ATTED-II provides coexpressed gene networks for Arabidopsis

ATTED-II (http://atted.jp) is a database of gene coexpression in Arabidopsis that can be used to design a wide variety of experiments, including the prioritization of genes for functional identification or for studies of regulatory relationships. Here, we report updates of ATTED-II that focus especially on functionalities for constructing gene networks with regard to the following points: (i) introducing a new measure of gene coexpression to retrieve functionally related genes more accurately, (ii) implementing clickable maps for all gene networks for step-by-step navigation, (iii) applying Google Maps API to create a single map for a large network, (iv) including information about protein–protein interactions, (v) identifying conserved patterns of coexpression and (vi) showing and connecting KEGG pathway information to identify functional modules. With these enhanced functions for gene network representation, ATTED-II can help researchers to clarify the functional and regulatory networks of genes in Arabidopsis

COXPRESdb: a database of coexpressed gene networks in mammals

A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein–protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species

Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

Information regarding gene coexpression is useful to predict gene function. Several databases have been constructed for gene coexpression in model organisms based on a large amount of publicly available gene expression data measured by GeneChip platforms. In these databases, Pearson's correlation coefficients (PCCs) of gene expression patterns are widely used as a measure of gene coexpression. Although the coexpression measure or GeneChip summarization method affects the performance of the gene coexpression database, previous studies for these calculation procedures were tested with only a small number of samples and a particular species. To evaluate the effectiveness of coexpression measures, assessments with large-scale microarray data are required. We first examined characteristics of PCC and found that the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function. In addition, we found that this problem could be overcome when we used correlation ranks instead of correlation values. This observation was evaluated by large-scale gene expression data for four species: Arabidopsis, human, mouse and rat

Identification of gene expression logical invariants in Arabidopsis.

Numerous gene expression datasets from diverse tissue samples from the plant variety Arabidopsis thaliana have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of the publicly available Arabidopsis datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated. We pointed out serious issues in the data normalization steps widely accepted and published recently in this context. We put together a web resource where gene expression relationships can be explored online which helps visualize the logical relationships between genes. We believe that this website will be useful in identifying important genes in different biological context. The web link is http://hegemon.ucsd.edu/plant/

Key Performance Indicators in Field Hospital Appraisal: A Systematic Review

Background: Field hospitals are health care institutions with mobile or fixed structures. Although there have been numerous models and indicators for assessing the performance of public hospitals, there is no model to evaluate the performance of field hospitals. Objectives: This study was aimed at determining key performance indicators in field hospital appraisal. Methods: In this study, we conducted a systematic review of publications in English or Persian language indexed by PubMed, Scopus, Emerald, Elsevier, Ovid, Google Scholar, Springer, ProQuest, WHO and Word Bank databases. PICO strategy was used for searching databases. Quality assessment of the publications were carried out using CASP checklist. Similarly, the preferred reporting items for PRISMA checklist were used to assess systematic reviews. The PRISMA checklist was used to guide the reporting of the systematic review. A descriptive summary with data tables was produced to summarize the literature. Following the results of our search, 592 publications were retrieved and 352 citations were excluded because of irrelevance or duplication. After excluding the duplicate and irrelevant items we screened 240 titles and abstracts. Two independent reviewers evaluated 240 potentially relevant studies, and 15 records met the criteria to be included in this review. Results: We found 13 criteria on the assessment of field hospital in the literature. We classified all the retrieved indicators according to the system approach. The results of this study showed that input indicators included 4 indicators, process indicators included 2 indicators, output indicators consisted of 4 indicators and outcome indicators involved 3 indicators. Conclusions: This study highlights the most important performance measurement indicators in field hospitals with a system approach. There was no model to assess the field hospitals; however, a systematic approach in assessment can improve the quality of services

GeneCAT—novel webtools that combine BLAST and co-expression analyses

The gene co-expression analysis toolbox (GeneCAT) introduces several novel microarray data analyzing tools. First, the multigene co-expression analysis, combined with co-expressed gene networks, provides a more powerful data mining technique than standard, single-gene co-expression analysis. Second, the high-throughput Map-O-Matic tool matches co-expression pattern of multiple query genes to genes present in user-defined subdatabases, and can therefore be used for gene mapping in forward genetic screens. Third, Rosetta combines co-expression analysis with BLAST and can be used to find ‘true’ gene orthologs in the plant model organisms Arabidopsis thaliana and Hordeum vulgare (Barley). GeneCAT is equipped with expression data for the model plant A. thaliana, and first to introduce co-expression mining tools for the monocot Barley. GeneCAT is available at http://genecat.mpg.d

Function Annotation of an SBP-box Gene in Arabidopsis Based on Analysis of Co-expression Networks and Promoters

The SQUAMOSA PROMOTER BINDING PROTEIN–LIKE (SPL) gene family is an SBP-box transcription family in Arabidopsis. While several physiological responses to SPL genes have been reported, their biological role remains elusive. Here, we use a combined analysis of expression correlation, the interactome, and promoter content to infer the biological role of the SPL genes in Arabidopsis thaliana. Analysis of the SPL-correlated gene network reveals multiple functions for SPL genes. Network analysis shows that SPL genes function by controlling other transcription factor families and have relatives with membrane protein transport activity. The interactome analysis of the correlation genes suggests that SPL genes also take part in metabolism of glucose, inorganic salts, and ATP production. Furthermore, the promoters of the correlated genes contain a core binding cis-element (GTAC). All of these analyses suggest that SPL genes have varied functions in Arabidopsis

Multi-dimensional correlations for gene coexpression and application to the large-scale data of Arabidopsis

Background: Recent improvements in DNA microarray techniques have made a large variety of gene expression data available in public databases. This data can be used to evaluate the strength of gene coexpression by calculating the correlation of expression patterns among different genes between many experiments. However, gene expression levels differ significantly across various tissues in higher organisms, as well as in different cellular location in eukaryotes in different cell state. Thus the usual correlation measure can only evaluate the difference of tissues or cellular localizations, and cannot adequately elucidate the functional relationship from the coexpression of genes