Making biological sense of genome-wide expression data

10.1038/14234 ◽  
1999 ◽  
Vol 23 (S3) ◽  
pp. 18-18 ◽  
Author(s):  
Mike Eisen
Keyword(s):  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Expression

scholarly journals Use of Genome-Wide Expression Data to Mine the “Gray Zone” of GWA Studies Leads to Novel Candidate Obesity Genes

PLoS Genetics ◽  
2010 ◽  
Vol 6 (6) ◽  
pp. e1000976 ◽  
Author(s):  
Jussi Naukkarinen ◽  
Ida Surakka ◽  
Kirsi H. Pietiläinen ◽  
Aila Rissanen ◽  
Veikko Salomaa ◽  
...  
Keyword(s):  
Expression Data ◽  
Gray Zone ◽  
Genome Wide ◽  
Obesity Genes ◽  
Gwa Studies ◽  
Genome Wide Expression

scholarly journals Stage prediction of embryonic stem cell differentiation from genome-wide expression data

2011 ◽  
Vol 27 (18) ◽  
pp. 2546-2553 ◽  
Author(s):  
Lan Zagar ◽  
Francesca Mulas ◽  
Silvia Garagna ◽  
Maurizio Zuccotti ◽  
Riccardo Bellazzi ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Expression

scholarly journals Viewing RNA-seq data on the entire human genome

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 596 ◽  
Author(s):  
Eric M. Weitz ◽  
Lorena Pantano ◽  
Jingzhi Zhu ◽  
Bennett Upton ◽  
Ben Busby
Keyword(s):  
Web Application ◽  
Source Code ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Rna Seq ◽  
Sequence Read Archive ◽  
Data Pipeline ◽  
Genome Wide ◽  
Small Team ◽  
Genome Wide Expression

RNA-Seq Viewer is a web application that enables users to visualize genome-wide expression data from NCBI’s Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) databases. The application prototype was created by a small team during a three-day hackathon facilitated by NCBI at Brandeis University. The backend data pipeline was developed and deployed on a shared AWS EC2 instance. Source code is available at https://github.com/NCBI-Hackathons/rnaseqview.

scholarly journals Comparative proteomics and global genome-wide expression data implicate role of ARMC8 in lung cancer

2015 ◽  
Vol 16 (9) ◽  
pp. 3691-3696 ◽  
Author(s):  
Asif Amin ◽  
Shoiab Bukhari ◽  
Taseem A Mokhdomi ◽  
Naveed Anjum ◽  
Asrar H Wafai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Comparative Proteomics ◽  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Expression

scholarly journals Cloudy with a Chance of Insights: Context Dependent Gene Regulation and Implications for the Evolution of Gene Expression

2019 ◽  
Author(s):  
Elisa Buchberger ◽  
Micael Reis ◽  
Ting-Hsuan Lu ◽  
Nico Posnien
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Evolutionary Biology ◽  
Expression Data ◽  
Regulatory Evolution ◽  
Specific Expression ◽  
Genome Wide ◽  
Key Driver ◽  
Context Dependent ◽  
Genome Wide Expression

Research in various fields of evolutionary biology has shown that divergence in gene expression is a key driver for phenotypic variation. An exceptional contribution of cis-regulatory evolution has for instance been found to contribute to morphological diversification. In the light of these findings, the analysis of genome-wide expression data has become one of the central tools to link genotype and phenotype information on a more mechanistic level. However, in many studies, especially if general conclusions are drawn from such data, a key feature of gene regulation is often neglected. With our article, we want to raise awareness that gene regulation and thus gene expression is highly context dependent. Genes show tissue- and developmental stage-specific expression. We argue that the regulatory context must be considered when studying evolution of gene expression.

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