Genome informatics. International Conference on Genome Informatics最新文献_第4页

On the performance of methods for finding a switching mechanism in gene expression. 关于寻找基因表达转换机制的方法的性能。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01

Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka

{"title":"On the performance of methods for finding a switching mechanism in gene expression.","authors":"Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka","doi":"","DOIUrl":"","url":null,"abstract":"We address an issue of detecting a switching mechanism in gene expression, where two genes are positively correlated for one experimental condition while they are negatively correlated for another. We compare the performance of existing methods for this issue, roughly divided into two types: interaction test (IT) and the difference of correlation coefficients. Interaction test, currently a standard approach for detecting epistasis in genetics, is the log-likelihood ratio test between two logistic regressions with/without an interaction term, resulting in checking the strength of interaction between two genes. On the other hand, two correlation coefficients can be computed for two experimental conditions and the difference of them shows the alteration of expression trends in a more straightforward manner. In our experiments, we tested three different types of correlation coefficients: Pearson, Spearman and a midcorrelation (biweight midcorrelation). The experiment was performed by using ~ 2.3 × 10(9) combinations selected out of the GEO (Gene Expression Omnibus) database. We sorted all combinations according to the p-values of IT or by the absolute values of the difference of correlation coefficients and then visually evaluated the top ranked combinations in terms of the switching mechanism. The result showed that 1) combinations detected by IT included non-switching combinations and 2) Pearson was affected by outliers easily while Spearman and the midcorrelation seemed likely to avoid them.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30252337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

G1 and G2 arrests in response to osmotic shock are robust properties of the budding yeast cell cycle. G1和G2对渗透冲击的反应是出芽酵母细胞周期的强大特性。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0017

C. Waltermann, Max Floettmann, E. Klipp

引用次数: 5

New kernel methods for phenotype prediction from genotype data. 从基因型数据预测表型的新核心方法。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848165786_0011

Ritsuko Onuki, T. Shibuya, M. Kanehisa

引用次数: 6

Gene regulatory network clustering for graph layout based on microarray gene expression data. 基于微阵列基因表达数据的基因调控网络聚类图布局。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0007

Kaname Kojima, S. Imoto, Masao Nagasaki, S. Miyano

引用次数: 1

Co-evolution of metabolism and protein sequences. 代谢和蛋白质序列的共同进化。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848165786_0013

M. Schütte, Niels Klitgord, D. Segrè, O. Ebenhöh

{"title":"Co-evolution of metabolism and protein sequences.","authors":"M. Schütte, Niels Klitgord, D. Segrè, O. Ebenhöh","doi":"10.1142/9781848165786_0013","DOIUrl":"https://doi.org/10.1142/9781848165786_0013","url":null,"abstract":"The set of chemicals producible and usable by metabolic pathways must have evolved in parallel with the enzymes that catalyze them. One implication of this common historical path should be a correspondence between the innovation steps that gradually added new metabolic reactions to the biosphere-level biochemical toolkit, and the gradual sequence changes that must have slowly shaped the corresponding enzyme structures. However, global signatures of a long-term co-evolution have not been identified. Here we search for such signatures by computing correlations between inter-reaction distances on a metabolic network, and sequence distances of the corresponding enzyme proteins. We perform our calculations using the set of all known metabolic reactions, available from the KEGG database. Reaction-reaction distance on the metabolic network is computed as the length of the shortest path on a projection of the metabolic network, in which nodes are reactions and edges indicate whether two reactions share a common metabolite, after removal of cofactors. Estimating the distance between enzyme sequences in a meaningful way requires some special care: for each enzyme commission (EC) number, we select from KEGG a consensus set of protein sequences using the cluster of orthologous groups of proteins (COG) database. We define the evolutionary distance between protein sequences as an asymmetric transition probability between two enzymes, derived from the corresponding pair-wise BLAST scores. By comparing the distances between sequences to the minimal distances on the metabolic reaction graph, we find a small but statistically significant correlation between the two measures. This suggests that the evolutionary walk in enzyme sequence space has locally mirrored, to some extent, the gradual expansion of metabolism.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90260338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Graphical analysis and experimental evaluation of Saccharomyces cerevisiae PTRK1|2 and PBMH1|2 promoter region. 酿酒酵母PTRK1|2和PBMH1|2启动子区图谱分析及实验评价

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848165786_0002

Susanne Gerber, G. Hasenbrink, Wouter T. Hendriksen, P. van Heusden, J. Ludwig, E. Klipp, H. Lichtenberg-Fraté

{"title":"Graphical analysis and experimental evaluation of Saccharomyces cerevisiae PTRK1|2 and PBMH1|2 promoter region.","authors":"Susanne Gerber, G. Hasenbrink, Wouter T. Hendriksen, P. van Heusden, J. Ludwig, E. Klipp, H. Lichtenberg-Fraté","doi":"10.1142/9781848165786_0002","DOIUrl":"https://doi.org/10.1142/9781848165786_0002","url":null,"abstract":"We designed a simple graphical presentation for the results of a transcription factor (TF) pattern matching analysis. The TF analysis algorithm utilized known sequence signature motifs from several databases. The graphical presentation enabled a quick overview of potential TF binding sites, their frequency and spacing on both DNA strands and thus straight forward identification of promising candidates for further experimental investigations. The developed tool was applied on in total four Saccharomyces cerevisiae gene promoter regions. The selected differentially expressed genes belong to functionally different families and encode duplicate functions, TRK1 and TRK2 as ion transporters and BMH1 and BMH2 as multiple regulators. Output evaluation revealed a number of TFs with promising differences in the promoter regions of each gene pair. Experimental investigations were performed by using corresponding TF yeast mutants for either phenotypic analysis of ion transport mediated growth or expression analysis of BMH1,2 genes. Upon phenotypic testing one TF mutant exhibited severely impaired growth under non-permissive conditions. This TF, Mot3p was identified as of most abundant potential binding sites and distinctive patterns among the TRK promoter regions.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74579065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Efficient and detailed model of the local Ca2+ release unit in the ventricular cardiac myocyte. 心室心肌细胞局部Ca2+释放单元的高效和详细模型。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01

Thomas Schendel, Martin Falcke

引用次数: 0

A systems biology approach: modelling of Aquaporin-2 trafficking. 系统生物学方法:水通道蛋白-2运输的建模。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01

Martina Fröhlich, Peter M T Deen, Edda Klipp

{"title":"A systems biology approach: modelling of Aquaporin-2 trafficking.","authors":"Martina Fröhlich, Peter M T Deen, Edda Klipp","doi":"","DOIUrl":"","url":null,"abstract":"In healthy individuals, dehydration of the body leads to release of the hormone vasopressin from the pituitary. Via the bloodstream, vasopressin reaches the collecting duct cells in the kidney, where the water channel Aquaporin-2 (AQP2) is expressed. After stimulation of the vasopressin V2 receptor by vasopressin, intracellular AQP2-containing vesicles fuse with the apical plasma membrane of the collecting duct cells. This leads to increased water reabsorption from the pro-urine into the blood and therefore to enhanced retention of water within the body. Using existing biological data we propose a mathematical model of AQP-2 trafficking and regulation in collecting duct cells. Our model includes the vasopressin receptor, adenylate cyclase, protein kinase A, and intracellular as well as membrane located AQP2. To model the chemical reactions we used ordinary differential equations (ODEs) based on mass action kinetics. We employ known protein concentrations and time series data to estimate the kinetic parameters of our model and demonstrate its validity. Through generating, testing and ranking different versions of the model, we show that some model versions can describe the data well as soon as important regulatory parts such as the reduction of the signal by internalization of the vasopressin-receptor or the negative feedback loop representing phosphodiesterase activity are included. We perform time-dependent sensitivity analysis to identify the reactions that have the greatest influence on the cAMP and membrane located AQP2 levels over time. We predict the time courses for membrane located AQP2 at different vasopressin concentrations, compare them with newly generated data and discuss the competencies of the model.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30252335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graphical analysis and experimental evaluation of Saccharomyces cerevisiae PTRK1|2 and PBMH1|2 promoter region. 酿酒酵母PTRK1|2和PBMH1|2启动子区图谱分析及实验评价

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01

Susanne Gerber, Guido Hasenbrink, Wouter Hendriksen, Paul Van Heusden, Jost Ludwig, Edda Klipp, Hella Lichtenberg-Fraté

{"title":"Graphical analysis and experimental evaluation of Saccharomyces cerevisiae PTRK1|2 and PBMH1|2 promoter region.","authors":"Susanne Gerber, Guido Hasenbrink, Wouter Hendriksen, Paul Van Heusden, Jost Ludwig, Edda Klipp, Hella Lichtenberg-Fraté","doi":"","DOIUrl":"","url":null,"abstract":"We designed a simple graphical presentation for the results of a transcription factor (TF) pattern matching analysis. The TF analysis algorithm utilized known sequence signature motifs from several databases. The graphical presentation enabled a quick overview of potential TF binding sites, their frequency and spacing on both DNA strands and thus straight forward identification of promising candidates for further experimental investigations. The developed tool was applied on in total four Saccharomyces cerevisiae gene promoter regions. The selected differentially expressed genes belong to functionally different families and encode duplicate functions, TRK1 and TRK2 as ion transporters and BMH1 and BMH2 as multiple regulators. Output evaluation revealed a number of TFs with promising differences in the promoter regions of each gene pair. Experimental investigations were performed by using corresponding TF yeast mutants for either phenotypic analysis of ion transport mediated growth or expression analysis of BMH1,2 genes. Upon phenotypic testing one TF mutant exhibited severely impaired growth under non-permissive conditions. This TF, Mot3p was identified as of most abundant potential binding sites and distinctive patterns among the TRK promoter regions.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28783005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of gene expression profiles produced by CAGE, illumina microarray and real time RT-PCR. CAGE、illumina芯片和实时RT-PCR基因表达谱的比较。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0005

André Fujita, Masao Nagasaki, S. Imoto, A. Saito, Emi Ikeda, Teppei Shimamura, R. Yamaguchi, Y. Hayashizaki, S. Miyano

{"title":"Comparison of gene expression profiles produced by CAGE, illumina microarray and real time RT-PCR.","authors":"André Fujita, Masao Nagasaki, S. Imoto, A. Saito, Emi Ikeda, Teppei Shimamura, R. Yamaguchi, Y. Hayashizaki, S. Miyano","doi":"10.1142/9781848166585_0005","DOIUrl":"https://doi.org/10.1142/9781848166585_0005","url":null,"abstract":"Several technologies are currently used for gene expression profiling, such as Real Time RT-PCR, microarray and CAGE (Cap Analysis of Gene Expression). CAGE is a recently developed method for constructing transcriptome maps and it has been successfully applied to analyzing gene expressions in diverse biological studies. The principle of CAGE has been developed to address specific issues such as determination of transcriptional starting sites, the study of promoter regions and identification of new transcripts. Here, we present both quantitative and qualitative comparisons among three major gene expression quantification techniques, namely: CAGE, illumina microarray and Real Time RT-PCR, by showing that the quantitative values of each method are not interchangeable, however, each of them has unique characteristics which render all of them essential and complementary. Understanding the advantages and disadvantages of each technology will be useful in selecting the most appropriate technique for a determined purpose.","PeriodicalId":73143,"journal":{"name":"Genome informatics. International Conference on Genome Informatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90344029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3