A fully automatic gridding method for cDNA microarray images

<p>Abstract</p> <p>Background</p> <p>Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation, quantification, normalization and clustering.</p> <p>Results</p> <p>We propose a parameterless and fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach, first, detects and corrects rotations in the images by applying an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm used to find the positions of the sub-grids in the image and the positions of the spots in each sub-grid. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the image, and the correct number of spots in each sub-grid. Moreover, a refinement procedure is used to correct possible misalignments and increase the accuracy of the method.</p> <p>Conclusions</p> <p>Extensive experiments on real-life microarray images and a comparison to other methods show that the proposed method performs these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approach can be used in various type of microarray images with different resolutions and spot sizes and does not need any parameter to be adjusted.</p

Fully automatic classification of breast cancer microarray images

AbstractA microarray image is used as an accurate method for diagnosis of cancerous diseases. The aim of this research is to provide an approach for detection of breast cancer type. First, raw data is extracted from microarray images. Determining the exact location of each gene is carried out using image processing techniques. Then, by the sum of the pixels associated with each gene, the amount of “genes expression” is extracted as raw data. To identify more effective genes, information gain method on the set of raw data is used. Finally, the type of cancer can be recognized via analyzing the obtained data using a decision tree. The proposed approach has an accuracy of 95.23% in diagnosing the breast cancer types

"Harshlighting" small blemishes on microarrays

BACKGROUND: Microscopists are familiar with many blemishes that fluorescence images can have due to dust and debris, glass flaws, uneven distribution of fluids or surface coatings, etc. Microarray scans show similar artefacts, which affect the analysis, particularly when one tries to detect subtle changes. However, most blemishes are hard to find by the unaided eye, particularly in high-density oligonucleotide arrays (HDONAs). RESULTS: We present a method that harnesses the statistical power provided by having several HDONAs available, which are obtained under similar conditions except for the experimental factor. This method "harshlights" blemishes and renders them evident. We find empirically that about 25% of our chips are blemished, and we analyze the impact of masking them on screening for differentially expressed genes. CONCLUSION: Experiments attempting to assess subtle expression changes should be carefully screened for blemishes on the chips. The proposed method provides investigators with a novel robust approach to improve the sensitivity of microarray analyses. By utilizing topological information to identify and mask blemishes prior to model based analyses, the method prevents artefacts from confounding the process of background correction, normalization, and summarization

Detection of transcriptional difference of porcine imprinted genes using different microarray platforms

BACKGROUND: Presently, multiple options exist for conducting gene expression profiling studies in swine. In order to determine the performance of some of the existing microarrays, Affymetrix Porcine, Affymetrix Human U133+2.0, and the U.S. Pig Genome Coordination Program spotted glass oligonucleotide microarrays were compared for their reproducibility, coverage, platform independent and dependent sensitivity using fibroblast cell lines derived from control and parthenogenic porcine embryos. RESULTS: Array group correlations between technical replicates demonstrated comparable reproducibility in both Affymetrix arrays. Glass oligonucleotide arrays showed greater variability and, in addition, approximately 10% of probes had to be discarded due to slide printing defects. Probe level analysis of Affymetrix Human arrays revealed significant variability within probe sets due to the effects of cross-species hybridization. Affymetrix Porcine arrays identified the greatest number of differentially expressed genes amongst probes common to all arrays, a measure of platform sensitivity. Affymetrix Porcine arrays also identified the greatest number of differentially expressed known imprinted genes using all probes on each array, an ad hoc measure of realistic performance for this particular experiment. CONCLUSION: We conclude that of the platforms currently available and tested, the Affymetrix Porcine array is the most sensitive and reproducible microarray for swine genomic studies

J Fluorescence

The scope of this paper is to illustrate the need for an improved quality assurance in fluorometry. For this purpose, instrumental sources of error and their influences on the reliability and comparability of fluorescence data are highlighted for frequently used photoluminescence techniques ranging from conventional macro- and microfluorometry over fluorescence microscopy and flow cytometry to microarray technology as well as in vivo fluorescence imaging. Particularly, the need for and requirements on fluorescence standards for the characterization and performance validation of fluorescence instruments, to enhance the comparability of fluorescence data, and to enable quantitative fluorescence analysis are discussed. Special emphasis is dedicated to spectral fluorescence standards and fluorescence intensity standards

Quality assessment of microarrays: Visualization of spatial artifacts and quantitation of regional biases

BACKGROUND: Quality-control is an important issue in the analysis of gene expression microarrays. One type of problem is regional bias, in which one region of a chip shows artifactually high or low intensities (or ratios in a two-channel array) relative to the majority of the chip. Current practice in quality assessment for microarrays does not address regional biases. RESULTS: We present methods implemented in R for visualizing regional biases and other spatial artifacts on spotted microarrays and Affymetrix chips. We also propose a statistical index to quantify regional bias and investigate its typical distribution on spotted and Affymetrix arrays. We demonstrate that notable regional biases occur on both Affymetrix and spotted arrays and that they can make a significant difference in the case of spotted microarray results. Although strong biases are also seen at the level of individual probes on Affymetrix chips, the gene expression measures are less affected, especially when the RMA method is used to summarize intensities for the probe sets. A web application program for visualization and quantitation of regional bias is provided at . CONCLUSION: Researchers should visualize and measure the regional biases and should estimate their impact on gene expression measurements obtained. Here, we (i) introduce pictorial visualizations of the spatial biases; (ii) present for Affymetrix chips a useful resolution of the biases into two components, one related to background, the other to intensity scale factor; (iii) introduce a single parameter to reflect the global bias present across an array. We also examine the pattern distribution of such biases and conclude that algorithms based on smoothing are unlikely to compensate adequately for them

Transcript Specificity in Yeast Pre-mRNA Splicing Revealed by Mutations in Core Spliceosomal Components

Appropriate expression of most eukaryotic genes requires the removal of introns from their pre–messenger RNAs (pre-mRNAs), a process catalyzed by the spliceosome. In higher eukaryotes a large family of auxiliary factors known as SR proteins can improve the splicing efficiency of transcripts containing suboptimal splice sites by interacting with distinct sequences present in those pre-mRNAs. The yeast Saccharomyces cerevisiae lacks functional equivalents of most of these factors; thus, it has been unclear whether the spliceosome could effectively distinguish among transcripts. To address this question, we have used a microarray-based approach to examine the effects of mutations in 18 highly conserved core components of the spliceosomal machinery. The kinetic profiles reveal clear differences in the splicing defects of particular pre-mRNA substrates. Most notably, the behaviors of ribosomal protein gene transcripts are generally distinct from other intron-containing transcripts in response to several spliceosomal mutations. However, dramatically different behaviors can be seen for some pairs of transcripts encoding ribosomal protein gene paralogs, suggesting that the spliceosome can readily distinguish between otherwise highly similar pre-mRNAs. The ability of the spliceosome to distinguish among its different substrates may therefore offer an important opportunity for yeast to regulate gene expression in a transcript-dependent fashion. Given the high level of conservation of core spliceosomal components across eukaryotes, we expect that these results will significantly impact our understanding of how regulated splicing is controlled in higher eukaryotes as well

Valproic Acid Teratogenicity: A Toxicogenomics Approach

Embryonic development is a highly coordinated set of processes that depend on hierarchies of signaling and gene regulatory networks, and the disruption of such networks may underlie many cases of chemically induced birth defects. The antiepileptic drug valproic acid (VPA) is a potent inducer of neural tube defects (NTDs) in human and mouse embryos. As with many other developmental toxicants however, the mechanism of VPA teratogenicity is unknown. Using microarray analysis, we compared the global gene expression responses to VPA in mouse embryos during the critical stages of teratogen action in vivo with those in cultured P19 embryocarcinoma cells in vitro. Among the identified VPA-responsive genes, some have been associated previously with NTDs or VPA effects [vinculin, metallothioneins 1 and 2 (Mt1, Mt2), keratin 1-18 (Krt1-18)], whereas others provide novel putative VPA targets, some of which are associated with processes relevant to neural tube formation and closure [transgelin 2 (Tagln2), thyroid hormone receptor interacting protein 6, galectin-1 (Lgals1), inhibitor of DNA binding 1 (Idb1), fatty acid synthase (Fasn), annexins A5 and A11 (Anxa5, Anxa11)], or with VPA effects or known molecular actions of VPA (Lgals1, Mt1, Mt2, Id1, Fasn, Anxa5, Anxa11, Krt1-18). A subset of genes with a transcriptional response to VPA that is similar in embryos and the cell model can be evaluated as potential biomarkers for VPA-induced teratogenicity that could be exploited directly in P19 cell–based in vitro assays. As several of the identified genes may be activated or repressed through a pathway of histone deacetylase (HDAC) inhibition and specificity protein 1 activation, our data support a role of HDAC as an important molecular target of VPA action in vivo

A mouse embryonic stem cell bank for inducible overexpression of human chromosome 21 genes

BACKGROUND: Dosage imbalance is responsible for several genetic diseases, among which Down syndrome is caused by the trisomy of human chromosome 21. RESULTS: To elucidate the extent to which the dosage imbalance of specific human chromosome 21 genes perturb distinct molecular pathways, we developed the first mouse embryonic stem (ES) cell bank of human chromosome 21 genes. The human chromosome 21-mouse ES cell bank includes, in triplicate clones, 32 human chromosome 21 genes, which can be overexpressed in an inducible manner. Each clone was transcriptionally profiled in inducing versus non-inducing conditions. Analysis of the transcriptional response yielded results that were consistent with the perturbed gene's known function. Comparison between mouse ES cells containing the whole human chromosome 21 (trisomic mouse ES cells) and mouse ES cells overexpressing single human chromosome 21 genes allowed us to evaluate the contribution of single genes to the trisomic mouse ES cell transcriptome. In addition, for the clones overexpressing the Runx1 gene, we compared the transcriptome changes with the corresponding protein changes by mass spectroscopy analysis. CONCLUSIONS: We determined that only a subset of genes produces a strong transcriptional response when overexpressed in mouse ES cells and that this effect can be predicted taking into account the basal gene expression level and the protein secondary structure. We showed that the human chromosome 21-mouse ES cell bank is an important resource, which may be instrumental towards a better understanding of Down syndrome and other human aneuploidy disorders