共查询到20条相似文献,搜索用时 15 毫秒
1.
Gabriela G Loots Patrick SG Chain Shalini Mabery Amy Rasley Emilio Garcia Ivan Ovcharenko 《BMC bioinformatics》2006,7(1):307-8
Background
There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility. 相似文献2.
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Background
Studies of differential expression that use Affymetrix GeneChip arrays are often carried out with a limited number of replicates. Reasons for this include financial considerations and limits on the available amount of RNA for sample preparation. In addition, failed hybridizations are not uncommon leading to a further reduction in the number of replicates available for analysis. Most existing methods for studying differential expression rely on the availability of replicates and the demand for alternative methods that require few or no replicates is high. 相似文献5.
Roel GW Verhaak Frank JT Staal Peter JM Valk Bob Lowenberg Marcel JT Reinders Dick de Ridder 《BMC bioinformatics》2006,7(1):105-15
Background
Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers. 相似文献6.
Richard D Pearson 《BMC bioinformatics》2008,9(1):164
Background
The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less use has been made of this data set to evaluate differential expression methods. It has been suggested that this data set should not be used for method comparison due to a number of inherent flaws. 相似文献7.
Li-Xuan Qin Richard P Beyer Francesca N Hudson Nancy J Linford Daryl E Morris Kathleen F Kerr 《BMC bioinformatics》2006,7(1):23-12
Background
There are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay. 相似文献8.
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Timothy J Robinson Michaela A Dinan Mark Dewhirst Mariano A Garcia-Blanco James L Pearson 《BMC bioinformatics》2010,11(1):108
Background
Over the past two decades more than fifty thousand unique clinical and biological samples have been assayed using the Affymetrix HG-U133 and HG-U95 GeneChip microarray platforms. This substantial repository has been used extensively to characterize changes in gene expression between biological samples, but has not been previously mined en masse for changes in mRNA processing. We explored the possibility of using HG-U133 microarray data to identify changes in alternative mRNA processing in several available archival datasets. 相似文献10.
Dmitriy Skvortsov Diana Abdueva Michael E Stitzer Steven E Finkel Simon Tavaré 《BMC bioinformatics》2007,8(1):203
Background
The sequencing of many genomes and tiling arrays consisting of millions of DNA segments spanning entire genomes have made high-resolution copy number analysis possible. Microarray-based comparative genomic hybridization (array CGH) has enabled the high-resolution detection of DNA copy number aberrations. While many of the methods and algorithms developed for the analysis microarrays have focused on expression analysis, the same technology can be used to detect genetic alterations, using for example standard commercial Affymetrix arrays. Due to the nature of the resultant data, standard techniques for processing GeneChip expression experiments are inapplicable. 相似文献11.
Background
We provide a systematic study of the sources of variability in expression profiling data using 56 RNAs isolated from human muscle biopsies (34 Affymetrix MuscleChip arrays), and 36 murine cell culture and tissue RNAs (42 Affymetrix U74Av2 arrays). 相似文献12.
Rudi Alberts Peter Terpstra Menno Hardonk Leonid V Bystrykh Gerald de Haan Rainer Breitling Jan-Peter Nap Ritsert C Jansen 《BMC bioinformatics》2007,8(1):132
Background
The Affymetrix GeneChip technology uses multiple probes per gene to measure its expression level. Individual probe signals can vary widely, which hampers proper interpretation. This variation can be caused by probes that do not properly match their target gene or that match multiple genes. To determine the accuracy of Affymetrix arrays, we developed an extensive verification protocol, for mouse arrays incorporating the NCBI RefSeq, NCBI UniGene Unique, NIA Mouse Gene Index, and UCSC mouse genome databases. 相似文献13.
Background
Affymetrix GeneChips™ are an important tool in many facets of biological research. Recently, notable design changes to the chips have been made. In this study, we use publicly available data from Affymetrix to gauge the performance of three human gene expression arrays: Human Genome U133 Plus 2.0 (U133), Human Exon 1.0 ST (HuEx) and Human Gene 1.0 ST (HuGene). 相似文献14.
Background
The original spotted array technology with competitive hybridization of two experimental samples and measuring relative expression levels is increasingly displaced by more accurate platforms that allow determining absolute expression values for a single sample (for example, Affymetrix GeneChip and Illumina BeadChip). Unfortunately, cross-platform comparisons show a disappointingly low concordance between lists of regulated genes between the latter two platforms. 相似文献15.
Background
The ability of rickettsiae to survive in multiple eukaryotic host environments provides a good model for studying pathogen-host molecular interactions. Rickettsia typhi, the etiologic agent of murine typhus, is a strictly intracellular gram negative α-proteobacterium, which is transmitted to humans by its arthropod vector, the oriental rat flea, Xenopsylla cheopis. Thus, R. typhi must cycle between mammalian and flea hosts, two drastically different environments. We hypothesize that temperature plays a role in regulating host-specific gene expression, allowing R. typhi to survive in mammalian and arthropod hosts. In this study, we used Affymetrix microarrays to screen for temperature-induced genes upon a temperature shift from 37°C to 25°C, mimicking the two different host temperatures in vitro. 相似文献16.
Richard D Pearson Xuejun Liu Guido Sanguinetti Marta Milo Neil D Lawrence Magnus Rattray 《BMC bioinformatics》2009,10(1):211
Background
Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analyses it has been shown that we can improve results of differential expression detection, principal component analysis and clustering. Previously, implementations of these uncertainty propagation methods have only been available as separate packages, written in different languages. Previous implementations have also suffered from being very costly to compute, and in the case of differential expression detection, have been limited in the experimental designs to which they can be applied. 相似文献17.
Background
The quality of microarray data can seriously affect the accuracy of downstream analyses. In order to reduce variability and enhance signal reproducibility in these data, many normalization methods have been proposed and evaluated, most of which are for data obtained from cDNA microarrays and Affymetrix GeneChips. CodeLink Bioarrays are a newly emerged, single-color oligonucleotide microarray platform. To date, there are no reported studies that evaluate normalization methods for CodeLink Bioarrays. 相似文献18.
Background
Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or by improper probe matching between platforms. We investigated whether the significant level of inconsistency between results produced by alternative gene expression microarray platforms could be reduced by stringent sequence matching of microarray probes. We mapped the short oligo probes of the Affymetrix platform onto cDNA clones of the Stanford microarray platform. Affymetrix probes were reassigned to redefined probe sets if they mapped to the same cDNA clone sequence, regardless of the original manufacturer-defined grouping. The NCI-60 gene expression profiles produced by Affymetrix HuFL platform were recalculated using these redefined probe sets and compared to previously published cDNA measurements of the same panel of RNA samples. 相似文献19.