Expression Profiler: next generation--an online platform for analysis of microarray data

Kapushesky, Misha; Kemmeren, Patrick; Culhane, Aedín C.; Durinck, Steffen; Ihmels, Jan; Körner, Christine; Kull, Meelis; Torrente, Aurora; Sarkans, Ugis; Vilo, Jaak; Brāzma, Alvis

doi:10.1093/nar/gkh470

Cited by 99 publications

(65 citation statements)

References 8 publications

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“…The statistical technique SAM (significance analysis of microarrays) was used for finding significant genes in the set of microarray experiments whereas two class unpaired data (metastasized vs. not metastasized tumors) were compared by 5000 permutations and false discovery rate of 0.7%. genes positively regulated (n=157) and negatively regulated (n=46) with a score (d) >2 were tested for a significant regulation (anoVa test, p<0.05) among the two groups of samples (metastasized vs. not metastasized) and were hierarchically clustered by Euclidian distance using the Expression Profiler at the ebi (19). for functional analysis of analyzed genes possibly responsible for metastatic invasion, sirna-knock down and invasion assays were carried out.…”

Section: Methodsmentioning

confidence: 99%

Differential expression of invasion promoting genes in childhood rhabdomyosarcoma

Armeanu–Ebinger

Bonin²,

Häbig³

et al. 2011

Int J Oncol

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Abstract. expression profiling of tumor tissue allows a systematic search for targeted therapies and offers relevant prognostic information. Molecular studies on rhabdomyosarcoma (RMS) revealed a more differentiated classification than the histological subgrouping into embryonal (rMe) and alveolar (RMA) rhabdomyosarcoma, and reflected the chromosomal aberrations found in rMS. We addressed biological processes like cell migration and emerging drug resistance by expression profiling to identify mechanisms of metastasic invasion and differential response to chemotherapy in rMS. gene expression analysis was performed in 19 rMS samples using the affymetrix u133 Plus2 array. Validation of target genes was performed by qrt-Pcr. Data were analyzed using Pathway analysis software. involvement of these genes in invasion processes was evaluated in knock-down experiments using specific interference rna and Matrigel tM invasion assay. in rMa tissues 211 of 534 genes were overexpressed, in rMe tissues 323 genes were overexpressed. Pathway analysis software identified a group of genes involved in cell growth, morphology and motility. in patients with distant metastases especially transcription factors such as foXf1 and lMo4 showed a high expression, which were described as determinants of tumor cell migration. Down-regulation of these factors inhibited the invasion of rMS cells >10-fold. Microarray technology is a powerful method not only to classify rMS samples, but also to identify major regulatory processes. functional evaluation of lMo4 and foXf1 identified targets of a molecular network for preventing metastatic invasion in rMS.

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Section: Methodsmentioning

confidence: 99%

Differential expression of invasion promoting genes in childhood rhabdomyosarcoma

Armeanu–Ebinger

Bonin²,

Häbig³

et al. 2011

Int J Oncol

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show abstract

“…Similar to Bixplorer, matrix-based visualizations enhanced with heatmaps are widespread in the bioinformatics domain for gene expression data analysis (e.g., BicAT [6], BiCluster viewer [40], BicOverlapper 2.0 [75], BiGGEsTS [34], BiVoc [37], Expression Profiler [54] and GAP [88]). To perform gene expression analysis, the collected raw microarray data are transformed into gene-expression matrices, where rows usually represent genes and columns stand for conditions [12].…”

Section: Matrix-based Visualizationsmentioning

confidence: 99%

A Five-Level Design Framework for Bicluster Visualizations

Sun

North

Ramakrishnan

2014

IEEE Trans. Visual. Comput. Graphics

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Abstract-Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptablity and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.

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“…The mean profile of each TRN mountain was calculated by averaging the expression values of the genes contained in the cluster. We then performed a hierarchical clustering of the matrix created using the EBI expression profiler (Kapushesky et al, 2004) to generate the hierarchical tree that was used to browse the co-expression network. Parameters: Euclidean distance, average linkage clustering, gene tree-based clustering.…”

Section: Identification Of Co-expressed Genesmentioning

confidence: 99%

Cis-regulatory properties of medaka synexpression groups

et al. 2012

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SUMMARYDuring embryogenesis, tissue specification is triggered by the expression of a unique combination of developmental genes and their expression in time and space is crucial for successful development. Synexpression groups are batteries of spatiotemporally co-expressed genes that act in shared biological processes through their coordinated expression. Although several synexpression groups have been described in numerous vertebrate species, the regulatory mechanisms that orchestrate their common complex expression pattern remain to be elucidated. Here we performed a pilot screen on 560 genes of the vertebrate model system medaka (Oryzias latipes) to systematically identify synexpression groups and investigate their regulatory properties by searching for common regulatory cues. We find that synexpression groups share DNA motifs that are arranged in various combinations into cis-regulatory modules that drive co-expression. In contrast to previous assumptions that these genes are located randomly in the genome, we discovered that genes belonging to the same synexpression group frequently occur in synexpression clusters in the genome. This work presents a first repertoire of synexpression group common signatures, a resource that will contribute to deciphering developmental gene regulatory networks.

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Expression Profiler: next generation--an online platform for analysis of microarray data

Cited by 99 publications

References 8 publications

Differential expression of invasion promoting genes in childhood rhabdomyosarcoma

Differential expression of invasion promoting genes in childhood rhabdomyosarcoma

A Five-Level Design Framework for Bicluster Visualizations

Cis-regulatory properties of medaka synexpression groups

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