Powerful specialized software is essential for managing, quantifying, and ultimately deriving scientific insight from results of a microarray experiment. We have developed a suite of software applications, known as TM4, to support such gene expression studies. The suite consists of open‐source tools for data managementandreporting,imageanalysis,normalizationandpipelinecontrol, and data mining and visualization. An integrated MIAME‐compliant MySQL database is included. This chapter describes each component of the suite and includes a sample analysis walk‐through.

https://www.ncbi.nlm.nih.gov/projects/Jax_GSA/IMGC2006_Workshop/Tm4_06.pdf

TM4 microarray software suite

Drosophila melanogaster is one of the most well studied genetic model organisms; nonetheless, its genome still contains unannotated coding and non-coding genes, transcripts, exons and RNA editing sites. Full discovery and annotation are pre-requisites for understanding how the regulation of transcription, splicing and RNA editing directs the development of this complex organism. Here we used RNA-Seq, tiling microarrays and cDNA sequencing to explore the transcriptome in 30 distinct developmental stages. We identified 111,195 new elements, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events, and inferred protein isoforms that previously eluded discovery using established experimental, prediction and conservation-based approaches. These data substantially expand the number of known transcribed elements in the Drosophila genome and provide a high-resolution view of transcriptome dynamics throughout development.

/pdf/the-developmental-transcriptome-of-drosophila-melanogaster-2ur7q1nmdy.pdf

The developmental transcriptome of Drosophila melanogaster

DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field.

/pdf/cluster-analysis-for-gene-expression-data-a-survey-n507v99tm6.pdf

Cluster analysis for gene expression data: a survey

Molecular and Functional Profiling of Memory CD8 T Cell Differentiation

Drosophila melanogaster is one of the well-studied metazoan organisms; nonetheless its genome still contains unannotated genes, exons and RNA editing sites. Furthermore, our understanding of how the regulation of transcription, splicing and RNA editing directs development of this complex organism remains limited. We have used RNA-seq, tiling microarrays and cDNA sequencing to explore the transcriptome in 30 distinct stages throughout development. We have identified 87,352 new features, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events. We have inferred protein isoforms that previously eluded discovery with established experimental, prediction and conservation-based approaches. Together, these data substantially expand the number of known transcribed elements in the Drosophila genome and provide a high-resolution view of transcriptome dynamics throughout the development of a metazoan.

/pdf/the-developmental-transcriptome-of-drosophila-melanogaster-1fh2onllx6.pdf

The cDNA microarray is one technological approach that has the potential to accurately measure changes in global mRNA expression levels. We report an assessment of an optimized cDNA microarray platform to generate accurate, precise and reliable data consistent with the objective of using microarrays as an acquisition platform to populate gene expression databases. The study design consisted of two independent evaluations with 70 arrays from two different manufactured lots and used three human tissue sources as samples: placenta, brain and heart. Overall signal response was linear over three orders of magnitude and the sensitivity for any element was estimated to be 2 pg mRNA. The calculated coefficient of variation for differential expression for all non-differentiated elements was 12–14% across the entire signal range and did not vary with array batch or tissue source. The minimum detectable fold change for differential expression was 1.4. Accuracy, in terms of bias (observed minus expected differential expression ratio), was less than 1 part in 10 000 for all non-differentiated elements. The results presented in this report demonstrate the reproducible performance of the cDNA microarray technology platform and the methods provide a useful framework for evaluating other technologies that monitor changes in global mRNA expression.

/pdf/an-evaluation-of-the-performance-of-cdna-microarrays-for-24snmwbahu.pdf

An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

Sexual dimorphism results in the formation of two types of individuals with specialized reproductive roles and is most evident in the germ cells and gonads. We have undertaken a global analysis of transcription between the sexes using a 31,464 element FlyGEM microarray to determine what fraction of the genome shows sex-biased expression, what tissues express these genes, the predicted functions of these genes, and where these genes map onto the genome. Females and males (both with and without gonads), dissected testis and ovary, females and males with genetically ablated germlines, and sex-transformed flies were sampled. Using any of a number of criteria, we find extensive sex-biased expression in adults. The majority of cases of sex differential gene expression are attributable to the germ cells. There is also a large class of genes with soma-biased expression. There is little germline-biased expression indicating that nearly all genes with germline expression also show sex-bias. Monte Carlo simulations show that some genes with sex-biased expression are non-randomly distributed in the genome.

/pdf/a-survey-of-ovary-testis-and-soma-biased-gene-expression-in-2cyorjl6to.pdf

A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogaster adults.

We have constructed a DNA microarray to monitor expression of predicted genes in Drosophila. By using homotypic hybridizations, we show that the array performs reproducibly, that dye effects are minimal, and that array results agree with systematic northern blotting. The array gene list has been extensively annotated and linked-out to other databases. Incyte and the NIH have made the platform available to the community via academic microarray facilities selected by an NIH committee.

/pdf/flygem-a-full-transcriptome-array-platform-for-the-3qs05jdj2p.pdf

FlyGEM, a full transcriptome array platform for the Drosophila community

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An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogaster adults.

FlyGEM, a full transcriptome array platform for the Drosophila community