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LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network

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BMC BIOINFORMATICS 2016年第SUPPL 17期17卷 221-230页

作者： Wei, Pi-Jing Zhang, Di Xia, Junfeng Zheng, Chun-Hou Anhui Univ Coll Elect Engn & Automat Hefei 230601 Anhui Peoples R China Anhui Univ Coll Comp Sci & Technol Hefei 230601 Anhui Peoples R China Anhui Univ Inst Hlth Sci Hefei 230601 Anhui Peoples R China

Background: Cancer is a complex disease which is characterized by the accumulation of genetic alterations during the patient's lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations. Results: In this paper, in order to identify driver genes effectively, we applied a generalized additive model to mutation profiles to filter genes with long length and constructed a new gene-gene interaction network. Then we integrated the mutation data and expression data into the gene-gene interaction network. Lastly, greedy algorithm was used to prioritize candidate driver genes from the integrated data. We named the proposed method Length-Net-Driver ( LNDriver). Conclusions: Experiments on three TCGA datasets, i.e., head and neck squamous cell carcinoma, kidney renal clear cell carcinoma and thyroid carcinoma, demonstrated that the proposed method was effective. Also, it can identify not only frequently mutated drivers, but also rare candidate driver genes.

关键词： Cancer Driver genes Mutation data expression data Interaction network

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VisExpress: Visual exploration of differential gene expression data

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INFORMATION VISUALIZATION 2017年第1期16卷 48-73页

作者： Simon, Svenja Mittelstaedt, Sebastian Kwon, Bum Chul Stoffel, Andreas Landstorfer, Richard Neuhaus, Klaus Muehlig, Anna Scherer, Siegfried Keim, Daniel A. Univ Konstanz Dept Comp & Informat Sci Box D78 D-78457 Constance Germany Tech Univ Munich Lehrstuhl Mikrobielle Okol Freising Weihenstephan Germany

Biologists are keen to understand how processes in cells react to environmental changes. Differential gene expression analysis allows biologists to explore functions of genes with data generated from different environments. However, these data and analysis lead to unique challenges since tasks are ill-defined, require implicit domain knowledge, comprise large volumes of data, and are, therefore, of explanatory nature. To investigate a scalable visualization-based solution, we conducted a design study with three biologists specialized in differential gene expression analysis. We stress our contributions in three aspects: first, we characterize the problem domain for exploring differential gene expression data and derive task abstractions and design requirements. Second, we investigate the design space and present an interactive visualization system, called VisExpress. Third, we evaluate the usefulness of VisExpress via a Pair Analytics study with real users and real data and report on insights that were gained by our experts with VisExpress.

关键词： Information visualization design study bioinformatics visualization interactive visual exploration biological data expression data gene expression profiling differential gene expression

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Exploiting scale-free information from expression data for cancer classification

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2005年第4期29卷 288-293页

作者： Antonov, AV Tetko, IV Kosykh, D Surmeli, D Mewes, HW GSF Natl Res Ctr Environm & Hlth Inst Bioinformat D-85764 Neuherberg Germany Inst Bioorgan & Petr Chem UA-02094 Kiev Ukraine Tech Univ Munich Dept Genome Oriented Bioinformat Wissensch Zentrum Weihenstephan D-85350 Freising Weihenstephan Germany

Most studies concerning expression data analyses usually exploit information on the variability of gene intensity across samples. This information is sensitive to initial data processing, which affects the final conclusions. However expression data contains scale-free information, which is directly comparable between different samples. We propose to use the pairwise ratio of gene expression values rather than their absolute intensities for a classification of expression data. This information is stable to data processing and thus more attractive for classification analyses. In proposed schema of data analyses only information on relative gene expression levels in each sample is exploited. Testing on publicly available datasets leads to superior classification results. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： cancer classification expression data microarray data scale-free schema genes pairwise ratio

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Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with expression data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第6期14卷 1410-1418页

作者： Gu, Deqing Jian, Xingxing Zhang, Cheng Hua, Qiang East China Univ Sci & Technol State Key Lab Bioreactor Engn Shanghai 200237 Peoples R China Collaborat Innovat Ctr Biomfg Technol Shanghai 200237 Peoples R China

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

关键词： Genome-scale metabolic network models genetic design expression data

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Effective dimensionality for principal component analysis of time series expression data

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BIOSYSTEMS 2003年第3期71卷 311-317页

作者： Hörnquist, M Hertz, J Wahde, M Linkoping Univ Dept Sci & Technol SE-60174 Norrkoping Sweden NORDITA DK-2100 Copenhagen Denmark Chalmers Univ Technol Div Mechatron S-41296 Gothenburg Sweden

Large-scale expression data are today measured for thousands of genes simultaneously. This development has been followed by an exploration of theoretical tools to get as much information out of these data as possible. Several groups have used principal component analysis (PCA) for this task. However, since this approach is data-driven, care must be taken in order not to analyze the noise instead of the data. As a strong warning towards uncritical use of the output from a PCA, we employ a newly developed procedure to judge the effective dimensionality of a specific data set. Although this data set is obtained during the development of rat central nervous system, our finding is a general property of noisy time series data. Based on knowledge of the noise-level for the data, we find that the effective number of dimensions that are meaningful to use in a PCA is much lower than what could be expected from the number of measurements. We attribute this fact both to effects of noise and the lack of independence of the expression levels. Finally, we explore the possibility to increase the dimensionality by performing more measurements within one time series, and conclude that this is not a fruitful approach. (C) 2003 Elsevier Ireland Ltd. All rights reserved.

关键词： PCA dimensionality expression data noise effects

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Incorporating expression data in metabolic modeling: A case study of lactate dehydrogenase

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JOURNAL OF THEORETICAL BIOLOGY 2006年第3期240卷 464-474页

作者： Downer, Joshua Sevinsky, Joel R. Ahn, Natalie G. Resing, Katheryn A. Betterton, M. D. Univ Colorado Dept Math Appl Boulder CO 80309 USA Univ Colorado Dept Chem & Biochem Boulder CO 80309 USA

Integrating biological information from different sources to understand cellular processes is an important problem in systems biology. We use data from mRNA expression arrays and chemical kinetics to formulate a metabolic model relevant to K562 erythroleukemia cells. MAP kinase pathway activation alters the expression of metabolic enzymes in K562 cells. Our array data show changes in expression of lactate dehydrogenase (LDH) isoforms after treatment with phorbol 12-myristate 13-acetate (PMA), which activates MAP kinase signaling. We model the change in lactate production which occurs when the MAP kinase pathway is activated, using a non-equilibrium, chemical-kinetic model of homolactic fermentation. In particular, we examine the role of LDH isoforms, which catalyse the conversion of pyruvate to lactate. Changes in the isoform ratio are not the primary determinant of the production of lactate. Rather, the total concentration of LDH controls the lactate concentration. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： metabolic modeling MAP kinase cell signaling enzyme kinetics expression data lactate dehydrogenase

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Identification of cardiomyopathy-related core genes through human metabolic networks and expression data

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BMC GENOMICS 2022年第1期23卷 1-15页

作者： Rong, Zherou Chen, Hongwei Zhang, Zihan Zhang, Yue Ge, Luanfeng Lv, Zhengyu Zou, Yuqing Lv, Junjie He, Yuehan Li, Wan Chen, Lina Harbin Med Univ Coll Bioinformat Sci & Technol Harbin Heilongjiang Peoples R China

Background Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

关键词： Cardiomyopathy Human metabolic network expression data Module Core genes

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Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data

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BMC MEDICAL GENETICS 2014年第1期15卷 1-8页

作者： Yang, Zuozhang Chen, Yongbin Fu, Yu Yang, Yihao Zhang, Ya Chen, Yanjin Li, Dongqi Kunming Med Univ Tumor Hosp Yunnan Prov Affiliated Hosp 3 Dept OrthopaedBone & Soft Tissue Tumors Res Ctr Kunming 650118 Yunnan Peoples R China Chinese Acad Sci Kunming Inst Zool Key Lab Anim Models & Human Dis Mech Kunming 650223 Yunnan Peoples R China Beijing Hosp Dept Dermatol Beijing 100730 Peoples R China

Background: To uncover the genes involved in the development of osteosarcoma (OS), we performed a meta-analysis of OS microarray data to identify differentially expressed genes (DEGs) and biological functions associated with gene expression changes between OS and normal control (NC) tissues. Methods: We used publicly available GEO datasets of OS to perform a meta-analysis. We performed Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein interaction (PPI) networks analysis. Results: Eight GEO datasets, including 240 samples of OS and 35 samples of controls, were available for the meta-analysis. We identified 979 DEGs across the studies between OS and NC tissues (472 up-regulated and 507 down-regulated). We found GO terms for molecular functions significantly enriched in protein binding (GO: 0005515, P = 3.83E-60) and calcium ion binding (GO: 0005509, P = 3.79E-13), while for biological processes, the enriched GO terms were cell adhesion (GO:0007155, P = 2.26E-19) and negative regulation of apoptotic process (GO: 0043066, P = 3.24E-15), and for cellular component, the enriched GO terms were cytoplasm (GO:0005737, P = 9.18E-63) and extracellular region (GO: 0005576, P = 2.28E-47). The most significant pathway in our KEGG analysis was Focal adhesion (P = 5.70E-15). Furthermore, ECM-receptor interaction (P = 1.27E-13) and Cell cycle (P = 4.53E-11) are found to be highly enriched. PPI network analysis indicated that the significant hub proteins containing PTBP2 (Degree = 33), RGS4 (Degree = 15) and FXYD6 (Degree = 13). Conclusions: Our meta-analysis detected DEGs and biological functions associated with gene expression changes between OS and NC tissues, guiding further identification and treatment for OS.

关键词： Osteosarcoma expression data Meta-analysis Microarray Differentially expressed genes

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Applying Intelligent Computing Techniques to Modeling Biological Networks from expression data

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Genomics, Proteomics & Bioinformatics 2008年第2期6卷 111-120页

作者： Wei-Po Lee Kung-Cheng Yang Department of Information Management National Sun Yat-sen University Kaohsiung Chinese Taipei Department of Management Information Systems National Pingtung University of Science and Technology Pingung Chinese Taipei

Constructing biological networks is one of the most important issues in systems biology. However, constructing a network from data manually takes a considerable large amount of time, therefore an automated procedure is advocated. To automate the procedure of network construction, in this work we use two intelligent computing techniques, genetic programming and neural computation, to infer two kinds of network models that use continuous variables. To verify the presented approaches, experiments have been conducted and the preliminary results show that both approaches can be used to infer networks successfully.

关键词： reverse engineering system modeling genetic programming recurrent neural network expression data

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Visualizing Uncertainty in Biological expression data

Visualizing Uncertainty in Biological Expression Data

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19th SPIE Conference on Visualization and data Analysis (VDA)

作者： Holzhueter, Clemens Lex, Alexander Schmalstieg, Dieter Schulz, Hans-Joerg Schumann, Heidrun Streit, Marc Univ Rostock Dept Comp Graph Rostock Germany Graz Univ Technol Inst Comp Graph & Vis Graz Austria Johannes Kepler Univ Linz Inst Comp Graph Linz Austria

ISBN: (纸本)9780819489418

expression analysis of similar to omics data using microarrays has become a standard procedure in the life sciences. However, microarrays are subject to technical limitations and errors, which render the data gathered likely to be uncertain. While a number of approaches exist to target this uncertainty statistically, it is hardly ever even shown when the data is visualized using for example clustered heatmaps. Yet, this is highly useful when trying not to omit data that is "good enough" for an analysis, which otherwise would be discarded as too unreliable by established conservative thresholds. Our approach addresses this shortcoming by first identifying the margin above the error threshold of uncertain, yet possibly still useful data. It then displays this uncertain data in the context of the valid data by enhancing a clustered heatmap. We employ different visual representations for the different kinds of uncertainty involved. Finally, it lets the user interactively adjust the thresholds, giving visual feedback in the heatmap representation, so that an informed choice on which thresholds to use can be made instead of applying the usual rule-of-thumb cut-offs. We exemplify the usefulness of our concept by giving details for a concrete use case from our partners at the Medical University of Graz, thereby demonstrating our implementation of the general approach.

关键词： Visualization Uncertainty expression data

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