To extract functional information on genes and processes from large expression datasets, analysis methods are required that can computationally deal with these amounts of data, are tunable to specific research questio...
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To extract functional information on genes and processes from large expression datasets, analysis methods are required that can computationally deal with these amounts of data, are tunable to specific research questions, and construct classifiers that are not overspecific to the dataset at hand. To satisfy these requirements, a stepwise procedure that combines elements from principal component analysis and discriminant analysis, was developed to specifically retrieve genes involved in processes of interest and classify samples based upon those genes. In a global expression dataset of 300 gene knock-outs in Saccharomyces cerevisiae, the procedure successfully classified samples with similar 'cellular component' gene Ontology annotations of the knock-out gene by expression signatures of limited numbers of genes. The genes discriminating 'mitochondrion' from the other subgroups were evaluated in more detail. The thiamine pathway turned out to be one of the processes involved and was successfully evaluated in a logistic model to predict whether yeast knock-outs were mitochondrial or not. Further, this pathway is biologically related to the mitochondrial system. Hence, this strongly indicates that our approach is effective and efficient in extracting meaningful information from large microarray experiments and assigning functions to yet uncharacterized genes. Copyright (C) 2007 John Wiley & Sons, Ltd.
The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, we have built a system called GEMS (geneexpression model selector) for the automated development and evalua...
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The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, we have built a system called GEMS (geneexpression model selector) for the automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarraygeneexpression data. In order to determine and equip the system with the best performing diagnostic methodologies in this domain, we first conducted a comprehensive evaluation of classification algorithms using 11 cancer microarray datasets. In this paper we present a preliminary evaluation of the system with five new datasets. The performance of the models produced automatically by GEMS is comparable or better than the results obtained by human analysts. Additionally, we performed a cross-dataset evaluation of the system. This involved using a dataset to build a diagnostic model and to estimate its future performance, then applying this model and evaluating its performance on a different dataset. We found that models produced by GEMS indeed perform well in independent samples and, furthermore, the cross-validation performance estimates output by the system approximate well the error obtained by the independent validation. GEMS is freely available for download for non-commercial use from http://***. (C) 2005 Elsevier Ireland Ltd. All rights reserved.
geneexpression profiling using microarrays requires microgram amounts of RNA, which limits its direct application for the study of nanogram RNA samples obtained using microdissection, laser capture microscopy, or nee...
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geneexpression profiling using microarrays requires microgram amounts of RNA, which limits its direct application for the study of nanogram RNA samples obtained using microdissection, laser capture microscopy, or needle biopsy. A novel system based on Ribo-SPIA technology (RS, Ovation-Biotin amplification and labeling system) was recently introduced. The utility of the RS system, an optimized prototype system for picogram RNA samples (pRS), and two T7-based systems involving one or two rounds of amplification (OneRA, Standard Protocol, or TwoRA, Small Sample Prototcol, version II) were evaluated in the present study. Mouse kidney (MK) and mouse universal reference (MUR) RNA samples, 0.3 ng to 10 mu g, were analyzed using high-density Affymetrix Mouse Genome 430 2.0 geneChip arrays. Call concordance between replicates, correlations of signal intensity, signal intensity ratios, and minimal fold increase necessary for significance were determined. All systems amplified partially overlapping sets of genes with similar signal intensity correlations. pRS amplified the highest number of genes from 10-ng RNA samples. We detected 24 of 26 genes verified by RT-PCR in samples prepared using pRS. TwoRA yielded somewhat higher call concordances than did RS and pRS (91.8% vs. 89.3% and 88.1%, respectively). Although all target preparation methods were suitable, pRS amplified the highest number of targets and was found to be suitable for amplification of as little as 0.3 ng of total RNA. In addition, RS and pRS were faster and simpler to use than the T7-based methods and resulted in the generation of cDNA, which is more stable than cRNA.
Toward functional genomics of flow induced outward remodeling of resistance arteries. Am J Physiol Heart Circ Physiol 288: H1022 - H1027, 2005;doi: 10.1152/ ajpheart. 00800.2004. - In resistance- sized arteries, a chr...
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Toward functional genomics of flow induced outward remodeling of resistance arteries. Am J Physiol Heart Circ Physiol 288: H1022 - H1027, 2005;doi: 10.1152/ ajpheart. 00800.2004. - In resistance- sized arteries, a chronic increase in blood flow leads to increases in arterial structural luminal diameter and arterial wall mass. In this review, we summarize recent evidence that outward remodeling of resistance arteries 1) can help maintain and restore tissue perfusion, 2) is not intimately related to flowinduced vasodilatation, 3) involves transient dedifferentiation and turnover of arterial smooth muscle cells, and 4) is preceded by increased expression of matricellular proteins, which have been shown to promote disassembly of focal adhesion sites. Studies of experimental and physiological resistance artery remodeling involving differential geneexpression analyses and the use of knockout and transgenic mouse models can help unravel the mechanisms of outward remodeling.
Background. Tissue ischemia and aging are independent features associated with the healing impairment of cutaneous wounds. However, the pathophysiology of these processes as they relate to impaired-healing wounds is p...
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Background. Tissue ischemia and aging are independent features associated with the healing impairment of cutaneous wounds. However, the pathophysiology of these processes as they relate to impaired-healing wounds is poorly understood. Materials and methods. A single full-thickness biopsy wound was made on both ears of young (3-6 month) and aged (>24 month) Fisher rats. One car was rendered ischemic by transection of the vasculature at the ear base, while the other ear served as an internal nonischemic control. Wounds were harvested from 3 to 7 days and were evaluated histologically for either granulation tissue formation and epithelialization. Total RNA from wounds harvested at postoperative day 7 was probed using a nylon-based cDNA array to assess global genetic expression alterations. Results. Healing in the rat ear model is impaired by both ischemia and advanced age as measured by granulation tissue formation and wound epithelialization. Granulation tissue formation was affected to a greater degree by ischemia than age (-58% versus -21%, respectively) while epithelialization displayed an opposite response (- 17% versus - 53%, respectively). Global analysis of geneexpression suggests that ischemia engenders a marked increase in genes displaying altered expression in aged animals compared to young animals. Importantly, all possible alterations in geneexpression are found in samples from aged ischemic wounds, indicating that gene regulation is not simply depressed by advanced age. Conclusions. Wound epithelialization appears to be affected to a greater degree by advanced age than by ischemia. The results demonstrate the distinctive phenotype presented by the clinically relevant combination of age and ischemia in an in vivo model of cutaneous wound healing. (C) 2004 Elsevier Inc. All rights reserved.
Large-scale geneexpression measurement techniques provide a unique opportunity to gain insight into biological processes under normal and pathological conditions. To interpret the changes in expression profiles for t...
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Large-scale geneexpression measurement techniques provide a unique opportunity to gain insight into biological processes under normal and pathological conditions. To interpret the changes in expression profiles for thousands of genes, we face the nontrivial problem of understanding the significance of these changes. In practice, the sources of background variability in expression data can be divided into three categories: technical, physiological, and sampling. To assess the relative importance of these sources of background variation, we generated replicate geneexpression profiles on high-density Affymetrix geneChip oligonucleotide arrays, using either identical RNA samples or RNA samples obtained under similar biological states. We derived a novel measure of dispersion in two-way comparisons, using a linear characteristic function. When comparing expression profiles from replicate tests using the same RNA sample (a test for technical variability), we observed a level of dispersion similar to the pattern obtained with RNA samples from replicate cultures of the same cell line (a test for physiological variability). On the other hand, a higher level of dispersion was observed when tissue samples of different animals were compared (an example of sampling variability). This implies that, in experiments in which samples from different subjects are used, the variation induced by the stimulus may be masked by non-stimuli-related differences in the subjects' biological state. These analyses underscore the need for replica experiments to reliably interpret large-scale expression data sets, even with simple microarray experiments.
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