Genome informatics. International Conference on Genome Informatics最新文献_第5页

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 DOI: 10.1142/9781848165786_0010

A. Niida, S. Imoto, Masao Nagasaki, R. Yamaguchi, S. Miyano

{"title":"A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.","authors":"A. Niida, S. Imoto, Masao Nagasaki, R. Yamaguchi, S. Miyano","doi":"10.1142/9781848165786_0010","DOIUrl":"https://doi.org/10.1142/9781848165786_0010","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":"76816021","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: 8

Different groups of metabolic genes cluster around early and late firing origins of replication in budding yeast. 在出芽酵母中，不同的代谢基因群围绕着早期和晚期的复制起始点聚集。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0015

T. Spiesser, E. Klipp

{"title":"Different groups of metabolic genes cluster around early and late firing origins of replication in budding yeast.","authors":"T. Spiesser, E. Klipp","doi":"10.1142/9781848166585_0015","DOIUrl":"https://doi.org/10.1142/9781848166585_0015","url":null,"abstract":"DNA replication is a fundamental process that is tightly regulated during the cell cycle. In budding yeast it starts from multiple origins of replication and proceeds in a timely fashion according to a reproducible temporal program until the entire DNA is replicated exactly once per cell cycle. In this program an origin seems to have an inherent firing probability at a specific time in S-phase that is conserved over the population. However, what exactly determines the origin initiation time remains obscure. In this work, we analyze the gene content that clusters around replication origins following the assumption that inherent origin properties that determine staggered initiation times could potentially be mirrored in the close origin proximity. We perform a Gene Ontology term enrichment test and find that metabolic genes are significantly over-represented in the regions that are close to the starting points of DNA replication. Furthermore, functional analysis also reveals that catabolic genes cluster around early firing origins, whereas anabolic genes can rather be found in the proximity of late firing origins of replication. We speculate that, in budding yeast, gene function around replication origins correlates with their intrinsic probability to initiate DNA replication at a given point in S-phase.","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":"90923687","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

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 DOI: 10.1142/9781848165786_0015

Takeyuki Tamura, Nils Christian, Kazuhiro Takemoto, O. Ebenhöh, T. Akutsu

引用次数: 1

Analysis of a lipid biosynthesis protein family and phospholipid structural variations. 脂质生物合成蛋白家族及磷脂结构变异分析。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848165786_0016

Michihiro Tanaka, Yuki Moriya, S. Goto, M. Kanehisa

{"title":"Analysis of a lipid biosynthesis protein family and phospholipid structural variations.","authors":"Michihiro Tanaka, Yuki Moriya, S. Goto, M. Kanehisa","doi":"10.1142/9781848165786_0016","DOIUrl":"https://doi.org/10.1142/9781848165786_0016","url":null,"abstract":"Glycerophospholipids are major structural lipids in cellular membrane systems and play key roles as suppliers of the first and second messengers in the signal transduction and molecular recognition processes. The distribution of lipid components differs among organelles and cells. The distribution is controlled by two pathways in lipid metabolism: de nova and remodeling pathways. Glycerophospholipids including arachidonic and stearic acids are mostly produced in the remodeling pathway, whereas lipid chains are reconstructed from those synthesized in the de novo pathway. Recently lysophospholipid acyltransferases have been isolated as key enzymes in the remodeling pathway, and the substrate specificity has been investigated in terms of the chemical substructures of glycerophospholipids, such as the type of head groups and the length of aliphatic chains. These experimental studies have been reported for specific organisms, and only two representative sequence motifs are known for acyltransferases: a general pattern and the pattern for membrane-bound O-acyltransferase (MBOAT). Here we attempt to correlate the sequence patterns and the substrate specificity of lysophospholipid acyltransferases in 89 eukaryotic genomes in order to understand the roles of this enzyme family and underlying glycerophospholipid structural variations. Using phylogenetic and domain analyses, the lysophospholipid acyltransferase family was divided into 18 subtypes. Furthermore, we examined the occurrence of identified subtypes in eukaryotic genomes, and found the expansion of these subtypes in vertebrates. These findings may provide clues to understanding structural variations and distributions of glycerophospholipids in different organisms.","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":"86195115","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

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 DOI: 10.1142/9781848166585_0003

Bekir Erguner, M. Hattori, S. Goto, M. Kanehisa

{"title":"Characterizing common substructures of ligands for GPCR protein subfamilies.","authors":"Bekir Erguner, M. Hattori, S. Goto, M. Kanehisa","doi":"10.1142/9781848166585_0003","DOIUrl":"https://doi.org/10.1142/9781848166585_0003","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":"https://sci-hub-pdf.com/10.1142/9781848166585_0003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72475192","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

Integer programming-based method for completing signaling pathways and its application to analysis of colorectal cancer. 基于整数规划的信号通路完成方法及其在结直肠癌分析中的应用。

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

Takeyuki Tamura, Yoshihiro Yamanishi, Mao Tanabe, Susumu Goto, Minoru Kanehisa, Katsuhisa Horimoto, Tatsuya Akutsu

引用次数: 0

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

Kaname Kojima, Seiya Imoto, Masao Nagasaki, Satoru Miyano

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 DOI: 10.1142/9781848166585_0004

M. Fröhlich, P. Deen, E. Klipp

{"title":"A systems biology approach: modelling of Aquaporin-2 trafficking.","authors":"M. Fröhlich, P. Deen, E. Klipp","doi":"10.1142/9781848166585_0004","DOIUrl":"https://doi.org/10.1142/9781848166585_0004","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":"79455469","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: 4

Analyzing gene coexpression data by an evolutionary model. 用进化模型分析基因共表达数据。

Genome informatics. International Conference on Genome Informatics Pub Date : 2010-01-01 DOI: 10.1142/9781848166585_0013

M. Schütte, M. Mutwil, S. Persson, O. Ebenhöh

{"title":"Analyzing gene coexpression data by an evolutionary model.","authors":"M. Schütte, M. Mutwil, S. Persson, O. Ebenhöh","doi":"10.1142/9781848166585_0013","DOIUrl":"https://doi.org/10.1142/9781848166585_0013","url":null,"abstract":"Coexpressed genes are tentatively translated into proteins that are involved in similar biological functions. Here, we constructed gene coexpression networks from collected microarray data of the organisms Arabidopsis thaliana, Saccharomyces cerevisiae, and Escherichia coli. Their degree distributions show the common property of an overrepresentation of highly connected nodes followed by a sudden truncation. In order to analyze this behavior, we present an evolutionary model simulating the genetic evolution. This model assumes that new genes emerge by duplication from a small initial set of primordial genes. Our model does not include the removal of unused genes but selective pressure is indirectly taken into account by preferentially duplicating the old genes. Thus, gene duplication represents the emergence of a new gene and its successful establishment. After a duplication event, all genes are slightly but iteratively mutated, thus altering their expression patterns. Our model is capable of reproducing global properties of the investigated coexpression networks. We show that our model reflects the mean inter-node distances and especially the characteristic humps in the degree distribution that, in the biological examples, result from functionally related genes.","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":"83831842","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

Active pathway identification and classification with probabilistic ensembles. 基于概率集成的主动路径识别与分类。

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

Timothy Hancock, Hiroshi Mamitsuka

引用次数: 0