221,042 research outputs found
Real-time DNA microarray analysis
We present a quantification method for affinity-based
DNA microarrays which is based on the
real-time measurements of hybridization kinetics.
This method, i.e. real-time DNA microarrays,
enhances the detection dynamic range of conventional
systems by being impervious to probe
saturation in the capturing spots, washing
artifacts, microarray spot-to-spot variations, and
other signal amplitude-affecting non-idealities. We
demonstrate in both theory and practice that the
time-constant of target capturing in microarrays,
similar to all affinity-based biosensors, is inversely
proportional to the concentration of the target
analyte, which we subsequently use as the fundamental
parameter to estimate the concentration
of the analytes. Furthermore, to empirically
validate the capabilities of this method in practical
applications, we present a FRET-based assay which
enables the real-time detection in gene expression
DNA microarrays
Predictive analysis of microarray data
Microarray gene expression data are analyzed by means of a Bayesian
nonparametric model, with emphasis on prediction of future observables,
yielding a method for selection of differentially expressed genes and a
classifier
MAPPI-DAT : data management and analysis for protein-protein interaction data from the high-throughput MAPPIT cell microarray platform
Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments
Integration of microarray analysis into the clinical diagnosis of hematological malignancies: How much can we improve cytogenetic testing?
PurposeTo evaluate the clinical utility, diagnostic yield and rationale of integrating microarray analysis in the clinical diagnosis of hematological malignancies in comparison with classical chromosome karyotyping/fluorescence in situ hybridization (FISH).MethodsG-banded chromosome analysis, FISH and microarray studies using customized CGH and CGH+SNP designs were performed on 27 samples from patients with hematological malignancies. A comprehensive comparison of the results obtained by three methods was conducted to evaluate benefits and limitations of these techniques for clinical diagnosis.ResultsOverall, 89.7% of chromosomal abnormalities identified by karyotyping/FISH studies were also detectable by microarray. Among 183 acquired copy number alterations (CNAs) identified by microarray, 94 were additional findings revealed in 14 cases (52%), and at least 30% of CNAs were in genomic regions of diagnostic/prognostic significance. Approximately 30% of novel alterations detected by microarray were >20 Mb in size. Balanced abnormalities were not detected by microarray; however, of the 19 apparently "balanced" rearrangements, 55% (6/11) of recurrent and 13% (1/8) of non-recurrent translocations had alterations at the breakpoints discovered by microarray.ConclusionMicroarray technology enables accurate, cost-effective and time-efficient whole-genome analysis at a resolution significantly higher than that of conventional karyotyping and FISH. Array-CGH showed advantage in identification of cryptic imbalances and detection of clonal aberrations in population of non-dividing cancer cells and samples with poor chromosome morphology. The integration of microarray analysis into the cytogenetic diagnosis of hematologic malignancies has the potential to improve patient management by providing clinicians with additional disease specific and potentially clinically actionable genomic alterations
The application of phenotypic microarray analysis to anti-fungal drug development
Candida albicans metabolic activity in the presence and absence of acetylcholine was measured using phenotypic microarray analysis. Acetylcholine inhibited C. albicans biofilm formation by slowing metabolism independent of biofilm forming capabilities. Phenotypic microarray analysis can therefore be used for screening compound libraries for novel anti-fungal drugs and measuring antifungal resistance
Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments
A two-groups mixed-effects model for the comparison of (normalized)
microarray data from two treatment groups is considered. Most competing
parametric methods that have appeared in the literature are obtained as special
cases or by minor modification of the proposed model. Approximate maximum
likelihood fitting is accomplished via a fast and scalable algorithm, which we
call LEMMA (Laplace approximated EM Microarray Analysis). The posterior odds of
treatment gene interactions, derived from the model, involve shrinkage
estimates of both the interactions and of the gene specific error variances.
Genes are classified as being associated with treatment based on the posterior
odds and the local false discovery rate (f.d.r.) with a fixed cutoff. Our
model-based approach also allows one to declare the non-null status of a gene
by controlling the false discovery rate (FDR). It is shown in a detailed
simulation study that the approach outperforms well-known competitors. We also
apply the proposed methodology to two previously analyzed microarray examples.
Extensions of the proposed method to paired treatments and multiple treatments
are also discussed.Comment: Published in at http://dx.doi.org/10.1214/10-STS339 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Comparability of Microarray Data between Amplified and Non Amplified RNA in Colorectal Carcinoma
Microarray analysis reaches increasing popularity during the investigation of prognostic gene clusters in oncology. The standardisation of technical procedures will be essential to compare various datasets produced by different research groups. In several projects the amount of available tissue is limited. In such cases the preamplification of RNA might be necessary prior to microarray hybridisation. To evaluate the comparability of microarray results generated either by amplified or non amplified RNA we isolated RNA from colorectal cancer samples (stage UICC IV) following tumour tissue enrichment by macroscopic manual dissection (CMD). One part of the RNA was directly labelled and hybridised to GeneChips (HG-U133A, Affymetrix), the other part of the RNA was amplified according to the ?Eberwine? protocol and was then hybridised to the microarrays. During unsupervised hierarchical clustering the samples were divided in groups regarding the RNA pre-treatment and 5.726 differentially expressed genes were identified. Using independent microarray data of 31 amplified vs. 24 non amplified RNA samples from colon carcinomas (stage UICC III) in a set of 50 predictive genes we validated the amplification bias. In conclusion microarray data resulting from different pre-processing regarding RNA pre-amplification can not be compared within one analysis
Physics-based analysis of Affymetrix microarray data
We analyze publicly available data on Affymetrix microarrays spike-in
experiments on the human HGU133 chipset in which sequences are added in
solution at known concentrations. The spike-in set contains sequences of
bacterial, human and artificial origin. Our analysis is based on a recently
introduced molecular-based model [E. Carlon and T. Heim, Physica A 362, 433
(2006)] which takes into account both probe-target hybridization and
target-target partial hybridization in solution. The hybridization free
energies are obtained from the nearest-neighbor model with experimentally
determined parameters. The molecular-based model suggests a rescaling that
should result in a "collapse" of the data at different concentrations into a
single universal curve. We indeed find such a collapse, with the same
parameters as obtained before for the older HGU95 chip set. The quality of the
collapse varies according to the probe set considered. Artificial sequences,
chosen by Affymetrix to be as different as possible from any other human genome
sequence, generally show a much better collapse and thus a better agreement
with the model than all other sequences. This suggests that the observed
deviations from the predicted collapse are related to the choice of probes or
have a biological origin, rather than being a problem with the proposed model.Comment: 11 pages, 10 figure
Annotation-based meta-analysis of microarray experiments
We are developing software applications to perform meta-analysis of microarray experiments based on standardized experiment annotations aiming to identify similar experiments and cluster experiments. The applications were tested on files obtained from the ArrayExpress public repository. Annotation terms were used to compute experiment dissimilarities to find experiments related to a query experiment. These applications may motivate efforts of bench biologists to better annotate experiments
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