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Search Results: 1 - 10 of 201281 matches for " Grier P Page "
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Bioinformatic Tools for Inferring Functional Information from Plant Microarray Data II: Analysis Beyond Single Gene
Issa Coulibaly,Grier P. Page
International Journal of Plant Genomics , 2008, DOI: 10.1155/2008/893941
Abstract: While it is possible to interpret microarray experiments a single gene at a time, most studies generate long lists of differentially expressed genes whose interpretation requires the integration of prior biological knowledge. This prior knowledge is stored in various public and private databases and covers several aspects of gene function and biological information. In this review, we will describe the tools and places where to find prior accurate biological information and how to process and incorporate them to interpret microarray data analyses. Here, we highlight selected tools and resources for gene class level ontology analysis (Section 2), gene coexpression analysis (Section 3), gene network analysis (Section 4), biological pathway analysis (Section 5), analysis of transcriptional regulation (Section 6), and omics data integration (Section 7). The overall goal of this review is to provide researchers with tools and information to facilitate the interpretation of microarray data.
Bioinformatic Tools for Inferring Functional Information from Plant Microarray Data: Tools for the First Steps
Grier P. Page,Issa Coulibaly
International Journal of Plant Genomics , 2008, DOI: 10.1155/2008/147563
Abstract: Microarrays are a very powerful tool for quantifying the amount of RNA in samples; however, their ability to query essentially every gene in a genome, which can number in the tens of thousands, presents analytical and interpretative problems. As a result, a variety of software and web-based tools have been developed to help with these issues. This article highlights and reviews some of the tools for the first steps in the analysis of a microarray study. We have tried for a balance between free and commercial systems. We have organized the tools by topics including image processing tools (Section 2), power analysis tools (Section 3), image analysis tools (Section 4), database tools (Section 5), databases of functional information (Section 6), annotation tools (Section 7), statistical and data mining tools (Section 8), and dissemination tools (Section 9).
Cross-Chip Probe Matching Tool: A Web-Based Tool for Linking Microarray Probes within and across Plant Species
Ruchi Ghanekar,Vinodh Srinivasasainagendra,Grier P. Page
International Journal of Plant Genomics , 2008, DOI: 10.1155/2008/451327
Abstract: The CCPMT is a free, web-based tool that allows plant investigators to rapidly determine if a given gene is present across various microarray platforms, which, of a list of genes, is present on array(s), and which gene a probe or probe set queries and vice versa, and to compare and contrast the gene contents of arrays. The CCPMT also maps a probe or probe sets to a gene or genes within and across species, and permits the mapping of the entire content from one array to another. By using the CCPMT, investigators will have a better understanding of the contents of arrays, a better ability to link data between experiments, ability to conduct meta-analysis and combine datasets, and an increased ability to conduct data mining projects.
Evaluating Statistical Methods Using Plasmode Data Sets in the Age of Massive Public Databases: An Illustration Using False Discovery Rates
Gary L. Gadbury,Qinfang Xiang,Lin Yang,Stephen Barnes,Grier P. Page,David B. Allison
PLOS Genetics , 2008, DOI: 10.1371/journal.pgen.1000098
Abstract: Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods.
A proposed metric for assessing the measurement quality of individual microarrays
Kyoungmi Kim, Grier P Page, T Mark Beasley, Stephen Barnes, Katherine E Scheirer, David B Allison
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-35
Abstract: We hypothesized that an index of the degree of spatiality of gene expression measurements associated with their physical geographic locations on an array could indicate the summary of the physical reliability of the microarray. We introduced a novel way to formulate this index using a statistical analysis tool. Our approach regressed gene expression intensity measurements on a polynomial response surface of the microarray's Cartesian coordinates. We demonstrated this method using a fixed model and presented results from real and simulated datasets.We demonstrated the potential of such a quantitative metric for assessing the reliability of individual arrays. Moreover, we showed that this procedure can be incorporated into laboratory practice as a means to set quality control specifications and as a tool to determine whether an array has sufficient quality to be retained in terms of spatial correlation of gene expression measurements.Gene expression microarrays are a powerful tool used in molecular biology and genetics for understanding gene expression change in biological processes under normal and pathological conditions [1]. Intensity measurements of gene expression are associated with significant variations as a result of the complex and multi-stage processing involved in microarray experiments. Beyond the variability that may be introduced during the fabrication of arrays as a result of print substrate quality and printing pin anomalies, several processing steps – mRNA sample extraction, amplification and labeling, hybridization, and scanning – may introduce substantial variation in measurements [2]. Although several studies have characterized the potential impact of these latter sources of variation on measurements of gene expression [2-4], methods for assessing the physical measurement quality of individual microarrays are not widely available. If technical replicates for a biological case are available, the degree of concordance between technical replicates ca
Transcriptional reprogramming of gene expression in bovine somatic cell chromatin transfer embryos
Nelida Rodriguez-Osorio, Zhongde Wang, Poothappillai Kasinathan, Grier P Page, James M Robl, Erdogan Memili
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-190
Abstract: Bovine clones from up to four generations of successive cloning were produced by chromatin transfer (CT). Using Affymetrix bovine microarrays we determined that the transcriptomes of blastocysts derived from the first and the fourth rounds of cloning (CT1 and CT4 respectively) have undergone an extensive reprogramming and were more similar to blastocysts derived from in vitro fertilization (IVF) than to the donor cells used for the first and the fourth rounds of chromatin transfer (DC1 and DC4 respectively). However a set of transcripts in the cloned embryos showed a misregulated pattern when compared to IVF embryos. Among the genes consistently upregulated in both CT groups compared to the IVF embryos were genes involved in regulation of cytoskeleton and cell shape. Among the genes consistently upregulated in IVF embryos compared to both CT groups were genes involved in chromatin remodelling and stress coping.The present study provides a data set that could contribute in our understanding of epigenetic errors in somatic cell chromatin transfer. Identifying "cumulative errors" after serial cloning could reveal some of the epigenetic reprogramming blocks shedding light on the reprogramming process, important for both basic and applied research.The process of early embryonic development is determined by activation of the embryonic genome, which for bovine embryos begins as a "minor genome activation" at the 1-cell stage [1] ascending to a "major genome activation" during the 8-cell to 16-cell stage [2]. In the absence of proper genome activation, the developing embryo will die because it can no longer support its essential developmental functions [3,4]. In the case of embryos produced by somatic cell nuclear transfer (SCNT) the somatic nucleus has to be reprogrammed in order to restart and continue the developmental process. It is believed that, guided by the ooplasm, the somatic nucleus aborts its own program of somatic gene expression and re-establishes a particular
The PowerAtlas: a power and sample size atlas for microarray experimental design and research
Grier P Page, Jode W Edwards, Gary L Gadbury, Prashanth Yelisetti, Jelai Wang, Prinal Trivedi, David B Allison
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-84
Abstract: To address this challenge, we have developed a Microrarray PowerAtlas [1]. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO). The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC).This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.Planning microarray studies provides unique challenges to investigators with respect to estimating power and the sample size required for a study. The questions proposed may be quite general and exploratory, such as "which genes are differentially expressed in response to a given treatment?" A microarray study should have a high probability to answer, at least in part, the questions and hypotheses being proposed [loosely speaking, power or in our case Expected Discovery Rate (EDR)]. It should also have a high probability that those genes declared significant are truly differentially expressed (i.e. the 'True Positive' probability should be high). Sample size is a critical determinant of statistical power and expected error rates.In traditional biomedical studies, investigators test one or at most a few hypotheses. This is not the case in microarray studies. Each treatment or group comparison involves the testing of every gene on the chip, which may number in the 10,000's. Some microarray experiments may involve multiple groups; thus the total number of hypotheses tested in a microarray experiment can run in the 100,000 s or more. In addition, the effects size and variance for each hypothe
Correlation of microRNA levels during hypoxia with predicted target mRNAs through genome-wide microarray analysis
Jennifer S Guimbellot, Stephen W Erickson, Tapan Mehta, Hui Wen, Grier P Page, Eric J Sorscher, Jeong S Hong
BMC Medical Genomics , 2009, DOI: 10.1186/1755-8794-2-15
Abstract: To identify changes induced by hypoxia, we conducted mRNA- and miRNA-array-based experiments in HT29 cells, and performed comparative analysis of the resulting data sets based on multiple target prediction algorithms. To date, few studies have investigated an environmental perturbation for effects on genome-wide miRNA levels, or their consequent influence on mRNA output.Comparison of miRNAs with predicted mRNA targets indicated a lower level of concordance than expected. We did, however, find preliminary evidence of combinatorial regulation of mRNA expression by miRNA.Target prediction programs and expression profiling techniques do not yet adequately represent the complexity of miRNA-mediated gene repression, and new methods may be required to better elucidate these pathways. Our data suggest the physiologic impact of miRNAs on cellular transcription results from a multifaceted network of miRNA and mRNA relationships, working together in an interconnected system and in context of hundreds of RNA species. The methods described here for comparative analysis of cellular miRNA and mRNA will be useful for understanding genome wide regulatory responsiveness and refining miRNA predictive algorithms.MicroRNAs (miRNA) are approximately 22-nucleotide, non-coding RNA sequences important in the control of gene expression. They are involved in a variety of cellular processes, including development, cell differentiation, signaling, and tumorigenesis[1], and are believed to represent 1% of the predicted genes in mammalian and nematode genomes[2,3]. Mammals in general (and primates in particular) appear to have a large number of miRNAs not found in other animal orders[2], suggesting that many functional miRNAs may have emerged during recent evolutionary periods. According to current functional and predictive models, each miRNA regulates multiple genes during differentiation and/or development at the transcription, translation, and posttranslational levels[1,4,5]. However, few of t
Overexpression of Constans Homologs CO1 and CO2 Fails to Alter Normal Reproductive Onset and Fall Bud Set in Woody Perennial Poplar
Chuan-Yu Hsu, Joshua P. Adams, Kyoungok No, Haiying Liang, Richard Meilan, Olga Pechanova, Abdelali Barakat, John E. Carlson, Grier P. Page, Cetin Yuceer
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0045448
Abstract: CONSTANS (CO) is an important flowering-time gene in the photoperiodic flowering pathway of annual Arabidopsis thaliana in which overexpression of CO induces early flowering, whereas mutations in CO cause delayed flowering. The closest homologs of CO in woody perennial poplar (Populus spp.) are CO1 and CO2. A previous report [1] showed that the CO2/FLOWERING LOCUS T1 (FT1) regulon controls the onset of reproduction in poplar, similar to what is seen with the CO/FLOWERING LOCUS T (FT) regulon in Arabidopsis. The CO2/FT1 regulon was also reported to control fall bud set. Our long-term field observations show that overexpression of CO1 and CO2 individually or together did not alter normal reproductive onset, spring bud break, or fall dormancy in poplar, but did result in smaller trees when compared with controls. Transcripts of CO1 and CO2 were normally most abundant in the growing season and rhythmic within a day, peaking at dawn. Our manipulative experiments did not provide evidence for transcriptional regulation being affected by photoperiod, light intensity, temperature, or water stress when transcripts of CO1 and CO2 were consistently measured in the morning. A genetic network analysis using overexpressing trees, microarrays, and computation demonstrated that a majority of functionally known genes downstream of CO1 and CO2 are associated with metabolic processes, which could explain their effect on tree size. In conclusion, the function of CO1 and CO2 in poplar does not appear to overlap with that of CO from Arabidopsis, nor do our data support the involvement of CO1 and CO2 in spring bud break or fall bud set.
Breast fibroblasts modulate epithelial cell proliferation in three-dimensional in vitro co-culture
Andrea Sadlonova, Zdenek Novak, Martin R Johnson, Damon B Bowe, Sandra R Gault, Grier P Page, Jaideep V Thottassery, Danny R Welch, Andra R Frost
Breast Cancer Research , 2004, DOI: 10.1186/bcr949
Abstract: NAF and CAF were grown with the nontumorigenic MCF10A epithelial cells and their more transformed, tumorigenic derivative, MCF10AT cells, in direct three-dimensional co-cultures on basement membrane material. The proliferation and apoptosis of MCF10A cells and MCF10AT cells were assessed by 5-bromo-2'-deoxyuridine labeling and TUNEL assay, respectively. Additionally, NAF and CAF were compared for expression of insulin-like growth factor II as a potential mediator of their effects on epithelial cell growth, by ELISA and by quantitative, real-time PCR.In relatively low numbers, both NAF and CAF suppressed proliferation of MCF10A cells. However, only NAF and not CAF significantly inhibited proliferation of the more transformed MCF10AT cells. The degree of growth inhibition varied among NAF or CAF from different individuals. In greater numbers, NAF and CAF have less inhibitory effect on epithelial cell growth. The rate of epithelial cell apoptosis was not affected by NAF or CAF. Mean insulin-like growth factor II levels were not significantly different in NAF versus CAF and did not correlate with the fibroblast effect on epithelial cell proliferation.Both NAF and CAF have the ability to inhibit the growth of pre-cancerous breast epithelial cells. NAF have greater inhibitory capacity than CAF, suggesting that the ability of fibroblasts to inhibit epithelial cell proliferation is lost during breast carcinogenesis. Furthermore, as the degree of transformation of the epithelial cells increased they became resistant to the growth-inhibitory effects of CAF. Insulin-like growth factor II could not be implicated as a contributor to this differential effect of NAF and CAF on epithelial cell growth.The structure and homeostasis of normal breast parenchyma is maintained by dynamic interactions between breast epithelial cells and their associated stroma. These stromal elements include the vasculature, adipocytes, resident immune cells, and fibroblasts with their numerous cellular p
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