In biology, signal transduction refers to a process by which a cell converts one kind of signal or stimulus into another. It involves ordered sequences of biochemical reactions inside the cell. These cascades of react...In biology, signal transduction refers to a process by which a cell converts one kind of signal or stimulus into another. It involves ordered sequences of biochemical reactions inside the cell. These cascades of reactions are carried out by enzymes and activated by second messengers. Signal transduction pathways are complex in nature. Each pathway is responsible for tuning one or more biological functions in the intracellular environment as well as more than one pathway interact among themselves to carry forward a single biological function. Such kind of behavior of these pathways makes understanding difficult. Hence, for the sake of simplicity, they need to be partitioned into smaller modules and then analyzed. We took VEGF signaling pathway, which is responsible for angiogenesis for this kind of modularized study. Modules were obtained by applying the algorithm of Nayak and De (Nayak and De, 2007) for different complexity values. These sets of modules were compared among themselves to get the best set of modules for an optimal complexity value. The best set of modules compared with four different partitioning algorithms namely, Farhat’s (Farhat, 1998), Greedy (Chartrand and Oellermann, 1993), Kernighan-Lin’s (Kernighan and Lin, 1970) and Newman’s community finding algorithm (Newman, 2006). These comparisons enabled us to decide which of the aforementioned algorithms was the best one to create partitions from human VEGF signaling pathway. The optimal complexity value, on which the best set of modules was obtained, was used to get modules from different species for comparative study. Comparison among these modules would shed light on the trend of development of VEGF signaling pathway over these species.展开更多
The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functi...The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functional units often corresponding to subpathways in the KEGG pathway maps, are designed for better automation of genome interpretation. Each KEGG module is represented by a simple Boolean expression of KEGG Orthology （KO） identifiers （K numbers）, enabling automatic evaluation of the completeness of genes in the genome. Here we focus on metabolic functions and introduce reaction modules for improving annotation and signature modules for inferring metabolic capacity. We also describe how genome annotation is performed in KEGG using the manually created KO database and the computationaUy generated SSDB database. The resulting KEGG GENES database with KO （K number） annotation is a reference sequence database to be compared for automated annotation and interpretation of newly determined genomes.展开更多
目的利用基因芯片技术研究肺腺癌、转移淋巴结、癌旁正常及胚胎肺组织中的基因表达差异,并利用代谢通路数据库分析这些基因与代谢通路相关性信息。方法分别抽取肺腺癌、转移淋巴结、癌旁正常和胚胎肺组织的总RNA并纯化mRNA,用逆转录的方...目的利用基因芯片技术研究肺腺癌、转移淋巴结、癌旁正常及胚胎肺组织中的基因表达差异,并利用代谢通路数据库分析这些基因与代谢通路相关性信息。方法分别抽取肺腺癌、转移淋巴结、癌旁正常和胚胎肺组织的总RNA并纯化mRNA,用逆转录的方法,将各mRNA的两种荧光Cy5和Cy3标记的cDNA链做探针,并与含有1152条人类全长基因芯片上进行杂交。用计算机处理和分析。将实验所得基因映射到(Kyoto encyclopedia of genes and genomes,KEGG)通路数据库的基因表达调控通路上,检测同一基因表达调控通路内基因的共表达趋势。结果4例肺腺癌组织和癌旁正常组织比较共表达差异基因25个;3例肺腺癌组织和转移淋巴结组织比较共表达差异基因8个;1例胚胎肺组织和癌旁正常组织比较差异表达基因316个,且与肺腺癌组织和癌旁正常组织比较16个基因有共同表达差异。在β-丙氨酸代谢通路和丁酸代谢通路中的基因与疾病组织中的基因显著相关(P<0.05),而与癌旁组织和淋巴结组织中的基因无关(P>0.05)。结论肺腺癌组织与癌旁正常组织比较、肺腺癌组织和转移淋巴结组织比较均存在共表达差异基因,这些基因可能与肺腺癌的发生、发展有关。代谢通路分析结果提示,在β-丙氨酸代谢通路和丁酸代谢通路中存在着肺腺癌疾病相关的基因,且在同一通路中的基因有共表达趋势。展开更多
文摘In biology, signal transduction refers to a process by which a cell converts one kind of signal or stimulus into another. It involves ordered sequences of biochemical reactions inside the cell. These cascades of reactions are carried out by enzymes and activated by second messengers. Signal transduction pathways are complex in nature. Each pathway is responsible for tuning one or more biological functions in the intracellular environment as well as more than one pathway interact among themselves to carry forward a single biological function. Such kind of behavior of these pathways makes understanding difficult. Hence, for the sake of simplicity, they need to be partitioned into smaller modules and then analyzed. We took VEGF signaling pathway, which is responsible for angiogenesis for this kind of modularized study. Modules were obtained by applying the algorithm of Nayak and De (Nayak and De, 2007) for different complexity values. These sets of modules were compared among themselves to get the best set of modules for an optimal complexity value. The best set of modules compared with four different partitioning algorithms namely, Farhat’s (Farhat, 1998), Greedy (Chartrand and Oellermann, 1993), Kernighan-Lin’s (Kernighan and Lin, 1970) and Newman’s community finding algorithm (Newman, 2006). These comparisons enabled us to decide which of the aforementioned algorithms was the best one to create partitions from human VEGF signaling pathway. The optimal complexity value, on which the best set of modules was obtained, was used to get modules from different species for comparative study. Comparison among these modules would shed light on the trend of development of VEGF signaling pathway over these species.
文摘The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functional units often corresponding to subpathways in the KEGG pathway maps, are designed for better automation of genome interpretation. Each KEGG module is represented by a simple Boolean expression of KEGG Orthology （KO） identifiers （K numbers）, enabling automatic evaluation of the completeness of genes in the genome. Here we focus on metabolic functions and introduce reaction modules for improving annotation and signature modules for inferring metabolic capacity. We also describe how genome annotation is performed in KEGG using the manually created KO database and the computationaUy generated SSDB database. The resulting KEGG GENES database with KO （K number） annotation is a reference sequence database to be compared for automated annotation and interpretation of newly determined genomes.
文摘目的利用基因芯片技术研究肺腺癌、转移淋巴结、癌旁正常及胚胎肺组织中的基因表达差异,并利用代谢通路数据库分析这些基因与代谢通路相关性信息。方法分别抽取肺腺癌、转移淋巴结、癌旁正常和胚胎肺组织的总RNA并纯化mRNA,用逆转录的方法,将各mRNA的两种荧光Cy5和Cy3标记的cDNA链做探针,并与含有1152条人类全长基因芯片上进行杂交。用计算机处理和分析。将实验所得基因映射到(Kyoto encyclopedia of genes and genomes,KEGG)通路数据库的基因表达调控通路上,检测同一基因表达调控通路内基因的共表达趋势。结果4例肺腺癌组织和癌旁正常组织比较共表达差异基因25个;3例肺腺癌组织和转移淋巴结组织比较共表达差异基因8个;1例胚胎肺组织和癌旁正常组织比较差异表达基因316个,且与肺腺癌组织和癌旁正常组织比较16个基因有共同表达差异。在β-丙氨酸代谢通路和丁酸代谢通路中的基因与疾病组织中的基因显著相关(P<0.05),而与癌旁组织和淋巴结组织中的基因无关(P>0.05)。结论肺腺癌组织与癌旁正常组织比较、肺腺癌组织和转移淋巴结组织比较均存在共表达差异基因,这些基因可能与肺腺癌的发生、发展有关。代谢通路分析结果提示,在β-丙氨酸代谢通路和丁酸代谢通路中存在着肺腺癌疾病相关的基因,且在同一通路中的基因有共表达趋势。
