Project description:Transcript profiling is crucial to study biological systems and various platforms have been implemented to survey mRNAs at the genome scale. We have assessed the characteristics of the CATMA microarray designed for Arabidopsis thaliana transcriptome analysis, and compared it with two commercial platforms from Agilent and Affymetrix. The CATMA array consists of gene-specific sequence tags of 150 to 500 base pairs, the Agilent (Arabidopsis 2) array of 60mer oligonucleotides, and the Affymetrix gene chip (ATH1) of 25mer oligonucleotide sets. We have matched each probe repertoire with the Arabidopsis genome annotation (TIGR release 5.0) and determined the correspondence between them. Array performance was analyzed by hybridization with labeled target derived from eight RNA samples made of shoot total RNA spiked with a calibrated series of 14 control transcripts. A total of fourteen cDNA clones were thus selected and used as templates to synthesize bona fide polyadenylated spike RNAs. Each spike RNA was calibrated then mixed in equal amount with one of the other spike RNAs to obtain seven pairs at equal concentration. These seven spike RNA pairs were then combined systematically to construct seven complex spike mixes in a design similar to an ordered Latin square, each mix containing six of the seven spike pairs in staggered concentrations covering five logs. To prevent loss of spike RNA through adsorption to the plastic ware, the spike mixes were prepared in 0.5 Âµg/Âµl Col RNA, resulting in a range of concentration from 0.1 to 10,000 copies per cellular equivalent (cpc), assuming that the total RNA contained 1% polyadenalated mRNA and that a cell contained on average 300,000 transcripts. These seven RNA samples included equal amounts of combined spike RNA . To convert the spike hybridization signals to ratios, an eighth sample was prepared, called the reference sample, consisting of the base Col RNA completed with all spike RNAs at a concentration of 100 cpc. The results from the eight experiments using the Affymetrix gene chips (ATH1) are available for analysis or download from this site. Experimenter name = Jim Beynon; Experimenter phone = 01798 470382; Experimenter fax = 01789 470552; Experimenter department = Horticulture Research International; Experimenter institute = Warwick University; Experimenter address = Horticulture Research International; Experimenter address = Wellesbourne; Experimenter address = Warwick; Experimenter zip/postal_code = CV34 6QJ; Experimenter country = UK Experiment Overall Design: 8 samples were used in this experiment

Project description:BackgroundExpression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples.ResultsWe describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that interarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log(2) units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators.ConclusionsThis study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells.

Project description:BackgroundThe main research tool for identifying microRNAs involved in specific cellular processes is gene expression profiling using microarray technology. Agilent is one of the major producers of microRNA arrays, and microarray data are commonly analyzed by using R and the functions and packages collected in the Bioconductor project. However, an analytical package that integrates the specific characteristics of microRNA Agilent arrays has been lacking.ResultsThis report presents the new bioinformatic tool AgiMicroRNA for the pre-processing and differential expression analysis of Agilent microRNA array data. The software is implemented in the open-source statistical scripting language R and is integrated in the Bioconductor project (http://www.bioconductor.org) under the GPL license. For the pre-processing of the microRNA signal, AgiMicroRNA incorporates the robust multiarray average algorithm, a method that produces a summary measure of the microRNA expression using a linear model that takes into account the probe affinity effect. To obtain a normalized microRNA signal useful for the statistical analysis, AgiMicroRna offers the possibility of employing either the processed signal estimated by the robust multiarray average algorithm or the processed signal produced by the Agilent image analysis software. The AgiMicroRNA package also incorporates different graphical utilities to assess the quality of the data. AgiMicroRna uses the linear model features implemented in the limma package to assess the differential expression between different experimental conditions and provides links to the miRBase for those microRNAs that have been declared as significant in the statistical analysis.ConclusionsAgiMicroRna is a rational collection of Bioconductor functions that have been wrapped into specific functions in order to ease and systematize the pre-processing and statistical analysis of Agilent microRNA data. The development of this package contributes to the Bioconductor project filling the gap in microRNA array data analysis.

Project description:Transcript profiling is crucial to study biological systems and various platforms have been implemented to survey mRNAs at the genome scale. We have assessed the characteristics of the CATMA microarray designed for Arabidopsis thaliana transcriptome analysis, and compared it with two commercial platforms from Agilent and Affymetrix. The CATMA array consists of gene-specific sequence tags of 150 to 500 base pairs, the Agilent (Arabidopsis 2) array of 60mer oligonucleotides, and the Affymetrix gene chip (ATH1) of 25mer oligonucleotide sets. We have matched each probe repertoire with the Arabidopsis genome annotation (TIGR release 5.0) and determined the correspondence between them. Array performance was analyzed by hybridization with labeled target derived from eight RNA samples made of shoot total RNA spiked with a calibrated series of 14 control transcripts. A total of fourteen cDNA clones were thus selected and used as templates to synthesize bona fide polyadenylated spike RNAs. Each spike RNA was calibrated then mixed in equal amount with one of the other spike RNAs to obtain seven pairs at equal concentration. These seven spike RNA pairs were then combined systematically to construct seven complex spike mixes in a design similar to an ordered Latin square, each mix containing six of the seven spike pairs in staggered concentrations covering five logs. To prevent loss of spike RNA through adsorption to the plastic ware, the spike mixes were prepared in 0.5 Âµg/Âµl Col RNA, resulting in a range of concentration from 0.1 to 10,000 copies per cellular equivalent (cpc), assuming that the total RNA contained 1% polyadenalated mRNA and that a cell contained on average 300,000 transcripts. These seven RNA samples included equal amounts of combined spike RNA . To convert the spike hybridization signals to ratios, an eighth sample was prepared, called the reference sample, consisting of the base Col RNA completed with all spike RNAs at a concentration of 100 cpc. The results from the eight experiments using the Affymetrix gene chips (ATH1) are available for analysis or download from this site.

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Transcription profiling of human B lymphocytes on agilent arrays

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