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Washing scaling of GeneChip microarray expression
Hans Binder, Knut Krohn, Conrad J Burden
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-291
Abstract: We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values.Washing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.Gene expression profiling using microarrays has become a standard technique for the large scale estimation of transcript abundance [1]. The method is based on the hybridization of RNA prepared from samples of interest with gene-specific oligonucleoti
Estimating RNA-quality using GeneChip microarrays
Mario Fasold, Hans Binder
BMC Genomics , 2012, DOI: 10.1186/1471-2164-13-186
Abstract: Microarray intensity data contains information to estimate the RNA quality of the sample. We here study the interplay of the characteristics of RNA surface hybridization with the effects of partly truncated transcripts on probe intensity. The 3′/5′ intensity gradient, the basis of microarray RNA quality measures, is shown to depend on the degree of competitive binding of specific and of non-specific targets to a particular probe, on the degree of saturation of the probes with bound transcripts and on the distance of the probe from the 3′-end of the transcript. Increasing degrees of non-specific hybridization or of saturation reduce the 3′/5′ intensity gradient and if not taken into account, this leads to biased results in common quality measures for GeneChip arrays such as affyslope or the control probe intensity ratio. We also found that short probe sets near the 3′-end of the transcripts are prone to non-specific hybridization presumable because of inaccurate positional assignment and the existence of transcript isoforms with variable 3′ UTRs. Poor RNA quality is associated with a decreased amount of RNA material hybridized on the array paralleled by a decreased total signal level. Additionally, it causes a gene-specific loss of signal due to the positional bias of transcript abundance which requires an individual, gene-specific correction. We propose a new RNA quality measure that considers the hybridization mode. Graphical characteristics are introduced allowing assessment of RNA quality of each single array (‘tongs plot’ and ‘degradation hook’). Furthermore, we suggest a method to correct for effects of RNA degradation on microarray intensities.The presented RNA degradation measure has best correlation with the independent RNA integrity measure RIN, and therefore presents itself as a valuable tool for quality control and even for the study of RNA degradation. When RNA degradation effects are detected in microarray experiments, a correction of the induced bias i
Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays
Chao Lu
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-103
Abstract: Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation.Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.The high-density oligonucleotide microarray, also known as GeneChip?, made by Affymetrix Inc (Santa Clara, CA), has been widely used in both academic institutions and industrial companies, and is considered as the "standard" of gene expression microarrays among several platforms. A single GeneChip? can hold more than 50,000 probe sets for every gene in human genome. A probe set is a collection of probe pairs that interrogates the same sequence, or set of sequences, and typically contains 11 probe pairs of 25-mer oligonucleotides [1-3]. Each pair contains the complementary sequence to the gene of interest, the so-called perfect match (PM), and a specificity control, called the Mismatch (MM) [3]. Gene expression level is obtained from the calculation of hybridization intensity to the probe pairs and is referred to as the "signal" [4-10]. The normalization method used in GeneChip software is called scaling and is defined as an adjustment of the average signal value of all arrays to a common value, the target sig
Establishing a major cause of discrepancy in the calibration of Affymetrix GeneChips
Andrew P Harrison, Caroline E Johnston, Christine A Orengo
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-195
Abstract: Our results indicate that the sets of genes identified as being most significantly differentially expressed, as estimated by the z-score of fold change, is relatively insensitive to the choice of background subtraction and normalisation. However, the contents of the gene list are most sensitive to the choice of expression measure. This is irrespective of whether the experiment uses a rat, mouse or human chip and whether the chip definition is made using probe mappings from Unigene, RefSeq, Entrez Gene or the original Affymetrix definitions. It is also irrespective of whether both Present and Absent, or just Present, Calls from the MAS5 algorithm are used to filter genelists, and this conclusion holds for genes of differing intensities. We also reach the same conclusion after assigning genes to be differentially expressed using t-statistics, although this approach results in a large amount of false positives in the sets of genes identified due to the small numbers of replicates typically used in microarray experiments.The major calibration uncertainty that biologists need to consider when analysing Affymetrix data is how their multiple probe values are condensed into one expression measure.Microarrays provide the opportunity to study the transcriptional output of a genome. The most common application of microarrays at present is to perform comparative studies, looking for relative changes between two conditions. The aim of calibration is to minimise as much systematic and experimental variation in the data, whilst retaining the biological variation. The calibration of microarrays, combined with statistics of the changes, underpins what can be inferred from these studies.In this paper we focus on the calibration of oligonucleotide GeneChip arrays produced by Affymetrix. The calibration process for GeneChips can be considered to have three parts: background correction; normalisation; expression measure. Background correction is needed to remove the proportion of intens
Power-law Signatures and Patchiness in Genechip Oligonucleotide Microarrays  [PDF]
Radhakrishnan Nagarajan
Quantitative Biology , 2007,
Abstract: . Genechip oligonucleotide microarrays have been used widely for transcriptional profiling of a large number of genes in a given paradigm. Gene expression estimation precedes biological inference and is given as a complex combination of atomic entities on the array called probes. These probe intensities are further classified into perfect-match (PM) and mis-match (MM) probes. While former is a measure of specific binding, the lat-ter is a measure of non-specific binding. The behavior of the MM probes has especially proven to be elusive. The present study investigates qualita-tive similarities in the distributional signatures and local correlation struc-tures/patchiness between the PM and MM probe intensities. These qualita-tive similarities are established on publicly available microarrays generated across laboratories investigating the same paradigm. Persistence of these similarities across raw as well as background subtracted probe intensities is also investigated. The results presented raise fundamental concerns in inter-preting Genechip oligonucleotide microarray data.
Comparing gene discovery from Affymetrix GeneChip microarrays and Clontech PCR-select cDNA subtraction: a case study
Wuxiong Cao, Charles Epstein, Hong Liu, Craig DeLoughery, Nanxiang Ge, Jieyi Lin, Rong Diao, Hui Cao, Fan Long, Xin Zhang, Yangde Chen, Paul S Wright, Steve Busch, Michelle Wenck, Karen Wong, Alan G Saltzman, Zhihua Tang, Li Liu, Asher Zilberstein
BMC Genomics , 2004, DOI: 10.1186/1471-2164-5-26
Abstract: The same RNA samples isolated from peripheral blood monocyte precursors and immature DC (iDC) were used for GeneChip microarray probing and SSH cDNA library construction. 10,000 clones from each of the two-way SSH libraries (iDC-monocytes and monocytes-iDC) were picked for sequencing. About 2000 transcripts were identified for each library from 8000 successful sequences. Only 70% to 75% of these transcripts were represented on the U95 series GeneChip microarrays, implying that 25% to 30% of these transcripts might not have been identified in a study based only on GeneChip microarrays. In addition, about 10% of these transcripts appeared to be "novel", although these have not yet been closely examined. Among the transcripts that are also represented on the chips, about a third were concordantly discovered as differentially regulated between iDC and monocytes by GeneChip microarray transcript profiling. The remaining two thirds were either not inferred as differentially regulated from GeneChip microarray data, or were called differentially regulated but in the opposite direction. This underscores the importance both of generating reciprocal pairs of SSH libraries, and of real-time RT-PCR confirmation of the results.This study suggests that SSH could be used as an alternative and complementary transcript profiling tool to GeneChip microarrays, especially in identifying novel genes and transcripts of low abundance.Gene expression profiling has become an invaluable tool in functional genomics. Since the mid-1990's, DNA microarrays [1-3], cDNA subtraction [4-7] and Serial Analysis of Gene Expression (SAGE) [8] have emerged as the leading transcript profiling technologies in the global analysis of biological systems. One of the high throughput technologies, high-density oligonucleotide GeneChip? microarrays, manufactured by Affymetrix [1,3,9], makes it possible to simultaneously measure the relative abundance of thousands of mRNAs in a cell. However, DNA microarray technol
Complete gene expression profiling of Saccharopolyspora erythraea using GeneChip DNA microarrays
Clelia Peano, Silvio Bicciato, Giorgio Corti, Francesco Ferrari, Ermanno Rizzi, Raoul JP Bonnal, Roberta Bordoni, Alberto Albertini, Luigi Bernardi, Stefano Donadio, Gianluca De Bellis
Microbial Cell Factories , 2007, DOI: 10.1186/1475-2859-6-37
Abstract: The transcriptional analysis identified a set of 404 genes, whose transcriptional signals vary during growth and characterize three distinct phases: a rapid growth until 32 h (Phase A); a growth slowdown until 52 h (Phase B); and another rapid growth phase from 56 h to 72 h (Phase C) before the cells enter the stationary phase. A non-parametric statistical method, that identifies chromosomal regions with transcriptional imbalances, determined regional organization of transcription along the chromosome, highlighting differences between core and non-core regions, and strand specific patterns of expression. Microarray data were used to characterize the temporal behaviour of major functional classes and of all the gene clusters for secondary metabolism. The results confirmed that the ery cluster is up-regulated during Phase A and identified six additional clusters (for terpenes and non-ribosomal peptides) that are clearly regulated in later phases.The use of a S. erythraea DNA microarray improved specificity and sensitivity of gene expression analysis, allowing a global and at the same time detailed picture of how S. erythraea genes are modulated. This work underlines the importance of using DNA microarrays, coupled with an exhaustive statistical and bioinformatic analysis of the results, to understand the transcriptional organization of the chromosomes of micro-organisms producing natural products.Soil-inhabiting Actinomycetes are prominent antibiotic producers. Berdy [1] estimated that 8700 antibiotics have been discovered from them, compared with 2900 from all other bacteria and 4900 from fungi. Even including secondary metabolites with biologic activities other than anti-microbial, Actinomycetes still stand out as excellent producers, with Streptomyces being the most prolific genus. Actinomycetes are an abundant and diverse group, encompassing several different genera belonging to diversified families within the order Actinomycetales [2]. The large number of seconda
G-stack modulated probe intensities on expression arrays - sequence corrections and signal calibration
Mario Fasold, Peter F Stadler, Hans Binder
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-207
Abstract: Longer runs of three or more consecutive G along the probe sequence and in particular triple degenerated G at its solution end ((GGG)1-effect) are associated with exceptionally large probe intensities on GeneChip expression arrays. This intensity bias is related to non-specific hybridization and affects both perfect match and mismatch probes. The (GGG)1-effect tends to increase gradually for microarrays of later GeneChip generations. It was found for DNA/RNA as well as for DNA/DNA probe/target-hybridization chemistries. Amplification of sample RNA using T7-primers is associated with strong positive amplitudes of the G-bias whereas alternative amplification protocols using random primers give rise to much smaller and partly even negative amplitudes.We applied positional dependent sensitivity models to analyze the specifics of probe intensities in the context of all possible short sequence motifs of one to four adjacent nucleotides along the 25meric probe sequence. Most of the longer motifs are adequately described using a nearest-neighbor (NN) model. In contrast, runs of degenerated guanines require explicit consideration of next nearest neighbors (GGG terms). Preprocessing methods such as vsn, RMA, dChip, MAS5 and gcRMA only insufficiently remove the G-bias from data.Positional and motif dependent sensitivity models accounts for sequence effects of oligonucleotide probe intensities. We propose a positional dependent NN+GGG hybrid model to correct the intensity bias associated with probes containing poly-G motifs. It is implemented as a single-chip based calibration algorithm for GeneChips which can be applied in a pre-correction step prior to standard preprocessing.Fig. 1a shows the surface image of a hybridized Affymetrix GeneChip expression array. Its area of about 1.6 cm2 divides into a grid of nearly one million probe spots of size (11 × 11) μm2. Each of them is covered by a 'turf' of 25meric oligonucleotides attached to the chip surface. Their sequence is chose
Direct calibration of PICKY-designed microarrays
Hui-Hsien Chou, Arunee Trisiriroj, Sunyoung Park, Yue-Ie C Hsing, Pamela C Ronald, Patrick S Schnable
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-347
Abstract: Using synthesized samples with known concentrations of specific oligonucleotides, a series of microarray experiments was conducted to evaluate microarrays designed by PICKY, an oligo microarray design software tool, and to test a direct microarray calibration method based on the PICKY-predicted, thermodynamically closest nontarget information. The complete set of microarray experiment results is archived in the GEO database with series accession number GSE14717. Additional data files and Perl programs described in this paper can be obtained from the website http://www.complex.iastate.edu webcite under the PICKY Download area.PICKY-designed microarray probes are highly reliable over a wide range of hybridization temperatures and sample concentrations. The microarray calibration method reported here allows researchers to experimentally optimize their hybridization conditions. Because this method is straightforward, uses existing microarrays and relatively inexpensive synthesized samples, it can be used by any lab that uses microarrays designed by PICKY. In addition, other microarrays can be reanalyzed by PICKY to obtain the thermodynamically closest nontarget information for calibration.PICKY is an optimal oligo microarray design software developed for large and complex genomes [1]. PICKY-estimated DNA annealing temperatures for probes can deviate from actual annealing temperatures because some potentially important parameters are unavailable to its design algorithms, such as variations in the salt composition of hybridization buffers, effects of partially immobilized probes on the microarray surface, nonlinear and multistage nontarget annealing with a probe, effects of incorporated dye molecules on transcript annealing efficiency with a probe, and effects of additional chemicals (e.g., SDS, formamide or DMSO) in the hybridization buffers. These parameters vary with lab environments, and their influence on hybridization kinetics can only be experimentally measured. Be
An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays
Erdem Arslan, Ian J Laurenzi
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-411
Abstract: In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at http://www.laurenzi.net webcite.Presently, there are several high throughput methods of quantifying changes in gene expression including oligonucleotide microarrays, quantitative realtime PCR (qPCR) and "next generation sequencing" (e.g. [1]). Of these, high density oligonucleotide microarrays are arguably the most important tools for genomic investigation. Although next generation sequencing is a promising alternative to microarrays for genome-scale expression profiling, and exhibit more sensitivity in the low-expression limit [2,3], microarray technology is substantially less expensive and the resulting data sets require much less information processing. Moreover, microarrays have substantially higher throughput than qPCR.Microarrays consist of DNA probes (reporters) which are orderly arranged on a glass slide. Probes may be attached to (or synthesized from) the slide surface via (1) mask-dependent [4-9] or maskless photolithographic DNA synthesis technology [10,11], or (2) robotic printing of PCR products or synthetic oligomers [12]. The first two of these methods yield oligonucleotide arrays (e.g. Affymetrix GeneChips), which are more reliable than PCR product-based arrays; they comprise the majority of commercial arrays by market share. Thus, we restrict our
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