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Expression Profiles

gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell. [Source: Wikipedia]

iSubgraph

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The high tumor heterogeneity makes it very challenging to identify key tumorigenic pathways as therapeutic targets. The integration of multiple omics data is a promising approach to identify driving regulatory networks in patient subgroups. Here, we propose a novel conceptual framework to discover patterns of miRNA-gene networks, observed frequently up- or down-regulated in a group of patients and to use such networks for patient stratification in hepatocellular carcinoma (HCC).

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RiceATM

Submitted by ChenLiang on Mon, 01/09/2017 - 11:36

MicroRNAs (miRNAs) are known to play critical roles in plant development and stress-response regulation, and they frequently display multi-targeting characteristics. The control of defined rice phenotypes occurs through multiple genes; however, evidence demonstrating the relationship between agronomic traits and miRNA expression profiles is lacking. In this study, we investigated eight yield-related traits in 187 local rice cultivars and profiled the expression levels of 193 miRNAs in these cultivars using microarray analyses.

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BCGSC miRNA Profiling Pipeline

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The comprehensive multiplatform genomics data generated by The Cancer Genome Atlas (TCGA) Research Network is an enabling resource for cancer research. It includes an unprecedented amount of microRNA sequence data: ~11 000 libraries across 33 cancer types. Combined with initiatives like the National Cancer Institute Genomics Cloud Pilots, such data resources will make intensive analysis of large-scale cancer genomics data widely accessible.

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MixMir

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

microRNAs (miRNAs) are a class of ~22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. miRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir.

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GiSAO.db

Submitted by ChenLiang on Thu, 04/06/2017 - 17:51

Age-related gene expression patterns of Homo sapiens as well as of model organisms such as Mus musculus, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster are a basis for understanding the genetic mechanisms of ageing. For an effective analysis and interpretation of expression profiles it is necessary to store and manage huge amounts of data in an organized way, so that these data can be accessed and processed easily.

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GeneHub-GEPIS

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

GeneHub-GEPIS is a web application that performs digital expression analysis in human and mouse tissues based on an integrated gene database. Using aggregated expressed sequence tag (EST) library information and EST counts, the application calculates the normalized gene expression levels across a large panel of normal and tumor tissues, thus providing rapid expression profiling for a given gene.

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Ortho2ExpressMatrix

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The study of gene families is pivotal for the understanding of gene evolution across different organisms and such phylogenetic background is often used to infer biochemical functions of genes. Modern high-throughput experiments offer the possibility to analyze the entire transcriptome of an organism; however, it is often difficult to deduct functional information from that data.

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miARma-Seq

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Large-scale RNAseq has substantially changed the transcriptomics field, as it enables an unprecedented amount of high resolution data to be acquired. However, the analysis of these data still poses a challenge to the research community. Many tools have been developed to overcome this problem, and to facilitate the study of miRNA expression profiles and those of their target genes. While a few of these enable both kinds of analysis to be performed, they also present certain limitations in terms of their requirements and/or the restrictions on data uploading.

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PCBC

Submitted by ChenLiang on Thu, 04/06/2017 - 18:48

The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples.

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COGERE

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components.

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