, GO terms and microRNAs (miRNAs) target gene sets further supports the ClustEx predictions.Taking the closely-connected and co-expressed DE genes in the condition-specific gene network as the signatures of the underlying responsive gene modules provides a new strategy to solve the module identification problem. The identified responsive gene modules of HUVECs and the corresponding enriched pathways/miRNAs provide useful resources for understanding the inflammatory and angiogenic responses of vascular systems.Understanding of cell responses to environmental stimuli is one of the central tasks of molecular biology. Genome-wide gene expression profiling techniques, such as microarray and deep sequencing, are widely used to identify the responsive genes whose expressions are significantly changed after the stimulus. But identifying the responsive genes by differential expressions does not consider the complex gene-gene interactions or regulation information. Increasing evidences suggest that cell responses are usually organized as pathways or responsive gene modules consisting of a group of interacted genes at the molecular level [1-4]. Identification of the responsive gene modules rather than independent responsive genes can provide better understanding of the underlying molecular mechanisms. With the increasing content of the gene-gene interaction databases, such as protein-protein interaction (PPI) databases and pathway databases, several methods have been developed to identify the responsive gene modules by finding an activ
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