scholarly journals An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Braja Gopal Patra ◽  
 Babak Soltanalizadeh ◽  
 Nan Deng ◽  
 Leqing Wu ◽  
Vahed Maroufy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Gene Expression Omnibus ◽  
Equation Model ◽  
Web Based ◽  
Domain Specific ◽  
Research Areas ◽  
Analytical Results ◽  
User Friendly ◽  
Expression Time

Abstract The exponential growth of genomic/genetic data in the era of Big Data demands new solutions for making these data findable, accessible, interoperable and reusable. In this article, we present a web-based platform named Gene Expression Time-Course Research (GETc) Platform that enables the discovery and visualization of time-course gene expression data and analytical results from the NIH/NCBI-sponsored Gene Expression Omnibus (GEO). The analytical results are produced from an analytic pipeline based on the ordinary differential equation model. Furthermore, in order to extract scientific insights from these results and disseminate the scientific findings, close and efficient collaborations between domain-specific experts from biomedical and scientific fields and data scientists is required. Therefore, GETc provides several recommendation functions and tools to facilitate effective collaborations. GETc platform is a very useful tool for researchers from the biomedical genomics community to present and communicate large numbers of analysis results from GEO. It is generalizable and broadly applicable across different biomedical research areas. GETc is a user-friendly and efficient web-based platform freely accessible at http://genestudy.org/

scholarly journals Gene Expression Time Course in the Human Skin during Elicitation of Allergic Contact Dermatitis

2007 ◽  
Vol 127 (11) ◽  
pp. 2585-2595 ◽  
Author(s):  
Malene B. Pedersen ◽  
Lone Skov ◽  
Torkil Menné ◽  
Jeanne D. Johansen ◽  
Jørgen Olsen
Keyword(s):  
Gene Expression ◽  
Contact Dermatitis ◽  
Allergic Contact Dermatitis ◽  
Human Skin ◽  
Time Course ◽  
Allergic Contact ◽  
Expression Time

scholarly journals Comparative analysis of clustering methods for gene expression time course data

2004 ◽  
Vol 27 (4) ◽  
pp. 623-631 ◽  
Author(s):  
Ivan G. Costa ◽  
Francisco de A. T. de Carvalho ◽  
Marcílio C. P. de Souto
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Time Course ◽  
Clustering Methods ◽  
Time Course Data ◽  
Expression Time

scholarly journals ImmuneRegulation: a web-based tool for identifying human immune regulatory elements

2019 ◽  
Vol 47 (W1) ◽  
pp. W142-W150 ◽  
Author(s):  
Selim Kalayci ◽  
Myvizhi Esai Selvan ◽  
Irene Ramos ◽  
Chris Cotsapas ◽  
Eva Harris ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Web Based ◽  
Transcriptome Regulation ◽  
Rich Data ◽  
User Friendly ◽  
Gene Expression Levels

Abstract Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu.

scholarly journals PIPE-T: a new Galaxy tool for the analysis of RT-qPCR expression data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Nicolò Zanardi ◽  
Martina Morini ◽  
Marco Antonio Tangaro ◽  
Federico Zambelli ◽  
Maria Carla Bosco ◽  
...  
Keyword(s):  
Gene Expression ◽  
Main Tool ◽  
Gene Expression Omnibus ◽  
Fast Method ◽  
Expression Data ◽  
Web Based ◽  
Qpcr Data ◽  
The Galaxy ◽  
R Packages ◽  
Polymerase Chain

AbstractReverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an accurate and fast method to measure gene expression. Reproducibility of the analyses is the main limitation of RT-qPCR experiments. Galaxy is an open, web-based, genomic workbench for a reproducible, transparent, and accessible science. Our aim was developing a new Galaxy tool for the analysis of RT-qPCR expression data. Our tool was developed using Galaxy workbench version 19.01 and functions implemented in several R packages. We developed PIPE-T, a new Galaxy tool implementing a workflow, which offers several options for parsing, filtering, normalizing, imputing, and analyzing RT-qPCR data. PIPE-T requires two input files and returns seven output files. We tested the ability of PIPE-T to analyze RT-qPCR data on two example datasets available in the gene expression omnibus repository. In both cases, our tool successfully completed execution returning expected results. PIPE-T can be easily installed from the Galaxy main tool shed or from Docker. Source code, step-by-step instructions, and example files are available on GitHub to assist new users to install, execute, and test PIPE-T. PIPE-T is a new tool suitable for the reproducible, transparent, and accessible analysis of RT-qPCR expression data.

A Bayesian approach to virus-gene expression time course data

2005 ◽  
Author(s):  
I.-S. Chang ◽  
Chi-Chung Wen ◽  
Yuh-Jenn Wu ◽  
P.K. Gupta ◽  
Shih Sheng Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bayesian Approach ◽  
Time Course ◽  
Virus Gene ◽  
Virus Gene Expression ◽  
Time Course Data ◽  
Expression Time

scholarly journals Exploring Drivers of Gene Expression in The Cancer Genome Atlas

2017 ◽  
Author(s):  
Andrea Rau ◽  
Michael Flister ◽  
Hallgeir Rui ◽  
Paul L. Auer
Keyword(s):  
Gene Expression ◽  
Germ Line ◽  
Molecular Data ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Web Based ◽  
Statistical Framework ◽  
Cancer Genome Atlas ◽  
User Friendly ◽  
Genome Atlas

The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating, and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells. To more thoroughly investigate the behavior of gene expression in TCGA tumor samples, we introduce a statistical framework for partitioning the variation in gene expression due to a variety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation, and germ-line genetic variation. As proof-of-principle, we identify and validate specific TFs that influence the expression of PTPN14 in breast cancer cells. We provide a freely available, user-friendly, browseable interactive web-based application for exploring the results of our transcriptome-wide analyses across 17 different cancers in TCGA at http://ls-shiny-prod.uwm.edu/edge_in_tcga.

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