scholarly journals Gene expression databases for kidney epithelial cells

2012 ◽  
Vol 302 (4) ◽  
pp. F401-F407 ◽  
Author(s):  
Jennifer C. Huling ◽  
Trairak Pisitkun ◽  
Jae H. Song ◽  
Ming-Jiun Yu ◽  
Jason D. Hoffert ◽  
...  

The 21st century has seen an explosion of new high-throughput data from transcriptomic and proteomic studies. These data are highly relevant to the design and interpretation of modern physiological studies but are not always readily accessible to potential users in user-friendly, searchable formats. Data from our own studies involving transcriptomic and proteomic profiling of renal tubule epithelia have been made available on a variety of online databases. Here, we provide a roadmap to these databases and illustrate how they may be useful in the design and interpretation of physiological studies. The databases can be accessed through http://helixweb.nih.gov/ESBL/Database .

Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.


2020 ◽  
pp. 580-592
Author(s):  
Libi Hertzberg ◽  
Assif Yitzhaky ◽  
Metsada Pasmanik-Chor

This article describes how the last decade has been characterized by the production of huge amounts of different types of biological data. Following that, a flood of bioinformatics tools have been published. However, many of these tools are commercial, or require computational skills. In addition, not all tools provide intuitive and highly accessible visualization of the results. The authors have developed GEView (Gene Expression View), which is a free, user-friendly tool harboring several existing algorithms and statistical methods for the analysis of high-throughput gene, microRNA or protein expression data. It can be used to perform basic analysis such as quality control, outlier detection, batch correction and differential expression analysis, through a single intuitive graphical user interface. GEView is unique in its simplicity and highly accessible visualization it provides. Together with its basic and intuitive functionality it allows Bio-Medical scientists with no computational skills to independently analyze and visualize high-throughput data produced in their own labs.


2008 ◽  
Vol 5 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Nicola Segata ◽  
Enrico Blanzieri ◽  
Corrado Priami

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.


2015 ◽  
Vol 11 (11) ◽  
pp. 509-511
Author(s):  
Jae-Hee Lee ◽  
◽  
Sang-Ho Kang ◽  
Jong-Yeol Lee ◽  
Chang-Kug Kim ◽  
...  

2017 ◽  
Author(s):  
Mikhail Pachkov ◽  
Piotr J Balwierz ◽  
Phil Arnold ◽  
Andreas J Gruber ◽  
Mihaela Zavolan ◽  
...  

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.


2012 ◽  
Vol 6 ◽  
pp. BBI.S9728
Author(s):  
Oksana Kohutyuk ◽  
Fadi Towfic ◽  
M. Heather West Greenlee ◽  
Vasant Honavar

Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from high-throughput analyses. Although many tools and databases are currently available for accessing such data, they are left unutilized by bench scientists as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by scientists with limited computational expertise. We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. It enables biologists to analyze public as well as private gene expression; interactively query gene expression datasets; integrate data from multiple networks; store and selectively share the data and results. Finally, we describe an application of BioNetwork Bench to the assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. The tool is available from http://bionetworkbench.sourceforge.net/ Background The emergence of high-throughput technologies has allowed many biological investigators to collect a great deal of information about the behavior of genes and gene products over time or during a particular disease state. Gene and protein networks offer a powerful approach for integration of the disparate yet complimentary types of data that result from such high-throughput analyses. There are a growing number of public databases, as well as tools for visualization and analysis of networks. However, such databases and tools have yet to be widely utilized by bench scientists, as they generally lack features for effective analysis and integration of both public and private datasets and do not offer an intuitive interface for use by biological scientists with limited computational expertise. Results We describe BioNetwork Bench, an open source, user-friendly suite of database and software tools for constructing, querying, and analyzing gene and protein network models. BioNetwork Bench currently supports a broad class of gene and protein network models (eg, weighted and un-weighted, undirected graphs, multi-graphs). It enables biologists to analyze public as well as private gene expression, macromolecular interaction and annotation data; interactively query gene expression datasets; integrate data from multiple networks; query multiple networks for interactions of interest; store and selectively share the data as well as results of analyses. BioNetwork Bench is implemented as a plug-in for, and hence is fully interoperable with, Cytoscape, a popular open-source software suite for visualizing macromolecular interaction networks. Finally, we describe an application of BioNetwork Bench to the problem of assembly and iterative expansion of a gene network that controls the differentiation of retinal progenitor cells into rod photoreceptors. Conclusions BioNetwork Bench provides a suite of open source software for construction, querying, and selective sharing of gene and protein networks. Although initially aimed at a community of biologists interested in retinal development, the tool can be adapted easily to work with other biological systems simply by populating the associated database with the relevant datasets.


BMC Genomics ◽  
2011 ◽  
Vol 12 (1) ◽  
Author(s):  
M Aleksi Kallio ◽  
Jarno T Tuimala ◽  
Taavi Hupponen ◽  
Petri Klemelä ◽  
Massimiliano Gentile ◽  
...  

Author(s):  
Mikhail Pachkov ◽  
Piotr J Balwierz ◽  
Phil Arnold ◽  
Andreas J Gruber ◽  
Mihaela Zavolan ◽  
...  

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.


2015 ◽  
Vol 18 (2) ◽  
pp. 101
Author(s):  
Gino Valentino Limmon

Epithelial cells are the primary target of respiratory viral infections and play a pivotal role in virusinducedlung infl ammation and in anti viral immune response. A common signal for the presence of viralinfections and induction of infl ammation is recognition of double stranded RNA (dsRNA). Thus far, therehas not been a high-throughput transcrptome analysis of RSV- or dsRNA-induced genes in primary humanbronchial epithelial cells (PHBE), nor there has been a comparison between dsRNA- and RSV-inducedgenes. To establish the transcriptome profi les and to determine the contribution of dsRNA in the inductionof infl ammation during respiratory virus infection, we compared the gene expression profi les of PHBE cellsthat were infected with Respiratory Syncytial Virus (RSV) or were treated with dsRNA. Our transcriptomeanalysis showed that RSV infection and and dsRNA treatment induced up-regulation of 2024 and 159 genesin PHBE respectively. Comparison of genes revealed that RSV and dsRNA commonly induced 75 genes inPHBE cells. The common up-regulated genes were functionally grouped in multiple response pathwaysinvolved in infl ammation and immune responses. Interestingly, there were several previously unreportedgenes that were up-regulated in primary human epithelial cells that are relevant to a TH2 allergic phenotype.This comparison of a high-throughput gene expression study offers a comprehensive view of transcriptionalchanges induced by dsRNA and RSV, and importantly compares dsRNA-induced genes with RSV-inducedgenes in PHBE cells. Keywords: RSV, dsRNA, transcriptome, immune response, infl ammation


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