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An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data  [PDF]
Daniel Ramsk?ld,Eric T. Wang,Christopher B. Burge ,Rickard Sandberg
PLOS Computational Biology , 2009, DOI: 10.1371/journal.pcbi.1000598
Abstract: The parts of the genome transcribed by a cell or tissue reflect the biological processes and functions it carries out. We characterized the features of mammalian tissue transcriptomes at the gene level through analysis of RNA deep sequencing (RNA-Seq) data across human and mouse tissues and cell lines. We observed that roughly 8,000 protein-coding genes were ubiquitously expressed, contributing to around 75% of all mRNAs by message copy number in most tissues. These mRNAs encoded proteins that were often intracellular, and tended to be involved in metabolism, transcription, RNA processing or translation. In contrast, genes for secreted or plasma membrane proteins were generally expressed in only a subset of tissues. The distribution of expression levels was broad but fairly continuous: no support was found for the concept of distinct expression classes of genes. Expression estimates that included reads mapping to coding exons only correlated better with qRT-PCR data than estimates which also included 3′ untranslated regions (UTRs). Muscle and liver had the least complex transcriptomes, in that they expressed predominantly ubiquitous genes and a large fraction of the transcripts came from a few highly expressed genes, whereas brain, kidney and testis expressed more complex transcriptomes with the vast majority of genes expressed and relatively small contributions from the most expressed genes. mRNAs expressed in brain had unusually long 3′UTRs, and mean 3′UTR length was higher for genes involved in development, morphogenesis and signal transduction, suggesting added complexity of UTR-based regulation for these genes. Our results support a model in which variable exterior components feed into a large, densely connected core composed of ubiquitously expressed intracellular proteins.
The Lognormal Distribution and Quantum Monte Carlo Data  [PDF]
Mervlyn Moodley
Physics , 2003,
Abstract: Quantum Monte Carlo data are often afflicted with distributions that resemble lognormal probability distributions and consequently their statistical analysis can not be based on simple Gaussian assumptions. To this extent a method is introduced to estimate these distributions and thus give better estimates to errors associated with them. This method is applied to a simple quantum model utilizing the single-thread Monte Carlo algorithm to estimate ground state energies.
Mining mouse microarray data
Dennis A Wigle, Janet Rossant, Igor Jurisica
Genome Biology , 2001, DOI: 10.1186/gb-2001-2-7-reviews1019
Abstract: Most articles on microarray technology, particularly reviews, begin with some over-reaching statement on their potential to illuminate the biological world. While resisting this temptation, we must acknowledge the increasing power of high-throughput expression profiling using high-density arrays. A quick Medline search of all papers referenced with the term 'microarray' shows an exponential increase since the first paper describing the approach by Pat Brown's group six years ago [1] (Figure 1). A calculated projection based on the number of papers this year to date predicts a total of around 500 microarray publications for 2001. The technology is quickly becoming a mainstay in the 'array' of tools available to the molecular biologist, and we expect it to be broadly applicable to our favorite genetically tractable organism, the laboratory mouse.The production of mouse microarrays has lagged somewhat behind the production of arrays from humans and from those model organisms for which full genome sequence is available, such as yeast or Caenorhabditis elegans. Oligonucleotide-based arrays from sources such as Affymetrix (Santa Clara, CA, USA) have been in production for a number of years, but their high cost has been a barrier to widespread general use in academia. The cDNA array effort has been hampered largely by the scarcity of large, well-annotated cDNA clone sets from mouse tissues. Fortunately, a number of recent publications have addressed this issue. Minoru Ko at the National Institute on Aging (NIA; National Institutes of Health, Bethesda, MD, USA) has developed a 15,247 clone set derived from mice largely at early developmental time points, with libraries covering stages from the early blastocyst to embryonic day (E) 7.5 (for which there are embryo and ectoplacental cone samples) [2]. This set will have added to it later this year a further 11,000 clones derived from sequencing of similar libraries, and libraries from trophoblast stem cells, hematopoietic stem
Evaluation of normalization methods for microarray data
Taesung Park, Sung-Gon Yi, Sung-Hyun Kang, SeungYeoun Lee, Yong-Sung Lee, Richard Simon
BMC Bioinformatics , 2003, DOI: 10.1186/1471-2105-4-33
Abstract: In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.Biological processes depend on complex interactions between many genes and gene products. To understand the role of a single gene or gene product in this network, many different types of information, such as genome-wide knowledge of gene expression, will be needed. Microarray technology is a useful tool to understand gene regulation and interactions [1-3]. For example, cDNA microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. cDNA microarrays consist of thousands of individual DNA sequences printed in a high density array on a glass slide. After being reverse-transcribed into cDNA and labelled using red (Cy5) and green (Cy3) fluorescent dyes, two target mRNA samples are hybridized with the arrayed DNA sequences or probes. Then, the relative abundance of these spotted DNA sequences can be measured. After image analysis, for each gene the data consist of two fluorescence intensity measurements, (R, G), showing the expression level of the gene in the red and green labelled mRNA samples. The ratio of the fluorescence intensity for each spot represents the relative abundance of the corresponding DNA sequence. cDNA microarray technology has important applications in pharmaceutical and clinical research. By comparing gene expression in normal and tumor tissues, for example, microarrays
Predictive analysis of microarray data  [PDF]
Paulo C. Marques F.,Carlos A. de B. Pereira
Statistics , 2013,
Abstract: Microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and a classifier.
Analysis of microarray expression data
Paul Kellam
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-1-reports226
Abstract: Navigation is easy from the introduction page and there are clear links to the main resources of the site within this page. From these, jump-off points are well described and easy to follow.Last modified on 11 August 1999.The major use of the site in its present form is to provide current links and detailed information on the use and analysis of microarrays and the future development of the EBI's gene expression database. This it does extremely well, and it is an excellent starting place for individuals wanting to begin microarray research.The most frustrating feature is not being able to upload your own datasets into Expression Profiler at this stage of its development.The large datasets used in this sort of research will need a powerful server for the use of Expression Profiler. It would also be good if components of the Expression Profiler system could be downloaded to run on local machines for more 'array intensive' laboratories.Further information on microarray data analysis can be found at Expression Profiler, The microarray project and Patrick Brown's laboratory homepage.The ArrayExpress databaseExpression ProfilerThe microarray projectPatrick Brown's laboratory
The miracle of microarray data analysis
Yuk Fai Leung, Dennis Shun Chiu Lam, Chi Pui Pang
Genome Biology , 2001, DOI: 10.1186/gb-2001-2-9-reports4021
Abstract: Life scientists are facing an unprecedented data-analysis challenge. The large datasets from genomic or proteomic studies are not easily analyzed by traditional one-by-one genetics. As a result, we have to bring together expertise from statistics, mathematics, bioinformatics, and computer science to handle such tasks. This conference successfully facilitated interaction between experts from various fields, sparked off many new ideas, and addressed challenges from several areas, such as microarray data mining, gene prediction, and pathway reconstruction.Several speakers emphasized the importance of getting better tests of statistical significance for the patterns of differentially expressed genes discovered using microarrays. Thomas D. Wu (Genentech Inc., San Francisco, USA) started the conference by reviewing various types of supervised and unsupervised learning methods for analyzing microarray data. Tom Downey (Partek Inc., St. Charles, USA) pointed out that commonly used visual-analysis methods are limited because their interpretation is subjective and the statistical significance of the results is not measurable. As a result, such methods are difficult to automate. Downey discussed several statistical tests, such as Student's t test and analysis of variance (ANOVA) that can be used to identify significantly different gene expression in different experimental conditions. He recommended applying statistical methods to microarray data in order to automate data analysis and make it objective. Kenneth Hess (University of Texas, Houston, USA) explored the role of replication in experiments. Using the fact that the variation in intensity ratio between replicates is inversely proportional to the magnitude of the ratio, he identified the differentially expressed genes from his microarray experiments as those with intensity-ratio variation different from the expected variation. He too suggested using t-statistics to identify the genes that are most reliably differentially
Predictive Analysis of Microarray Data  [PDF]
Paulo C. Marques F., Carlos A. de B. Pereira
Open Journal of Genetics (OJGen) , 2014, DOI: 10.4236/ojgen.2014.41009
Abstract:

Microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and the corresponding classifier.

Effective affinities in microarray data  [PDF]
T. Heim,J. Klein Wolterink,E. Carlon,G. T. Barkema
Quantitative Biology , 2006, DOI: 10.1088/0953-8984/18/18/S03
Abstract: In the past couple of years several studies have shown that hybridization in Affymetrix DNA microarrays can be rather well understood on the basis of simple models of physical chemistry. In the majority of the cases a Langmuir isotherm was used to fit experimental data. Although there is a general consensus about this approach, some discrepancies between different studies are evident. For instance, some authors have fitted the hybridization affinities from the microarray fluorescent intensities, while others used affinities obtained from melting experiments in solution. The former approach yields fitted affinities that at first sight are only partially consistent with solution values. In this paper we show that this discrepancy exists only superficially: a sufficiently complete model provides effective affinities which are fully consistent with those fitted to experimental data. This link provides new insight on the relevant processes underlying the functioning of DNA microarrays.
Deafness Gene Expression Patterns in the Mouse Cochlea Found by Microarray Analysis  [PDF]
Hidekane Yoshimura, Yutaka Takumi, Shin-ya Nishio, Nobuyoshi Suzuki, Yoh-ichiro Iwasa, Shin-ichi Usami
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0092547
Abstract: Background Tonotopy is one of the most fundamental principles of auditory function. While gradients in various morphological and physiological characteristics of the cochlea have been reported, little information is available on gradient patterns of gene expression. In addition, the audiograms in autosomal dominant non syndromic hearing loss can be distinctive, however, the mechanism that accounts for that has not been clarified. We thought that it is possible that tonotopic gradients of gene expression within the cochlea account for the distinct audiograms. Methodology/Principal Findings We compared expression profiles of genes in the cochlea between the apical, middle, and basal turns of the mouse cochlea by microarray technology and quantitative RT-PCR. Of 24,547 genes, 783 annotated genes expressed more than 2-fold. The most remarkable finding was a gradient of gene expression changes in four genes (Pou4f3, Slc17a8, Tmc1, and Crym) whose mutations cause autosomal dominant deafness. Expression of these genes was greater in the apex than in the base. Interestingly, expression of the Emilin-2 and Tectb genes, which may have crucial roles in the cochlea, was also greater in the apex than in the base. Conclusions/Significance This study provides baseline data of gradient gene expression in the cochlea. Especially for genes whose mutations cause autosomal dominant non syndromic hearing loss (Pou4f3, Slc17a8, Tmc1, and Crym) as well as genes important for cochlear function (Emilin-2 and Tectb), gradual expression changes may help to explain the various pathological conditions.
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