The filamentous fungal gene expression database (FFGED)

Filamentous fungal gene expression assays provide essential information for understanding systemic cellular regulation. To aid research on fungal gene expression, we constructed a novel, comprehensive, free database, the filamentous fungal gene expression database (FFGED), available at http://bioinfo.townsend.yale.edu. FFGED features user-friendly management of gene expression data, which are assorted into experimental metadata, experimental design, raw data, normalized details, and analysis results. Data may be submitted in the process of an experiment, and any user can submit multiple experiments, thus classifying the FFGED as an “active experiment” database. Most importantly, FFGED functions as a collective and collaborative platform, by connecting each experiment with similar related experiments made public by other users, maximizing data sharing among different users, and correlating diverse gene expression levels under multiple experimental designs within different experiments. A clear and efficient web interface is provided with enhancement by AJAX (Asynchronous JavaScript and XML) and through a collection of tools to effectively facilitate data submission, sharing, retrieval and visualization.     

HugeIndex: a database with visualization tools for high-density oligonucleotide array data from normal human tissues

High-density oligonucleotide arrays are a powerful tool for uncovering changes in global gene expression in various disease states. To this end, it is essential to first characterize the variations of gene expression in normal physiological processes. We established the Human Gene Expression (HuGE) Index database (www.HugeIndex.org) to serve as a public repository for gene expression data on normal human tissues using high-density oligonucleotide arrays. This resource currently contains the results of 59 gene expression experiments on 19 human tissues. We provide interactive tools for researchers to query and visualize our data over the Internet. To facilitate data analysis, we cross-reference each gene on the array with its annotation in the LocusLink database at NCBI.     

GEO(Gene Expression Omnibus ）:高通量基因表达数据库

 GEO(Gene Expression Omnibus)数据库包括高通量实验数据的广泛分类,有单通道和双通道以微阵列为基础的对mRNA丰度的测定;基因组DNA和蛋白质分子的实验数据;其中包括来自以非阵列为基础的高通量功能基因组学和蛋白质组学技术的数据也被存档,例如基因表达系列分析（serial analysis of gene expression,SAGE）和蛋白质鉴定技术.迄今为止,GEO数据库包含的数据含概10 000个杂交实验和来自30种不同生物体的SAGE库.本文概述了GEO数据库的查询和浏览,数据下载和格式,数据分析,贮存与更新,并着重分析GEO数据浏览器中控制词汇的使用,阐述了GEO数据库的数据挖掘以及GEO在分子生物学领域中的应用前景.GEO可由此公众网址直接登陆http://www.ncbi.nlm.nih.gov/projects/geo/.     

GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox

High-throughput gene expression analysis has become a frequent and powerful research tool in biology. At present, however, few software applications have been developed for biologists to query large microarray gene expression databases using a Web-browser interface. We present GENEVESTIGATOR, a database and Web-browser data mining interface for Affymetrix GeneChip data. Users can query the database to retrieve the expression patterns of individual genes throughout chosen environmental conditions, growth stages, or organs. Reversely, mining tools allow users to identify genes specifically expressed during selected stresses, growth stages, or in particular organs. Using GENEVESTIGATOR, the gene expression profiles of more than 22,000 Arabidopsis genes can be obtained, including those of 10,600 currently uncharacterized genes. The objective of this software application is to direct gene functional discovery and design of new experiments by providing plant biologists with contextual information on the expression of genes. The database and analysis toolbox is available as a community resource at https://www.genevestigator.ethz.ch.     

CVD: the intestinal crypt/villus in situ hybridization database

The intestinal crypt/villus in situ hybridization database (CVD) query interface is a web-based tool to search for genes with similar relative expression patterns along the crypt/villus axis of the mammalian intestine. The CVD is an online database holding information for relative gene expression patterns in the mammalian intestine and is based on the scoring of in situ hybridization experiments reported in the literature. CVD contains expression data for 88 different genes collected from 156 different in situ hybridization profiles. The web-based query interface allows execution of both single gene queries and pattern searches. The query results provide links to the most relevant public gene databases. AVAILABILITY: http://pc113.imbg.ku.dk/ps/     

New algorithms for multi-class cancer diagnosis using tumor gene expression signatures

MOTIVATION: The increasing use of DNA microarray-based tumor gene expression profiles for cancer diagnosis requires mathematical methods with high accuracy for solving clustering, feature selection and classification problems of gene expression data. RESULTS: New algorithms are developed for solving clustering, feature selection and classification problems of gene expression data. The clustering algorithm is based on optimization techniques and allows the calculation of clusters step-by-step. This approach allows us to find as many clusters as a data set contains with respect to some tolerance. Feature selection is crucial for a gene expression database. Our feature selection algorithm is based on calculating overlaps of different genes. The database used, contains over 16 000 genes and this number is considerably reduced by feature selection. We propose a classification algorithm where each tissue sample is considered as the center of a cluster which is a ball. The results of numerical experiments confirm that the classification algorithm in combination with the feature selection algorithm perform slightly better than the published results for multi-class classifiers based on support vector machines for this data set. AVAILABILITY: Available on request from the authors.     

ARROGANT: an application to manipulate large gene collections

ARROGANT (ARRay OrGANizing Tool) is a software tool developed to facilitate the identification, annotation and comparison of large collections of genes or clones. The objective is to enable users to compile gene/clone collections from different databases, allowing them to design experiments and analyze the collections as well as associated experimental data efficiently. ARROGANT can relate different sequence identifiers to their common reference sequence using the UniGene database, allowing for the comparison of data from two different microarray experiments. ARROGANT has been successfully used to analyze microarray expression data for colon cancer, to compile genes potentially related to cardiac diseases for subsequent resequencing (to identify single nucleotide polymorphisms, SNPs), to design a new comprehensive human cDNA microarray for cancer, to combine and compare expression data generated by different microarrays and to provide annotation for genes on custom and Affymetrix chips.     

MAML2基因表达及临床参数与低级别胶质瘤(LGG)患者的诊断及预后价值

脑胶质瘤(Glioma)是最常见的中枢系统恶性肿瘤,MAML2是NOTCH信号通路的共激活因子,通过癌基因组数据库(TCGA)分析验证MAML2基因表达及相关临床参数与低级别胶质瘤(LGG)的诊断及预后价值。从癌基因数据库LGG数据库中下载患者基因表达量数据及患者临床数据,采用统计学方法验证MAML2基因表达差异及临床参数与胶质瘤的诊断与预后关系。在TCGA LGG队列中,发现LGG组织中的MAML2基因较正常组织明显上调(P<0.001),其差异表达可作为低级别胶质瘤的潜在诊断标志物。同时,MAML2低表达组的LGG患者总体生存率低于高表达组(P=0.005 2)。此外,单因素多因素分析提示肿瘤分级,初治后肿瘤再发事件及MAML2低表达是低级别胶质瘤患者的独立危险因素。研究结果表明MAML2基因有可能成为诊断及预测低级别胶质瘤的一个潜在分子标记物。     

Differential profiles of genes expressed in neonatal brain of 129X1/SvJ and C57BL/6J mice: A database to aid in analyzing DNA microarrays using nonisogenic gene-targeted mice.

Strain-specific differences in gene expression have been observed among various inbred mouse strains. Two strains that are commonly used in gene-targeting research today are the 129 substrains, which are used to produce ES cell lines, and C57BL/6J, which is used for the extensive backcrosses required to produce isogenic knockout mice. When F2 nonisogenic littermates are assessed using DNA microarrays, one must determine whether the expression profiles obtained resulted either from specific alteration(s) induced by the targeted gene mutation or from gene expression differences related to the genetic background of the parent mouse strains. In the present study, we report the differential expression profile of genes expressed in neonatal brains and adult spleen and liver of 129X1/SvJ and C57BL/6J strains of mice. These comprehensive profiles were assessed using two types of Agilent Mouse Oligo Microarrays (development and standard) and were compiled into a publicly available database. Researchers can use this database to determine whether their microarray findings represent strain-specific differences in gene expression by comparing their data with those cataloged in our database. This database is useful for effectively analyzing DNA microarray data from nonisogenic littermates, and would help researchers avoid time-consuming backcrosses and confirmatory experiments requiring the use of many mice.     

ArrayExpress--a public repository for microarray gene expression data at the EBI

ArrayExpress is a new public database of microarray gene expression data at the EBI, which is a generic gene expression database designed to hold data from all microarray platforms. ArrayExpress uses the annotation standard Minimum Information About a Microarray Experiment (MIAME) and the associated XML data exchange format Microarray Gene Expression Markup Language (MAGE-ML) and it is designed to store well annotated data in a structured way. The ArrayExpress infrastructure consists of the database itself, data submissions in MAGE-ML format or via an online submission tool MIAMExpress, online database query interface, and the Expression Profiler online analysis tool. ArrayExpress accepts three types of submission, arrays, experiments and protocols, each of these is assigned an accession number. Help on data submission and annotation is provided by the curation team. The database can be queried on parameters such as author, laboratory, organism, experiment or array types. With an increasing number of organisations adopting MAGE-ML standard, the volume of submissions to ArrayExpress is increasing rapidly. The database can be accessed at http://www.ebi.ac.uk/arrayexpress.     

Rice Expression Database(RED):An integrated RNA-Seq-derived gene expression database for rice

Rice is one of the most important stable food as well as a monocotyledonous model organism for the plant research community.Here,we present RED(Rice Expression Database;http://expression.ic4r.org),an integrated database of rice gene expression profiles derived entirely from RNA-Seq data.RED features a comprehensive collection of 284 high-quality RNA-Seq experiments,integrates a large number of gene expression profiles and covers a wide range of rice growth stages as well as various treatments.Based on massive expression profiles,RED provides a list of housekeeping and tissue-specific genes and dynamically constructs co-expression networks for gene(s) of interest.Besides,it provides user-friendly web interfaces for querying,browsing and visualizing expression profiles of concerned genes.Together,as a core resource in BIG Data Center,RED bears great utility for characterizing the function of rice genes and better understanding important biological processes and mechanisms underlying complex agronomic traits in rice.     

A database for the analysis of immunity genes in Drosophila

While microarray experiments generate voluminous data, discerning trends that support an existing or alternative paradigm is challenging. To synergize hypothesis building and testing, we designed the Pathogen Associated Drosophila MicroArray (PADMA) database for easy retrieval and comparison of microarray results from immunity-related experiments (www.padmadatabase.org). PADMA also allows biologists to upload their microarray-results and compare it with datasets housed within PADMA. We tested PADMA using a preliminary dataset from Ganaspis xanthopoda-infected fly larvae, and uncovered unexpected trends in gene expression, reshaping our hypothesis. Thus, the PADMA database will be a useful resource to fly researchers to evaluate, revise, and refine hypotheses.     

A database for the analysis of immunity genes in Drosophila: PADMA database

While microarray experiments generate voluminous data, discerning trends that support an existing or alternative paradigm is challenging. To synergize hypothesis building and testing, we designed the Pathogen Associated Drosophila MicroArray (PADMA) database for easy retrieval and comparison of microarray results from immunity-related experiments (www.padmadatabase.org). PADMA also allows biologists to upload their microarray-results and compare it with datasets housed within PADMA. We tested PADMA using a preliminary dataset from Ganaspis xanthopoda-infected fly larvae, and uncovered unexpected trends in gene expression, reshaping our hypothesis. Thus, the PADMA database will be a useful resource to fly researchers to evaluate, revise, and refine hypotheses.     

PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens

Physcomitrella patens is a bryophyte model plant that is often used to study plant evolution and development. Its resources are of great importance for comparative genomics and evo‐devo approaches. However, expression data from Physcomitrella patens were so far generated using different gene annotation versions and three different platforms: CombiMatrix and NimbleGen expression microarrays and RNA sequencing. The currently available P. patens expression data are distributed across three tools with different visualization methods to access the data. Here, we introduce an interactive expression atlas, Physcomitrella Expression Atlas Tool (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visualization methods to query the data in a single web‐based tool. Moreover, PEATmoss includes 35 expression experiments not previously available in any other expression atlas. To facilitate gene expression queries across different gene annotation versions, and to access P. patens annotations and related resources, a lookup database and web tool linked to PEATmoss was implemented. PEATmoss can be accessed at https://peatmoss.online.uni-marburg.de     

Large scale data mining approach for gene-specific standardization of microarray gene expression data

MOTIVATION: The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of gene's behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples. RESULTS: The availability of a large collection of array samples sharing the same platform in public databases, such as NCBI GEO, enabled us to re-standardize the expression intensity of a gene using its mean and variation in the wide variety of experimental conditions. This approach was evaluated via the re-identification of breast cancer-specific gene expression. It successfully prioritized several genes associated with breast tumor, for which the expression difference between normal and breast cancer cells was marginal and thus would have been difficult to recognize using conventional analysis methods. Maximizing the utility of microarray data in the public database, it provides a valuable tool particularly for the identification of previously unrecognized disease-related genes. AVAILABILITY: A user friendly web-interface (http://compbio.sookmyung.ac.kr/~lage/) was constructed to provide the present large-scale approach for the analysis of GEO microarray data (GS-LAGE server).