Genome informatics. International Conference on Genome Informatics最新文献_第2页

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

Christian Waltermann, Max Floettmann, Edda Klipp

引用次数: 0

Characterizing common substructures of ligands for GPCR protein subfamilies. 表征GPCR蛋白亚家族配体的共同亚结构。

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

Bekir Erguner, Masahiro Hattori, Susumu Goto, Minoru Kanehisa

{"title":"Characterizing common substructures of ligands for GPCR protein subfamilies.","authors":"Bekir Erguner, Masahiro Hattori, Susumu Goto, Minoru Kanehisa","doi":"","DOIUrl":"","url":null,"abstract":"The G-protein coupled receptor (GPCR) superfamily is the largest class of proteins with therapeutic value. More than 40% of present prescription drugs are GPCR ligands. The high therapeutic value of GPCR proteins and recent advancements in virtual screening methods gave rise to many virtual screening studies for GPCR ligands. However, in spite of vast amounts of research studying their functions and characteristics, 3D structures of most GPCRs are still unknown. This makes target-based virtual screenings of GPCR ligands extremely difficult, and successful virtual screening techniques rely heavily on ligand information. These virtual screening methods focus on specific features of ligands on GPCR protein level, and common features of ligands on higher levels of GPCR classification are yet to be studied. Here we extracted common substructures of GPCR ligands of GPCR protein subfamilies. We used the SIMCOMP, a graph-based chemical structure comparison program, and hierarchical clustering to reveal common substructures. We applied our method to 850 GPCR ligands and we found 53 common substructures covering 439 ligands. These substructures contribute to deeper understanding of structural features of GPCR ligands which can be used in new drug discovery methods.","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":"30251335","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

Prediction of regulatory transcription factors in T helper cell differentiation and maintenance. 调控转录因子在辅助T细胞分化和维持中的预测。

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

Yue-Hien Lee, Manuela Benary, Ria Baumgrass, Hanspeter Herzel

{"title":"Prediction of regulatory transcription factors in T helper cell differentiation and maintenance.","authors":"Yue-Hien Lee, Manuela Benary, Ria Baumgrass, Hanspeter Herzel","doi":"","DOIUrl":"","url":null,"abstract":"Naive T-helper (Th) cells differentiate into distinct lineages including Th1, Th2, Th17 and regulatory T (Treg) cells. Each of these Th-lineages has specific functions in immune defense and T cell homeostasis. Th cell fate decisions and commitment are dependent on the kind and strength of T cell stimulation and the subsequent gene expression profiles. Our analysis targeted the identification of new regulatory transcription factor binding sites (TFBSs) in the promoter regions of up- and down-regulated genes in Treg cell differentiation and lineage maintenance. For this approach we compared different gene groups from global gene expression studies with background models of randomly selected genes to identify significantly overrepresented TFBSs. Results of our analysis suggest that Ets and IRF family members contribute to the regulation of the initial induction of Treg cells. Furthermore, we identified the overrepresented TFBS-pairs Runx-NFAT and GATA3-Foxp3 in Treg specific genes and Foxp3 dependent genes, respectively. Interestingly, previous studies have observed functional interactions of both TFBS-pairs in T cells. This study provides a starting point for further investigations to elucidate the transcriptional network in Treg cells.","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":"28785609","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

Analysis and prediction of nutritional requirements using structural properties of metabolic networks and support vector machines. 利用代谢网络和支持向量机的结构特性分析和预测营养需求。

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

Takeyuki Tamura, Nils Christian, Kazuhiro Takemoto, Oliver Ebenhöh, Tatsuya Akutsu

引用次数: 0

A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells. 一种新的癌症转录组荟萃分析方法揭示了癌细胞中普遍存在的转录网络。

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

Atsushi Niida, Seiya Imoto, Masao Nagasaki, Rui Yamaguchi, Satoru Miyano

{"title":"A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.","authors":"Atsushi Niida, Seiya Imoto, Masao Nagasaki, Rui Yamaguchi, Satoru Miyano","doi":"","DOIUrl":"","url":null,"abstract":"Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.","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":"28785611","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

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

Moritz Schütte, Niels Klitgord, Daniel Segrè, Oliver Ebenhöh

{"title":"Co-evolution of metabolism and protein sequences.","authors":"Moritz Schütte, Niels Klitgord, Daniel Segrè, Oliver Ebenhöh","doi":"","DOIUrl":"","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":"28785614","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

Kinetic modelling of DNA replication initiation in budding yeast. 出芽酵母DNA复制起始的动力学模拟。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0001

Matteo Barberis, T. Spiesser, E. Klipp

{"title":"Kinetic modelling of DNA replication initiation in budding yeast.","authors":"Matteo Barberis, T. Spiesser, E. Klipp","doi":"10.1142/9781848166585_0001","DOIUrl":"https://doi.org/10.1142/9781848166585_0001","url":null,"abstract":"DNA replication is restricted to a specific time window of the cell cycle, called S phase. Successful progression through S phase requires replication to be properly regulated to ensure that the entire genome is duplicated exactly once, without errors, in a timely fashion. As a result, DNA replication has evolved into a tightly regulated process involving the coordinated action of numerous factors that function in all phases of the cell cycle. Biochemical mechanisms driving the eukaryotic cell division cycle have been the subject of a number of mathematical models. However, cell cycle networks reported in literature so far have not addressed the steps of DNA replication events. In particular, the assembly of the replication machinery is crucial for the timing of S phase. This event, called \"initiation\", which occurs in late M / early G1 of the cell cycle, starts with the assembly of the pre-replicative complex (pre-RC) at the origins of replication on the DNA. Its activation depends on the availability of different kinase complexes, cyclin-dependent kinases (CDKs) and Dbf-dependent kinase (DDK), which phosphorylate specific components of the pre-RC to convert it into the pre-initiation complex (pre-IC). We have developed an ODE-based model of the network responsible for this process in budding yeast by using mass-action kinetics. We considered all steps from the assembly of the first components at the DNA replication origin up to the active replisome that recruits the polymerases and verified the computational dynamics with the available literature data. Our results highlighted the link between activation of CDK and DDK and the step-by-step formation of both pre-RC and pre-IC, suggesting S-CDK (Cdk1-Clb5,6) to be the main regulator of the process.","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":"79042684","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: 6

Genome-wide analysis of plant UGT family based on sequence and substrate information. 基于序列和底物信息的植物UGT家族全基因组分析。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0011

Y. Nishimura, T. Tokimatsu, Masaaki Kotera, S. Goto, M. Kanehisa

{"title":"Genome-wide analysis of plant UGT family based on sequence and substrate information.","authors":"Y. Nishimura, T. Tokimatsu, Masaaki Kotera, S. Goto, M. Kanehisa","doi":"10.1142/9781848166585_0011","DOIUrl":"https://doi.org/10.1142/9781848166585_0011","url":null,"abstract":"UGTs (UDP glycosyltransferase) are the largest glycosyltransferase gene family in higher plants, modifying secondary metabolites, hormones, and xenobiotics. This gene family plays an important role in the vast diversity of plant secondary metabolites specific to species. Experimental data of biochemical activities and physiological roles of plant UGTs are increasing but most UGTs are not still functionally characterized. To understand their catalytic specificity and function from sequence data, phylogenetic analyses have been achieved mainly in Arabidopsis, but massive and comprehensive approach covering various species has not been applied yet. In this study, we collected 733 UGT sequences derived from 96 plant species and 252 substrate specificity data. We constructed a phylogenetic tree and divided most part of these genes into nine sequence groups, which are characterized by biochemical specificity. Furthermore, we performed genome-wide analysis of seven plant species UGTs by mapping them into these groups. We propose this is the first step to understand whole glycosylated secondary metabolites of each plant species from its genome information.","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":"84944925","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

Phylogenetic analysis of lipid mediator GPCRs. 脂质介质gpcr的系统发育分析。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0010

Sayaka Mizutani, Michihiro Tanaka, C. Wheelock, M. Kanehisa, S. Goto

{"title":"Phylogenetic analysis of lipid mediator GPCRs.","authors":"Sayaka Mizutani, Michihiro Tanaka, C. Wheelock, M. Kanehisa, S. Goto","doi":"10.1142/9781848166585_0010","DOIUrl":"https://doi.org/10.1142/9781848166585_0010","url":null,"abstract":"Lipid mediator is the collective term for prostanoids, leukotrienes, lysophospholipids, platelet-activating factor, endocannabinoids and other bioactive lipids, that are involved in various physiological functions including inflammation, immune regulation and cellular development. They act by binding to their ligand-specific G-protein coupled receptors (GPCRs). Since 1990's a number of lipid GPCRs have been cloned in humans, with a few more identified in other vertebrates. However, the conservation of these receptors has been poorly investigated in other eukaryotes. Herein we performed a phylogenetic analysis by collecting their orthologs in 13 eukaryotes with complete genomes. The analysis shows that orthologs for prostanoid receptors are likely to be conserved in the 13 eukaryotes. In contrast, those for lysophospholipid and cannabinoid receptors appear to be conserved only in vertebrates and chordates. Receptors for leukotrienes and other bioactive lipids are limited to vertebrates. These results indicate that the lipid mediators and their receptors have coevolved with the development of highly modulated physiological functions such as immune regulation and the formation of the central nervous system. Accordingly, examining the presence and role of lipid mediator GPCR orthologs in invertebrate species can provide insight into the development of fundamental biological processes across diverse taxa.","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":"79885603","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: 2

Fluxviz - Cytoscape plug-in for visualization of flux distributions in networks. 用于网络中通量分布可视化的Cytoscape插件。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0008

M. König, H. Holzhütter

{"title":"Fluxviz - Cytoscape plug-in for visualization of flux distributions in networks.","authors":"M. König, H. Holzhütter","doi":"10.1142/9781848166585_0008","DOIUrl":"https://doi.org/10.1142/9781848166585_0008","url":null,"abstract":"MOTIVATION Methods like FBA and kinetic modeling are widely used to calculate fluxes in metabolic networks. For the analysis and understanding of simulation results and experimentally measured fluxes visualization software within the network context is indispensable. RESULTS We present Flux Viz, an open-source Cytoscape plug-in for the visualization of flux distributions in molecular interaction networks. FluxViz supports (i) import of networks in a variety of formats (SBML, GML, XGMML, SIF, BioPAX, PSI-MI) (ii) import of flux distributions as CSV, Cytoscape attributes or VAL files (iii) limitation of views to flux carrying reactions (flux subnetwork) or network attributes like localization (iv) export of generated views (SVG, EPS, PDF, BMP, PNG). Though FluxViz was primarily developed as tool for the visualization of fluxes in metabolic networks and the analysis of simulation results from FASIMU, a flexible software for batch flux-balance computation in large metabolic networks, it is not limited to biochemical reaction networks and FBA but can be applied to the visualization of arbitrary fluxes in arbitrary graphs. AVAILABILITY The platform-independent program is an open-source project, freely available at http://sourceforge.net/projects/fluxvizplugin/ under GNU public license, including manual, tutorial and examples.","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":"84952502","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: 30