RNA Transcription Detected on Chromosomes 21 and 22 Using High Density Oligonucleotide Arrays. Thomas R. Gingeras, Affymetrix Inc., Santa Clara, CA. The first drafts of complete human genome sequence have brought with them the opportunities to map the RNA transcription patterns that are characteristic of each differentiated and undifferentiated cell type and characterize the sequence variations that underlie the phenotypic differences observed in the human population. By using the very high information content inherent in high-density oligonucleotide arrays it will be possible to map the locations of RNA transcription along the length of the entire human genome. Such a transcriptome map will provide information concerning: 1) the identification of novel transcription domains of the genome, 2) the predominant utilization of exon sequences during differentially spliced gene expression and 3) a empirically derived set of results which can be compared to the sequence annotation now being assembled ...
EMEA Oligonucleotide DNA Microarrays (oDNA) market analysis of an industry is a crucial thing for various stakeholders like investors, CEOs, traders, suppliers and others. The Oligonucleotide DNA Microarrays (oDNA) industry research report is a resource, which provides current as well as upcoming technical and financial details of the industry.. Oligonucleotide DNA Microarrays (oDNA) market 2017-2022 research report is a professional and in-depth study on the current state of this market. Various definitions and classification of the industry, applications of the industry and chain structure are given. Present day status of the Oligonucleotide DNA Microarrays (oDNA) industry policies and news are analysed.. Following are major Table of Content of Oligonucleotide DNA Microarrays (oDNA) Industry:. Oligonucleotide DNA Microarrays (oDNA) Market Competition by Manufacturers, Oligonucleotide DNA Microarrays (oDNA) Production, Revenue by Region, Oligonucleotide DNA Microarrays (oDNA) Supply, ...
TY - JOUR. T1 - DNA microarray-based identification of bacterial and fungal pathogens in bloodstream infections. AU - Yoo, Seung Min. AU - Choi, JunYong. AU - Yun, Jung Kuk. AU - Choi, Jae Kyung. AU - Shin, So Youn. AU - Lee, Kyungwon. AU - Kim, June Myung. AU - Lee, Sang Yup. PY - 2010/2/1. Y1 - 2010/2/1. N2 - The accurate and rapid identification of pathogens in blood is a major challenge in clinical pathogen diagnostics because of the high mortality of sepsis. Here we report the development of DNA microarray for the identification of pathogens causing bloodstream infections. Species-specific and bacteria- and fungi-broad-ranged probes were designed to identify 50 bacteria and 7 fungi. The specificities and sensitivities of the selected probes were successfully validated by applying reference strains. To assess the performance of the DNA microarray in a clinical setting, blind tests were performed using 112 blood culture specimens that showed preliminary presence of pathogenic microorganisms ...
A DNA microarray is a solid support such as a glass slide, silicon chip or nylon membrane on which DNA molecules are attached at precise locations. Using DNA microarrays, the expression of tens of thousands of genes in a biological sample can be detected in one experiment. Emerging data suggests that the use of DNA microarrays can aid the differentiation of tumors with similar morphological appearance, predict patient outcome independently of conventional prognostic factors and select for response or resistance to specific anti-cancer therapies. DNA microarray technology thus has the potential to supplement standard diagnostic procedures in oncology and permit a more individualized approach to patient management. Prior to clinical application, however, this methodology must be simplified, standardized, evaluated in external quality assessment schemes and made available at relatively low costs. Most importantly, the preliminary, but promising, early findings must be validated by high-level ...
The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms.
TY - JOUR. T1 - Application of high-density DNA microarray to study smoke- and hydrogen peroxide-induced injury and repair in human bronchial epithelial cells. AU - Yoneda, Ken Y. AU - Mann-Jong Chang, Mary. AU - Chmiel, Ken. AU - Chen, Yin. AU - Wu, Reen. PY - 2003/8/1. Y1 - 2003/8/1. N2 - Recent advances in high-density DNA microarray technique allow the possibility to analyze thousands of genes simultaneously for their differential gene expression patterns in various biologic processes. Through clustering analysis and pattern recognition, the significance of these differentially expressed genes can be recognized and correlated with the biologic events that may take place inside the cell and tissue. High-density DNA microarray nylon membranes were used to explore gene expression and regulation associated with smoke-and hydrogen peroxide-induced injury and repair in differentiated human bronchial epithelial cells in vitro. At least three phases of change in gene expression could be recognized. ...
High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated ...
Array-based comparative genomic hybridization (CGH) and gene expression profiling have become vital techniques for identifying molecular defects underlying genetic diseases. Regardless of the microarray platform, cyanine dyes (Cy3 and Cy5) are one of the most widely used fluorescent dye pairs for microarray analysis owing to their brightness and ease of incorporation, enabling high level of assay sensitivity. However, combining both dyes on arrays can become problematic during summer months when ozone levels rise to near 25 parts per billion (ppb). Under such conditions, Cy5 is known to rapidly degrade leading to loss of signal from either homebrew or commercial arrays. Cy5 can also suffer disproportionately from dye photobleaching resulting in distortion of (Cy5/Cy3) ratios used in copy number analysis. Our laboratory has been active in fluorescent dye research to find a suitable alternative to Cy5 that is stable to ozone and resistant to photo-bleaching. Here, we report on the development of such a
Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not. A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological
TY - JOUR. T1 - A Sequence Based Validation of Gene Expression Microarray Data. AU - Thallinger, Gerhard. AU - Obermayr, Eva. AU - Charoentong, Pornpimol. AU - Tong, Dan. AU - Trajanoski, Zlatko. AU - Zeillinger, Robert. PY - 2012. Y1 - 2012. UR - http://thescipub.com/ajb.toc. U2 - 10.3844/ajbsp.2012.1.9. DO - 10.3844/ajbsp.2012.1.9. M3 - Article. VL - 1. SP - 1. EP - 9. JO - American journal of bioinformatics. JF - American journal of bioinformatics. SN - 1948-9862. IS - 1. ER - ...
This example shows how to use MATLAB® and Bioinformatics Toolbox™ for preprocessing Affymetrix® oligonucleotide microarray probe-level data with two preprocessing techniques, Robust Multi-array Average (RMA) and GC Robust Multi-array Average (GCRMA).
contribute to the vascular remodeling process associated with hypertension and atherosclerosis, the aims of this study were to assess the impact of 2-ME on pathophysiological pathways regulating SMC growth using transcriptional profiling. High-density oligonucleotide microarrays (Affymetrix Human Genome U_133 Plus 2.0 GeneChips) were used to identify differentially expressed genes in cultured human aortic SMCs treated with 2-ME (acutely, for 4 hrs, n=3; and chronically, for 48 hrs, n=3) and vehicle-treated time-matched controls (n=3 for each time point). Both single gene analysis (performed using Significance Analysis of Microarrays) as well as Gene Set Enrichment Analysis (GSEA, a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states) indicated downregulation of genes critically involved in mitotic spindle assembly and function in SMCs chronically treated with 2-ME when compared to ...
This introductory course on microarray analysis is targeted to people without previous knowledge on this field. We will cover the basic steps that should be followed to obtain a list of differentially expressed genes, starting from raw expression data. The theoretical part of the course will be comprehensive and the similarities and differences between one- and two-color arrays will be discussed. For the practical part, an already published public dataset of Agilent two-color arrays will be used for all subsequent hands-on work. Students will get familiar with raw data format, background subtraction and normalization procedures that are needed before any differential expression analysis ...
With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis. Two microarray data sets based on a 17k cDNA microarray system were used, consisting of 82 normal colon mucosa and 72 colorectal cancer tissues. Each data set was prepared from either total RNA or amplified mRNA, and the difference of RNA source between these two data sets was detected by ANOVA (Analysis of variance) model. A simple integration method was introduced which was based on the distributions of gene expression ratios among different microarray data sets. The method transformed gene
Arrayit offers complete microarray services including microarray design, microarray sample preparation, microarray labeling, microarray amplification, microarray hybridization, microarray processing, microarray scanning, and microarray data analysis. Our high-throughput microarray cleanroom facilities enable a microarray service to meet the needs of every research laboratory, biotech, pharmaceutical company, hospitical and clinic. DNA microarrays, protein microarrays, peptide microarrays and VIP microarrays are included in our services offerings.
With the microarray technology rapidly advanced, tiling arrays have quickly become one of the most powerful tools in genome-wide investigations. High density tiling arrays [1] can be used to address many biological problems such as transcriptome mapping, protein-DNA interaction mapping (ChIP-chip) and array CGH among others [2]. ChIP-chip [3], the focus of the paper, is a technique that combines chromatin immunoprecipitation (ChIP) with microarray technology (chip). It allows efficient, scalable and comprehensive identification of binding sites and profiles of DNA-binding proteins [4]. High density ChIP-chip tiling arrays not only help us map the binding sites of a protein in the genome, but also allow us to better understand the binding events of the protein by clearly displaying the binding occupancy profiles. Several methods have been proposed to analyze the ChIP-chip data; for example, Joint Binding De-convolution (JBD) [5] uses a probabilistic graphical model to improve spatial resolution ...
CGH stands for comparative genome hybridisation, which aims to compare the presence / absence / number of similar genes in 2 genomes (i.e. its a survey of genetic differences between 2 organisms). So, a CGH experiment might compare gemomic DNA from 2 closely related bacterial species or strains. The difference between CGH array experiments and gene expression array experiments is just the target which is hybridised to the array: labelled genomic DNA for CGH; labelled cDNA (or cRNA - derived from mRNA in either case) for gene expression. You can use exactly the same arrays for both types of experiment, although the objective of CGH is normally genome-wide comparison of 2 genomes, so CGH normally uses whole-genome microarrays (for prokaryotes, at least). In general, you can use any genomic array for either gene expression or CGH ...
We performed a Nimblegen intra-platform microarray comparison by assessing two categories of flax target probes (short 25-mers oligonucleotides and long 60-mers oligonucleotides) in identical conditions of target production, design, labelling, hybridization, image analyses, and data filtering. We compared technical parameters of array hybridizations, precision and accuracy as well as specific gene expression profiles. Comparison of the hybridization quality, precision and accuracy of expression measurements, as well as an interpretation of differential gene expression in flax tissues were performed. Both array types yielded reproducible, accurate and comparable data that are coherent for expression measurements and identification of differentially expressed genes. 60-mers arrays gave higher hybridization efficiencies and therefore were more sensitive allowing the detection of a higher number of unigenes involved in the same biological process and/or belonging to the same multigene family. The two flax
Other. The DNA Microarray report does the thorough study of the key industry players to understand their business strategies, annual revenue, company profile and their contribution to the DNA Microarray market share in the United States. Diverse factors of the DNA Microarray industry like the supply chain scenario, industry standards, import/export details are also mentioned in this report.. Key Highlights of the United States DNA Microarray Market 2017 Report:. A Clear understanding of the DNA Microarray market based on growth, constraints, opportunities, feasibility study.. Concise DNA Microarray Market study based on major United States regions.. Analysis of evolving market segments as well as a complete study of existing DNA Microarray market segments.. Before Purchasing, Request Free Sample Copy of the Report Here: http://qyresearch.us/report/united-states-dna-microarray-market-2017/107122/#requestForSample. Furthermore, distinct aspects of DNA Microarray market like the technological ...
Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature ...
A literature search for non-uniform distributions of P values shows few citations [3, 5] and these both relate to statistical tests applied to expression results generated using Affymetrix microarrays. Huang et al. [5] compare gene expression profiles of tumours in three groups of mice using an Affymetrix Mouse Genechip array. When an ANOVA was performed on the expression of around 23,000 genes, a distribution of P values similar to Figure 1 was obtained. However when a t-test was applied to 2 of these groups, a distribution of P values similar to Figure 3 was obtained. The authors hypothesised that the reason for such a non-uniform distribution was due to excess biological similarity between some samples in the groups used for the t-test. This excess biological similarity was thought to be due to 2 pairs of samples being littermates of the same age and a further 2 pairs of samples were assayed at the same time. This resulted in the samples used for the t-test not being statistically ...
Oligonucleotide microarrays measure the relative transcript abundance of thousands of mRNAs in parallel. A large number of procedures for normalization and detection of differentially expressed genes have been proposed. However, the relative impact of these methods on the detection of differentially expressed genes remains to be determined. We have employed four different normalization methods and all possible combinations with three different statistical algorithms for detection of differentially expressed genes on a prototype dataset. The number of genes detected as differentially expressed differs by a factor of about three. Analysis of lists of genes detected as differentially expressed, and rank correlation coefficients for probability of differential expression shows that a high concordance between different methods can only be achieved by using the same normalization procedure. Normalization has a profound influence of detection of differentially expressed genes. This influence is higher than
Microarray technology is a powerful tool for genomic analysis. It gives a global view of the genome in a single experiment. Data analysis of the microarray is a vital part of the experiment. Each microarray study comprises multiple microarrays, each giving tens of thousands of data points. Since the volume of data is growing exponentially as microarrays grow larger, the analysis becomes more challenging. In general the greater the volume of data, the more chances arise for erroneous results. Handling such large volumes of data requires high-end computational infrastructures and programs that can handle multiple data formats. There are already programs available for microarray data analysis on various platforms. However, due to rapid development, diversity in microarray technology, and different data formats, there is always the need for more comprehensive and complete microarray data analysis. Proper data processing and quality control are critical to the validity and interpretability of gene ...
Apolipoprotein O (apoO) is a new member of the apolipoprotein family. However, data on its physiological functions are limited and inconsistent. Using a microarray expression analysis, this study explored the function of apoO in liver cells. HepG2 cells were treated either with oleic acid or tumor necrosis factor-α for 24 h. mRNA and protein expression of apoO were assessed by quantitative real-time PCR (qRT-PCR) and Western blot respectively. An efficient lentiviral siRNA vector targeting the human apoO gene was designed and constructed. The gene expression profile of HepG2 human hepatocellular carcinoma cells transfected with the apoO silencing vector was investigated using a whole-genome oligonucleotide microarray. The expression levels of some altered genes were validated using qRT-PCR. ApoO expression in HepG2 cells was dramatically affected by lipid and inflammatory stimuli. A total of 282 differentially expressed genes in apoO-silenced HepG2 cells were identified by microarray analysis. These
immune Uncategorized 155294-62-5 IC50, Rabbit Polyclonal to SNX4. We have assessed the tool of RNA titration examples for evaluating microarray system functionality and the influence of different normalization methods over the outcomes obtained. widespread make use of, many locally are concerned using the comparability from the outcomes attained using different microarray systems and therefore the natural relevance from the qualitative and quantitative outcomes obtained. Microarray system functionality has been examined before over the requirements of awareness, specificity, powerful range, accuracy1C12 and precision. Within the MicroArray Quality Control (MAQC) task, very similar assessments have already been reported13 also,14. Other research have used described mixtures of RNA examples (titration examples) for interplatform2,15 and interlaboratory15 evaluations. Here weve investigated an alternative solution functionality metric: the talents of different microarray systems to accurately ...
Microarrays provided a practical method for measuring the expression of thousand of genes simultaneously. Although next generation sequencing has mainly replaced these assays, there is still a large amount of data available in public databases, that would enable to better design sequencing experiment with the insight of an high-throughput gene expression screening. For this reasons methods that have been developed in the past to analyse microarrays gene expression data, are still a valuable resource. Microarray technology is associated with many significant sources of experimental uncertainty, which must be considered in order to make confident inference from the data. Estimate of uncertainty is not entirely achieved using repeat experiments. Outliers are often due to flaws in the microarray technique or to problems in the hybridization of the biological material. In high-density oligonucleotide arrays as well as in cDNA spotted arrays the aim is to extract from pixel intensity signals an ...
The human genome encodes approximately 100,000 different genes, and at least partial sequence information for nearly all will be available soon. Sequence information alone, however, is insufficient for a full understanding of gene function, expression, regulation, and splice-site variation. Because …
Microarray Detection and Characterization of Bacterial Foodborne Pathogens und Buchbewertungen gibt es auf ReadRate.com. Bücher können hier direkt online erworben werden.
STAT6 up or down regulation was defined like a 2 fold big difference in the imply expression degree within a offered information set. For examination ple, up regulation between GBM patients refers to a two fold increase in STAT6 expression, com pared to your normal STAT6 expression levels in all individuals inside of the GBM sub population. Therefore, just about every patient Inhibitors,Modulators,Libraries sub population has a distinct baseline, and person individuals STAT6 expression levels are only compared to other sufferers in the same sub population. Affymetrix microarray Microarray analysis of Affymetrix chips was performed as previously described in. Briefly, complete RNA was extracted from wild style and STAT6 deficient U 1242MG and U 87MG cells. Biotin labeled cRNA was ready from somewhere around two ug of total RNA and hybridized to Human Genome U133 plus two Affymetrix oligonucleotide arrays, which incorporate roughly 56,400 transcripts of human genes or ESTs.. Soon after washing ...
BACKGROUND: Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples. RESULTS: We demonstrate an asymmetry in the detection of the two alleles for each SNP, which deleteriously influences both allelic proportions and copy number estimates. The asymmetry is caused by a remaining bias between the two dyes used in the Infinium II assay after using the normalization method in Illuminas proprietary software (BeadStudio). We propose a quantile normalization strategy for correction of this dye bias. We tested the ...
TY - JOUR. T1 - Identification of cell surface marker candidates on SV-T2 cells using DNA microarray on DLC-coated glass. AU - Tuoya, AU - Hirayama, Koichi. AU - Nagaoka, Tadahiro. AU - Yu, Dongwei. AU - Fukuda, Takayuki. AU - Tada, Hiroko. AU - Yamada, Hidenori. AU - Seno, Masaharu. PY - 2005/8/19. Y1 - 2005/8/19. N2 - We analyzed gene expression profiles of normal mouse fibroblast BALB/c 3T3 cells and its SV40 transformant SV-T2 cells using our originally developed cell surface marker DNA microarray, which is prepared on a diamond-like carbon-coated glass. As a result, CD62L and IL-6 receptor α gene expressions were upregulated in SV-T2 and were thought to be candidates for cell surface markers of the cells. The result of microarray analysis was validated by real-time quantitative PCR, immunohistochemistry and biological assays. These data show that our cell surface marker DNA microarray should be useful in finding the candidates of cell type-specific surface markers.. AB - We analyzed gene ...
The well-defined differences in metastatic behavior and the clonal relationship of the K7M2 and K12 cells allowed the use of cDNA microarrays to define potentially important genetic determinants for pulmonary metastasis in this model. Recently, cDNA microarray technology has been used to list genes that are differentially expressed between high and low metastatic tumor systems (18, 19, 20) . Data generated in such cDNA microarray comparisons are of considerable value; however, it is difficult to determine how best to use this information. Both traditional reductionist and novel bio-informatic approaches (including hierarchical cluster analyses) have been used to manage microarray data. In the cDNA microarray comparisons presented herein, we identified 53 genes that were differentially expressed between the high (K7M2) and the low (K12) metastatic OSA primary tumors. To use this information, we used a reductionist approach that was based on biological differences demonstrated between the high and ...
and Shahdara. Gene expression markers (GEMs) are based on differences in transcript levels that exhibit bimodal distributions in segregating progeny, while single feature polymorphism (SFP) markers rely on differences in hybridization to individual oligonucleotide probes. Unlike SFPs, GEMs can be derived from any type of DNA-based expression microarray. Our method identifies SFPs independent of a genes expression level. Alleles for each GEM and SFP marker were ascertained with GeneChip data from parental accessions as well as RILs; a novel algorithm for allele determination using RIL distributions capitalized on the high level of genetic replication per locus. GEMs and SFP markers provided robust markers in 187 and 968 genes, respectively, which allowed estimation of gene order consistent with that predicted from the Col-0 genomic sequence. Using microarrays on a population to simultaneously measure gene expression variation and obtain genotypic data for a linkage map will facilitate expression ...
Replication of time series in microarray experiments is costly. To analyze time series data with no replicate, many model-specific approaches have been proposed. However, they fail to identify the genes whose expression patterns do not fit the pre-defined models. Besides, modeling the temporal expression patterns is difficult when the dynamics of gene expression in the experiment is poorly understood. We propose a method called Partial Energy ratio for Microarray (PEM) for the analysis of time course microarray data. In the PEM method, we assume the gene expressions vary smoothly in the temporal domain. This assumption is comparatively weak and hence the method is general enough to identify genes expressed in unexpected patterns. To identify the differentially expressed genes, a new statistic is developed by comparing the energies of two convoluted profiles. We further improve the statistic for microarray analysis by introducing the concept of partial energy. The PEM statistic can be easily ...
The accuracy of gene expression measurements generated using cDNA microarrays is dependent on the quality of the image generated following hybridization of fluorescently labelled cDNA. It is not known how this image is influenced by sample preparation factors which such as RNA quality, cDNA synthesis and labelling efficiency. In this study we used a simple metric based on the ratio of the total feature (F) and background (B) fluorescence, which correlates with the visual assessment of 60 microarray images, to determine the influence of sample preparation on image quality. Results indicate that RNA purity (A260/A280) and integrity (18S:28S ratio) do not strongly influence microarray image quality. cDNA having an nucleotide to dye ratio greater than 100 produced poor microarray images, however, cDNA labelled more efficiently was not a guarantee of a better image. The data also indicate that the array image quality is not improved by loading more cDNA into the hybridization mixture however poor image
The technique of chromatin immunoprecipitation (ChIP) is a powerful method for identifying in vivo DNA binding sites of transcription factors and for studying chromatin modifications. Unfortunately, the large number of cells needed for the standard ChIP protocol has hindered the analysis of many biologically interesting cell populations that are difficult to obtain in large numbers. New ChIP methods involving the use of carrier chromatin have been developed that allow the one-gene-at-a-time analysis of very small numbers of cells. However such methods are not useful if the resultant sample will be applied to genomic microarrays or used in ChIP-sequencing assays. Therefore, we have miniaturized the ChIP protocol such that as few as 10,000 cells (without the addition of carrier reagents) can be used to obtain enough sample material to analyze the entire human genome. We demonstrate the reproducibility of this MicroChIP technique using 2.1 million feature high-density oligonucleotide arrays and ...
Adjusting background for chip # 1 of 42 using MLE method. Adjusting background for chip # 2 of 42 using MLE method. Adjusting background for chip # 3 of 42 using MLE method. Adjusting background for chip # 4 of 42 using MLE method. Adjusting background for chip # 5 of 42 using MLE method. Adjusting background for chip # 6 of 42 using MLE method. Adjusting background for chip # 7 of 42 using MLE method. Adjusting background for chip # 8 of 42 using MLE method. Adjusting background for chip # 9 of 42 using MLE method. Adjusting background for chip # 10 of 42 using MLE method. Adjusting background for chip # 11 of 42 using MLE method. Adjusting background for chip # 12 of 42 using MLE method. Adjusting background for chip # 13 of 42 using MLE method. Adjusting background for chip # 14 of 42 using MLE method. Adjusting background for chip # 15 of 42 using MLE method. Adjusting background for chip # 16 of 42 using MLE method. Adjusting background for chip # 17 of 42 using MLE method. Adjusting ...
Researchers use DNA microarrays, or gene chips, to distinguish among different types of tissues based on the expression patterns of thousands of genes. A new study says the technology can be modified to profile more tissue samples simultaneously and with greater efficiency. The innovation is called an array of arrays. Gene expression profiles are typically obtained one at a time by hybridizing a single tissue sample to a single array on an individual glass slide. The integrated device described in the study is a glass wafer that includes 49 individual oligonucleotide arrays arranged as a 7 × 7 array of arrays. David J. Lockhart and colleagues at the Genomics Institute of the Novartis Research Foundation, in San Diego, California, developed a way to hybridize many tissue samples to multiple arrays on a single glass slide, or wafer. Using this and other modifications, they completed a study of gene expression in ovarian cancer in a single experiment. This was done in a fraction of the time and ...
Background Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists. Results Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded ...
Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, Arabidopsis thaliana Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonst …
1567 With microarray data we successfully developed a novel system for predicting early intrahepatic recurrence of hepatocellular carcinoma (HCC) [1]. The HCC microarray data consists of 60 HCC patients with high-density oligonucleotide microarrays representing approximately 6000 genes. In this data, the 20 patients had early intrahepatic recurrence within 1 year after curative surgery and 40 patients had nonrecurrence. We randomly divided 60 samples into 33 training samples and 27 blinded samples. With 33 training samples, we selected 12 genes useful for prediction. In general, visualization of high-dimensional space is important to evaluate interactively genes and to elucidate mechanism of recurrence. The Karhunen-Loeve (KL) expansion [2], which corresponds to PCA, and the Sammons nonlinear mapping (SNM) [3], which corresponds to MDS, are popular visualization methods. We call these methods unsupervised visualization where labeled samples are not used. In this paper we propose visualization ...
Browse decades of harmonized childhood cancer data and discover how this multi-species repository accelerates the search for cures.
TY - JOUR. T1 - Evaluating the performance of oligonucleotide microarrays for bacterial strains with increasing genetic divergence from the reference strain. AU - Oh, Seungdae. AU - Yoder-Himes, Deborah R.. AU - Tiedje, James. AU - Park, Joonhong. AU - Konstantinidis, Konstantinos T.. PY - 2010/5. Y1 - 2010/5. N2 - DNA oligonucleotide microarrays (oligoarrays) are being developed continuously; however, several issues regarding the applicability of these arrays for whole-genome DNA-DNA strain comparisons (genomotyping) have not been investigated. For example, the extent of false negatives (i.e., no hybridization signal is observed when the amino acid sequence is conserved but the nucleotide sequence has diverged to a level that does not allow hybridization) remains speculative. To provide quantitative answers to such questions, we performed competitive DNA-DNA oligoarray (60-mer) hybridizations with several fully sequenced (tester) strains and a reference strain (whose genome was used to design ...
Osteoarthritis (OA) is characterized by alterations to subchondral bone as well as articular cartilage. Changes to bone in OA have also been identified at sites distal to the affected joint, which include increased bone volume fraction and reduced bone mineralization. Altered bone remodelling has been proposed to underlie these bone changes in OA. To investigate the molecular basis for these changes, we performed microarray gene expression profiling of bone obtained at autopsy from individuals with no evidence of joint disease (control) and from individuals undergoing joint replacement surgery for either degenerative hip OA, or fractured neck of femur (osteoporosis [OP]). The OP sample set was included because an inverse association, with respect to bone density, has been observed between OA and the low bone density disease OP. Compugen human 19K-oligo microarray slides were used to compare the gene expression profiles of OA, control and OP bone samples. Four sets of samples were analyzed, comprising 10
Genome-wide transcriptional profiling has important applications in advancing knowledge of vocal fold biology. With the use of DNA microarray technology, analysis of global patterns of gene expression can reveal unexpected networks of coordinated regulation in the extracellular matrix of the lamina propria. Transcriptional gene expression patterns for 2 vocal fold pathologies-vocal fold polyp (VP; N= 1) and vocal fold granuloma (VG; N= 1) were analyzed by means of DNA microarray analysis for 4,632 human genes using another patients true vocal fold (TVF; N= 1) as a control. Twenty-four and 29 genes for VG and VP, respectively, were established to be either over- or underexpressed compared to that of TVF. Five-way cluster analysis revealed broad patterns that suggest a potential degree of organization underlying gene expression in these tissues. For the 1 VG, genes involved represent inflammation and wound healing; for the 1 VP, involved genes demonstrate a tempered wound repair response and ...
Abstract Background Conventional differential gene expression analysis by methods such as students t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups. Results Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than ...
Background: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. Methods: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. Results: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the ...
The increased use of microarray expression profiling to study both the molecular biology of cancer and the cellular physiology of difficult-to-isolate cell types has led to a growing need for methods that allow the use of limiting quantities of RNA. This limitation has prompted the development of amplification methods that produce the quantities of RNA required for microarray analysis. Efforts have become increasingly focused upon developing a protocol that minimizes amplification bias, provides versatility, and reduces technical complexity. We evaluated the new protocol Transplex™ Whole Transcriptome Amplification (WTA) produced by Rubicon Genomics. The kit was tested on Human Reference RNA (Stratagene) and on RNA extracted from a renal tumor cell line. Reproducibility, sensitivity and reliability in calling differentially expressed genes were evaluated by both Real-Time PCR and GeneChip® technology (Affymetrix). We tested reproducibility by comparing the expression profiles provided by U133 ...
Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified. Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n = 101) and/or poor quality control criteria (n = 10) (test set). With 10-marker classifiers, all training set samples as well as 97 of the 101 test
TY - JOUR. T1 - Overlay analysis of the oligonucleotide array gene expression profiles and copy number abnormalities as determined by array comparative genomic hybridization in medulloblastomas. AU - Lo, Ken C.. AU - Rossi, Michael R.. AU - Burkhardt, Tania. AU - Pomeroy, Scott L.. AU - Cowell, John K.. PY - 2007/1/1. Y1 - 2007/1/1. N2 - Combined analysis of gene expression array data and array-based comparative genomic hybridization data have been used in a series of 26 pediatric brain tumors to define up- and downregulated genes that coincide with losses, gains, and amplifications involving specific chromosome regions. Frequent losses were defined in chromosome arms 3q, 6q, 8p, 10q, 16q, 17p, and gains were identified in chromosome 7, and chromosome arms 9p and 17q. Amplification of a 2p region was seen in only one tumor, which corresponded to increased expression of the MYCN and DDX1 genes. To facilitate the analysis of the two data sets, we have developed a custom overlay tool that defines ...
Global gene expression analysis reveals pathway differences between teratogenic and non-teratogenic exposure concentrations of bisphenol A and 17β-estradiol in embryonic zebrafish.
TY - JOUR. T1 - Self-organizing latent lattice models for temporal gene expression profiling. AU - Zhang, Byoung Tak. AU - Yang, Jinsan. AU - Chi, Sung Wook. N1 - Funding Information: This work was supported by the Korean Government through BK21-IT, BrainTech, IMT2000 Bioinformatics and NRL Programs.. PY - 2003/7. Y1 - 2003/7. N2 - DNA microarrays are a high-throughput technology useful for functional genomics and gene expression analysis. While many microarray data are generated in sequence, most expression analysis tools are not utilizing the temporal information. Temporal expression profiling is important in many applications, including developmental studies, pathway analysis, and disease prognosis. In this paper, we develop a learning method designed for temporal gene expression profiling from massive DNA-microarray data. It attempts to learn probabilistic lattice maps of the gene expressions, which are then used for profiling the trajectories of temporal expressions of co-regulated genes. ...
Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles. We introduce a tool, TimeXNet, which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. TimeXNet uses a specialized form of the network flow optimization approach to identify the most probable paths connecting the genes with significant changes in expression at consecutive time intervals. TimeXNet has been extensively evaluated for its ability to predict novel regulators and their associated pathways within active gene sub-networks
Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z-score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z-score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when ...
1. Chockalingm A, Campbell NR, Fodor JG. Worldwide epidemic of hypertension. Can J Cardio. 2006;22:553-555 2. Tomson J, Lip GYH. Blood Pressure demographic: nature or nurture…… genes or environment?. BMC Med. 2005;3:3 3. WHO. World Health Report 2002: Reducing Risks, Promoting Healthy life. Geneva: World Health Organization. 2002 4. Heller RA. et al. Discovery and analysis of inflammatory disease related genes using cDNA microarrays. Proc Nat Acad Sci. 1997;94:2150- 2155 5. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 1996;14:1675-80 6. Tzouvelekis A, Patlakas G, Bouros D. Application of Microarray Technology in pulmonary diseases. Respir Res. 2004;5:26 7. King HC, Sinha AA. Gene expression profile analysis by DNA microarrays: promise and pitfalls. JAMA. 2001;286:2280-2288 8. LI JJ. Inflammation in hypertension: primary ...
Purpose: Retinopathy of prematurity (ROP) is a common blinding disease caused by the abnormal growth of blood vessels in the retina of premature babies with low birth weight and low gestation period. However, the mechanisms and factors contributing to the progression of ROP are still unknown. The present study aimed to identify gene(s) responsible for ROP progression by a global gene expression profiling.. Methods: From a cohort of 600 subjects comprising ROP babies (n=350) and controls (n=250), 15 ROP babies at any stage (gestational age [GA] ≤ 35 weeks and/or birth weight [BW] ≤ 1700 g) and premature babies with no ROP (n=6) (GA ≤ 35 weeks and/or BW ≤ 1700 g) and full term babies of the same age and no ROP (n=3), were screened. RNA was isolated from 0.5-1 ml of blood using RNeasy mini kit from Qiagen and the purity and integrity of RNA was checked with Bioanalyzer 2100 (Agilent). Global gene expression profiling was performed by using Illumina bead Chip array having ~47,000 ...
Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE) is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLEs KNN algorithm. In addition, kernel method based support vector machine (SVM) will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of
Affymetrix GeneChip® arrays are used widely to study transcriptional changes in response to developmental and environmental stimuli. GeneChip® arrays comprise multiple 25-mer oligonucleotide probes per gene and retain certain advantages over direct sequencing. For plants, there are several public GeneChip® arrays whose probes are localised primarily in 3′ exons. Plant whole-transcript (WT) GeneChip® arrays are not yet publicly available, although WT resolution is needed to study complex crop genomes such as Brassica, which are typified by segmental duplications containing paralogous genes and/or allopolyploidy. Available sequence data were sampled from the Brassica A and C genomes, and 142,997 gene models identified. The assembled gene models were then used to establish a comprehensive public WT exon array for transcriptomics studies. The Affymetrix GeneChip® Brassica Exon 1.0 ST Array is a 5 µM feature size array, containing 2.4 million 25-base oligonucleotide probes representing 135,201 gene
CG000170_TechNote_BiologicalandTechnicalVariationinSingleCell3GeneExpressionExperiments_RevA_.pdf. Technical Note - Biological & Technical Variation in Single Cell Gene Expression Experiments. The Chromium Single Cell 3′ v2 Reagent Kits protocol (Document CG00052) produces Single Cell 3′ short-read sequencer compatible libraries. Technical and biological variation may be present in the experiment design, and may impact data interpretation. Potential sources of technical variation include running a sample on two separate microfluidic chips or at different well positions on the same chip, and or technical variation introduced by sequencing libraries on separate Illumina flowcells or sequencing lanes. This Technical Note examines the potential sources of technical and biological variation and their effects on single cell gene expression. These factors need to be considered when designing an experiment to minimize bias and generate reliable single cell gene expression data.. FOR USE WITH. ...
Preface. Acknowledgments.. 1 Introduction.. 1.1 Basic Terminology.. 1.1.1 The Central Dogma of Molecular Biology.. 1.1.2 Genome.. 1.1.3 Proteome.. 1.1.4 DNA (Deoxyribonucleic Acid).. 1.1.5 RNA (Ribonucleic Acid).. 1.1.6 mRNA (messenger RNA).. 1.1.7 Genetic Code.. 1.1.8 Gene.. 1.1.9 Gene Expression and the Gene Expression Level.. 1.1.10 Protein.. 1.2 Overlapping Areas of Research.. 1.2.1 Genomics.. 1.2.2 Proteomics.. 1.2.3 Bioinformatics.. 1.2.4 Transcriptomics and Other -omics.. 1.2.5 Data Mining.. 2 Basic Analysis of Gene Expression Microarray Data.. 2.1 Introduction.. 2.2 Microarray Technology.. 2.2.1 Spotted Microarrays.. 2.2.2 Affymetrix GeneChip® Microarrays.. 2.2.3 Bead-Based Microarrays.. 2.3 Low-Level Preprocessing of Assymetrix Microarrays.. 2.3.1 MAS5.. 2.3.2 RMA.. 2.3.3 GCRMA.. 2.3.4 PLIER.. 2.4 Public Repositories of Microarray Data.. 2.4.1 Microarray Gene Expression Data Society (MGED) Standards.. 2.4.2 Public Databases.. 2.4.2.1 Gene Expression Omnibus (GEO).. 2.4.2.2 ...
Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method - Background: Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general. Results: A methodological study of affine models for gene expression data is carried out. Focus is on two-channel comparative studies, but the findings generalize also to single- and multi-channel data. The discussion applies to spotted as well as in-situ synthesized microarray data. Existing normalization methods such as curve-fit (lowess) normalization, parallel and perpendicular translation normalization, and quantile normalization, but also dye-swap normalization are
Initial physiological adjustments in response to drought stress lead to drastic changes in gene expression. The traditional approaches of assessing such drought-induced changes in gene expression involve measuring the differences in mRNA levels of one or few genes at a time. DNA expression microarray technology is a powerful tool that can monitor changes in expression of a large number of genes simultaneously. Expression microarrays also provide new insights into physiological and biochemical pathways of drought tolerance, and thus can lead to identification of novel candidate genes that can rapidly advance breeding for drought tolerance. This review describes the basic principles and potential applications of gene expression microarrays in understanding and improving drought tolerance in plants. A case study is presented involving hybridization of field-grown panicle samples from drought tolerant and susceptible rice germplasm targets with probes from a normalized panicle cDNA library. Results ...
IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 2, NO. 2, APRIL-JUNE 2005 1 The Latent Process Decomposition of cDNA Microarray Data Sets Simon Rogers, Mark Girolami, Colin Campbell, and Rainer Breitling Abstract-We present a new computational technique1 which enables the probabilistic analysis of cDNA microarray data and we demonstrate its effectiveness in identifying features of biomedical importance. A hierarchical Bayesian model, called Latent Process Decomposition (LPD), is introduced in which each sample in the data set is represented as a combinatorial mixture over a finite set of latent processes, which are expected to correspond to biological processes. Parameters in the model are estimated using efficient variational methods. This type of probabilistic model is most appropriate for the interpretation of measurement data generated by cDNA microarray technology. For determining informative substructure in such data sets, the proposed model has several important ...
Seker, H. (2004) A Multi-Fuzzy Filtering Approach to Reliable Gene Expression Profile Analysis. Proceedings of the 2004 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, October 2004, pp. 37-40 ...
TY - JOUR. T1 - Transcription network construction for large-scale microarray datasets using a high-performance computing approach. AU - Zhu, Mengxia Michelle. AU - Wu, Qishi. PY - 2008/3/4. Y1 - 2008/3/4. N2 - Background: The advance in high-throughput genomic technologies including microarrays has demonstrated the potential of generating a tremendous amount of gene expression data for the entire genome. Deciphering transcriptional networks that convey information on intracluster correlations and intercluster connections of genes is a crucial analysis task in the post-sequence era. Most of the existing analysis methods for genome-wide gene expression profiles consist of several steps that often require human involvement based on experiential knowledge that is generally difficult to acquire and formalize. Moreover, large-scale datasets typically incur prohibitively expensive computation overhead and thus result in a long experiment-analysis research cycle. Results: We propose a parallel ...
Differential gene expression patterns in developing sexually dimorphic rat brain regions exposed to antiandrogenic, estrogenic, or complex endocrine disruptor mixtures: Glutamatergic synapses as ...
Gene set analysis methods use prior biological knowledge to analyze gene expression data. This prior knowledge takes the form of predefined groups of genes, linked through their biological function. Gene set analysis methods have been successfully applied in transversal studies, their results being more sensitive and interpretable than those of methods investigating genomic data one gene at a time. The time-course gene set analysis (TcGSA) introduced here is an extension of such gene set analysis to longitudinal data. This method identifies a priori defined groups of genes whose expression is not stable over time, taking into account the potential heterogeneity between patients and between genes. When biological conditions are compared, it identifies the gene sets that have different expression dynamics according to these conditions. Data from 2 studies are analyzed: data from an HIV therapeutic vaccine trial, and data from a recent study on influenza and pneumococcal vaccines. In both cases, TcGSA
Tahira, A. C., Kubrusly, M. S., Faria, M. F., Verjovski-Almeida, S., Reis, E. M., & Machado, M. C. C. (2010). Gene expression profiling reveals long intronic non-coding RNAs differentially expressed in pancreatic cancer and metastasis. Pancreas. Philadelphia ...
Purpose This article provides a review of the transcriptomic expression profiling studies that have been performed on meningiomas so far. We discuss some future prospects and challenges ahead in the field of gene expression profiling. Methods We performed a systematic search in the PubMed and EMBASE databases in May 2010 using the following search terms alone or in combination: meningioma, microarray analysis, oligonucleotide array sequence analysis, or gene expression profiling. Only original research articles in English that had used RNA hybridized to high-resolution microarray chips to generate gene expression profiles were included. Results We identified 13 articles matching the inclusion criteria. All studies had been performed during the last decade. Conclusions The main results of the studies can be grouped in three categories: (1) several groups have identified meningioma-specific genes and genes associated with the three WHO grades, and the main histological subtypes of grade I ...
A meta-analysis was performed across six public microarray datasets for human small cell lung cancer (SCLC) comprising 365 samples across eight different platforms. Genes were ranked according to effect size and p-value for tumor versus control samples, and false discovery rates were calculated. The top scoring 48 genes that were significant by both methods, along with the 48 highest rated surface antigen genes, were used to populate a gene list for subsequent single cell evaluation. High throughput gene expression analysis was performed for 400 individual cells from one SCLC line (H446) using the Fluidigm microfluidic platform. Supervised machine learning was applied to identify transcriptionally-defined subgroups among these cells. The non-parametric Kolmogorov-Smirnov test was then used to determine those surface markers best able to distinguish each cluster. Individual cells from each group were then FACS-isolated, and clonogenicity was evaluated after 14 days in culture. Using these surface ...
APC (Adenomatous polyposis coli) plays an important role in the pathogenesis of both familial and sporadic colorectal cancer. Patients carrying germline APC mutations develop multiple colonic adenomas at younger age and higher frequency than non-carrier cases which indicates that silencing of one APC allele may be sufficient to initiate the transformation process. To elucidate the biological dysregulation underlying adenoma formation we examined global gene expression profiles of adenomas and corresponding normal mucosa from an FAP patient. Differential expression of the most significant gene identified in this study was further validated by mRNA in situ hybridization, reverse transcriptase PCR and Northern blotting in different sets of adenomas, tumours and cancer cell lines. Eighty four genes were differentially expressed between all adenomas and corresponding normal mucosa, while only seven genes showed differential expression within the adenomas. The first group included pregnancy specific β-1
TY - JOUR. T1 - Improved significance test for DNA microarray data. T2 - Temporal effects of shear stress on endothelial genes. AU - Zhao, Yihua. AU - Chen, Benjamin P C. AU - Miao, Hui. AU - Yuan, Suli. AU - Li, Yi Shuan. AU - Hu, Yingli. AU - Rocke, David M. AU - Chien, Shu. PY - 2003/4. Y1 - 2003/4. N2 - Statistical methods for identifying differentially expressed genes from microarray data are evolving. We developed a test for the statistical significance of differential expression as a function of time. When applied to microarray data obtained from endothelial cells exposed to shearing for different durations, the new multi-group test (G-test) identified three times as many genes as the one-way ANOVA at the same significance level. Using simulated data, we showed that this increase in sensitivity was achieved without sacrificing specificity. Several genes known to respond to shear stress by Northern blotting were identified by the G-test at P ≤ 0.01 (but not by ANOVA), with similar ...
The systematic comparison of transcriptional responses of organisms is a powerful tool in functional genomics. For example, mutants may be characterized by comparing their transcript profiles to those obtained in other experiments querying the effects on gene expression of many experimental factors including treatments, mutations and pathogen infections. Similarly, drugs may be discovered by the relationship between the transcript profiles effectuated or impacted by a candidate drug and by the target disease. The integration of such data enables systems biology to predict the interplay between experimental factors affecting a biological system. Unfortunately, direct comparisons of gene expression profiles obtained in independent, publicly available microarray experiments are typically compromised by substantial, experiment-specific biases. Here we suggest a novel yet conceptually simple approach for deriving Functional Association(s) by Response Overlap (FARO) between microarray gene expression
This project is an investigation of whether analysing subsets of time series gene expression data can give additional information about putatively co-regulated genes, compared to only using the whole time series. The original gene expression data set was partitioned into subsets and similarity was computed for both the whole timed series and subsets. Pearson correlation was used as similarity measure between gene expression profiles. The results indicate that analysing co-expression in subsets of gene expression data derives true-positive connections, with respect to co-regulation, that are not detected by only using the whole time series data. Unfortunately, with the actual data set, chosen similarity measure and partitioning of the data, randomly generated connections have the same amount of true-positives as the ones derived by the applied analysis. However, it is worth to continue further analysis of the subsets of gene expression data, which is based on the multi-factorial nature of gene ...
The effect of the CCR5-delta32 deletion on global gene expression considering immune response and inflammation : The natural function of the C-C chemokine receptor type 5 (CCR5) is poorly understood. A 32 base pair deletion in the CCR5 gene (CCR5-delta32) located on chromosome 3 results in a non-functional protein. It is supposed that this deletion causes an alteration in T-cell response to inflammation. For example, the presence of the CCR5-delta32 allele in recipients of allografts constitutes as an
TY - JOUR. T1 - Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma. T2 - A report from the International DLBCL Rituximab-CHOP Consortium Program Study. AU - Visco, C.. AU - Li, Y.. AU - Xu-Monette, Z. Y.. AU - Miranda, R. N.. AU - Green, T. M.. AU - Li, Y.. AU - Tzankov, A.. AU - Wen, W.. AU - Liu, W. M.. AU - Kahl, B. S.. AU - DAmore, E. S.G.. AU - Montes-Moreno, S.. AU - Dybkær, K.. AU - Chiu, A.. AU - Tam, W.. AU - Orazi, A.. AU - Zu, Y.. AU - Bhagat, G.. AU - Winter, J. N.. AU - Wang, H. Y.. AU - ONeill, S.. AU - Dunphy, C. H.. AU - Hsi, E. D.. AU - Zhao, X. F.. AU - Go, R. S.. AU - Choi, W. W.L.. AU - Zhou, F.. AU - Czader, M.. AU - Tong, J.. AU - Zhao, X.. AU - Van Krieken, J. H.. AU - Huang, Q.. AU - Ai, W.. AU - Etzell, J.. AU - Ponzoni, M.. AU - Ferreri, A. J.M.. AU - Piris, M. A.. AU - Møller, M. B.. AU - Bueso-Ramos, C. E.. AU - Medeiros, L. ...
Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: a report from the International DLBCL Rituximab-CHOP Consortium Program Study.
We use cookies to ensure that we give you the best experience on our website. If you click Continue well assume that you are happy to receive all cookies and you wont see this message again. Click Find out more for information on how to change your cookie settings ...
Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and small-n. Therefore, RF can be used to select and rank the genes for the diagnosis and effective treatment of cancer. Methods: The microarray gene expression data of colon, leukemia, and prostate cancers were collected from public databases. Primary preprocessing was done on them using limma package, and then, the RF classification method was implemented on datasets separately in R software. Finally, the selected genes in each of the cancers were evaluated and compared with those of previous experimental studies and their functionalities were assessed in molecular cancer processes. Result: The RF method extracted very small sets of genes while it retained its
Background Gene expression microarrays permit the quantification of transcript accumulation for most or all genes within a genome. increasing this process, we could actually identify eQTLs managing network replies for 18 out of 20 a priori-described gene networks within a recombinant inbred series population produced from accessions Bay-0 and Shahdara. Bottom line This approach gets the potential to become expanded to assist in direct exams of the ACP-196 manufacture partnership between phenotypic characteristic and transcript hereditary architecture. The usage of a priori explanations for network eQTL id has enormous prospect of providing path toward upcoming eQTL analyses. History Many phenotypic characteristics, ranging from disease susceptibility to development, are quantitative in nature and are analyzed in both animals and plants via quantitative trait locus (QTL) mapping [1-3]. QTLs are regions of the genome associated with phenotypic variance for a trait. These regions may or may not ...
Report on emerging technologies for translational bioinformatics: a symposium on gene expression profiling for archival tissues. . Biblioteca virtual para leer y descargar libros, documentos, trabajos y tesis universitarias en PDF. Material universiario, documentación y tareas realizadas por universitarios en nuestra biblioteca. Para descargar gratis y para leer online.
TY - JOUR. T1 - Critical Appraisal of DNA Microarrays in Psychiatric Genomics. AU - Mirnics, Károly. AU - Levitt, Pat. AU - Lewis, David A.. PY - 2006/7/15. Y1 - 2006/7/15. N2 - Transcriptome profiling using DNA microarrays are data-driven approaches with the potential to uncover unanticipated relationships between gene expression alterations and psychiatric disorders. Studies to date have yielded both convergent and divergent findings. Differences may be explained, at least in part, by the use of a variety of microarray platforms and analytical approaches. Consistent findings across studies suggest, however, that important relationships may exist between altered gene expression and genetic susceptibility to psychiatric disorders. For example, GAD67, RGS4, DTNBP1, NRG1, and GABRAB2 show expression alterations in the postmortem brain of subjects with schizophrenia, and these genes have been also implicated as putative, heritable schizophrenia susceptibility genes. Thus, we propose that for some ...
For more than a decade, global gene expression profiling has been extensively used to elucidate the biology of human papillomaviruses (HPV) and their role in cervical- and head-and-neck cancers. Since 2008, the expression profiling of miRNAs has been reported in multiple HPV studies. Two major strategies have been employed in the gene and miRNA profiling studies: In the first approach, HPV positive tumors were compared to normal tissues or to HPV negative tumors. The second strategy relied on analysis of cell cultures transfected with single HPV oncogenes or with HPV genomes compared to untransfected cells considered as models for the development of premalignant and malignant transformations.. In this review, we summarize what we have learned from a decade of global expression profiling studies. We performed comprehensive analysis of the overlap of the lists of differentially expressed genes and microRNAs, in both tissue samples and cell culture based studies. The review focuses mainly on HPV16, ...
Because microarray experiments literally involve the comparison of thousands of data points, the scientific community has grappled with identifying specific guidelines for the conductance, statistical analysis, and interpretation of microarray experiments due to the significant potential for false positives (i.e. , type I error). To this end, the Microarray Gene Expression Data Society, an international organization of molecular biologists, computer scientists, and data analysts, developed standards known as the Minimum Information About a Microarray Experiment (MIAME), which outlines the minimum information that should be reported about a microarray experiment to enable its unambiguous interpretation and reproduction.5 1In addition adhering to the MIAME guidelines, Lucchinetti et al. analyzed their microarray data using a highly sophisticated technique known as gene set enrichment analysis. Gene set enrichment analysis is a computational method that determines whether an a priori defined set of ...