TM4: A Free, Open-Source System for Microarray Data Management and Analysis

Saeed, Alexander I.; Sharov, Vasily; White, Joseph A.; Li, J.; Liang, Wei; Bhagabati, Nirmal; Braisted, John C.; Klapa, Maria I.; Currier, Tracey; Thiagarajan, Mathangi; Sturn, Alexander; Snuffin, M.; Rezantsev, A.; Попов, Д. А.; Ryltsov, A.; Kostukovich, E.; Borisovsky, I.; Liu, Z.; Vinsavich, A.; Trush, V.; Quackenbush, John

doi:10.2144/03342mt01

Cited by 4,297 publications

(3,206 citation statements)

References 8 publications

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“…Microarray experiments followed the procedures previously described [16]. Fluorescence intensities were normalized by applying the Lowess algorithm [17]. Slice analysis was applied as criteria for selection of modulated genes.…”

Section: Microarray Analysismentioning

confidence: 99%

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Transcriptional response to ionizing radiation in lymphocyte subsets

Mori

Benotmane

Tirone

et al. 2005

CMLS, Cell. Mol. Life Sci.

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Human lymphocyte subpopulations differ in their cellular responses to ionizing radiation. To shed light on the molecular basis of this effect, we characterized the transcriptional response to 1 Gy X-rays of CD4+ T lymphocytes. Of 18,433 genes tested, 102 were modulated more than 1.5-fold. The majority of the strongly activated genes were p53 targets involved in DNA repair and apoptosis. The expression of three of these genes was further tested by quantitative RT-PCR in lymphocyte subpopulations [CD4+ and CD8+ T, CD19+ B, CD56+ natural killer cells and peripheral blood lymphocytes (PBLs)] from ten adult donors. In contrast to DDB2, TNFRSF10B and BAX were differentially modulated among the subpopulations and the PBLs, being more activated in irradiated CD19+ B and CD8+ T lymphocytes. The level of BAX activation in the various subpopulations correlated with the sensitivity of the cells to radiation, suggesting its possible role in the differential radiosensitivity of hematopoietic cell subsets.

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Section: Microarray Analysismentioning

confidence: 99%

“…Slice analysis was applied as criteria for selection of modulated genes. Data point population of 50 and data keep range over 2 standard deviations were used as filter [17]. Functional analysis groups were defined using Database for Annotation, Visualization and Integrated Discovery (DAVID: http://apps1.niaid.nih.gov/david/).…”

Section: Microarray Analysismentioning

confidence: 99%

Transcriptional response to ionizing radiation in lymphocyte subsets

Mori

Benotmane

Tirone

et al. 2005

CMLS, Cell. Mol. Life Sci.

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“…For serumexposed NBK relative to NBK, all differentially expressed genes were evaluated using unpaired, twotailed t-test as well as statistically verified by Statistical Analysis of Microarray (SAM) [36]. Heat maps were generated in the TIGR Multiexperiment Viewer 4.1 program (http://www.tm4.org) [37].…”

Section: Methodsmentioning

confidence: 99%

Serum-responsive expression of carbonyl-metabolizing enzymes in normal and transformed human buccal keratinocytes

Staab

Ceder

Roberg

et al. 2008

Cell. Mol. Life Sci.

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Gene expression of carbonyl-metabolizing enzymes (CMEs) was investigated in normal buccal keratinocytes (NBK) and the transformed buccal keratinocyte lines SVpgC2a and SqCC/Y1. Studies were performed at a serum concentration known to induce terminal squamous differentiation (TSD) in normal cells. Overall, 39 of 58 evaluated CMEs were found to be expressed at the transcript level. Together the transformed cell lines showed altered transcription of eight CME genes compared to NBK, substantiating earlier results. Serum increased transcript levels of ALDH1A3, DHRS3, HPGD and AKR1A1, and decreased those of ALDH4A1 in NBK; of these, the transformed, TSD-deficient cell lines partly retained regulation of ALDH1A3 and DHRS3. Activity measurements in crude cell lysates, including relevant enzymatic inhibitors, indicated significant capacity for CME-mediated xenobiotic metabolism among the cell lines, notably with an increase in serum-differentiated NBK. The results constitute the first evidence for differential CME gene expression and activity in non-differentiated and differentiated states of epithelial cells.

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“…Because the data structure is essentially identical with that of ordinary microarray data, all data mining methodology developed for transcriptome analysis can be used for integrated analysis. MultiExperiment Viewer (MeV) is a free Java application that implements many advanced modules for analyzing microarray data [51,52].…”

Section: Principal Component Analysis and Application Of Other Data Mmentioning

confidence: 99%

Integrative Analysis of Secondary Metabolism and Transcript Regulation in Arabidopsis Thaliana

Mizutani

Saito

2013

The Handbook of Plant Metabolomics

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Metabolic states in plants are dynamically controlled via transcriptional regulation in response to environmental and developmental conditions. However, the regulatory mechanisms between gene expression (input) and the resultant metabolite accumulation (output) remain unclear because complex post-transcriptional events such as feedback regulation and inter-tissue translocation often play important roles in these mechanisms [1]. Hence detailed investigations of the dynamic behavior of metabolic systems and an understanding of their general rules are a major challenge for plant systems biology [2,3]. One promising strategy is a global survey of input (gene expression) and output (metabolite accumulation) signals to estimate the mechanisms working within these systems [4]. In this regard, recent advances in analytical and informatics technologies enable us to perform integrated analyses of transcriptome and metabolome data while considering metabolic pathway information [5].The pioneering applications of this strategy involved the investigation of the reprogramming of gene expression and metabolism triggered by nutritional stresses such as sulfur starvation [6][7][8][9]. This research demonstrated that two main types of information can be derived from integrated analysis [10]. The first outcome of such studies was the discovery of a gene-to-metabolite network regulating plant metabolism during environmental stresses. Integrated analysis of time-course data showed that groups of metabolites and genes related to primary and secondary metabolites are coordinately modulated by sulfur deficiency-induced stress [8,[11][12][13][14]. A similar analysis was performed for various plants such as cold-acclimating Arabidopsis, pathogen-infected Medicago, fruit ripening tomato, and metabolically engineered rice [15][16][17][18][19][20]. Another outcome was the successful prediction of novel gene function by using the rules underlying gene expressions and metabolite accumulation. Integrated analysis allowed the prediction of genes involved in glucosinolate biosynthesis (e.g., genes encoding sulfotransferases [21], 2 MYB transcription factors [9], and chain elongation enzymes [22,23]) in a comprehensive manner. The strategy was also used in nonmodel plants [24,25].

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TM4: A Free, Open-Source System for Microarray Data Management and Analysis

Cited by 4,297 publications

References 8 publications

Transcriptional response to ionizing radiation in lymphocyte subsets

Transcriptional response to ionizing radiation in lymphocyte subsets

Serum-responsive expression of carbonyl-metabolizing enzymes in normal and transformed human buccal keratinocytes

Integrative Analysis of Secondary Metabolism and Transcript Regulation in Arabidopsis Thaliana

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