scholarly journals WTFgenes: What's The Function of these genes? Static sites for model-based gene set analysis

2017 ◽  
Author(s):  
Christopher J. Mungall ◽  
Ian H. Holmes
Keyword(s):  
Gene Ontology ◽  
Hypergeometric Distribution ◽  
Gene Set Analysis ◽  
Web Browser ◽  
Gene Set ◽  
Model Based ◽  
Link Type ◽  
Common Technique

AbstractA common technique for interpreting experimentally-identified lists of genes is to look for enrichment of genes associated to particular ontology terms. The most common technique uses the hypergeometric distribution; more recently, a model-based approach was proposed. These approaches must typically be run using downloaded software, or on a server. We develop a collapsed likelihood for model-based gene set analysis and present WTFgenes, an implementation of both hypergeometric and model-based approaches, that can be published as a static site with computation run in JavaScript on the user's web browser client. Apart from hosting files, zero server resources are required: the site can (for example) be served directly from Amazon S3 or GitHub Pages. A C++11 implementation yielding identical results runs roughly twice as fast as the JavaScript version. WTFgenes is available from https://github.com/evoldoers/wtfgenes under the BSD3 license. A demonstration for the Gene Ontology is usable at https://evoldoers.github.io/wtfgo. Contact: Ian Holmes [email protected].

scholarly journals WTFgenes: What's The Function of these genes? Static sites for model-based gene set analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 423
Author(s):  
Christopher J. Mungall ◽  
Ian H. Holmes
Keyword(s):  
Gene Ontology ◽  
Hypergeometric Distribution ◽  
Gene Set Analysis ◽  
Web Browser ◽  
Gene Set ◽  
Model Based ◽  
Common Technique

A common technique for interpreting experimentally-identified lists of genes is to look for enrichment of genes associated with particular ontology terms. The most common test uses the hypergeometric distribution; more recently, a model-based test was proposed. These approaches must typically be run using downloaded software, or on a server. We develop a collapsed likelihood for model-based gene set analysis and present WTFgenes, an implementation of both hypergeometric and model-based approaches, that can be published as a static site with computation run in JavaScript on the user's web browser client. Apart from hosting files, zero server resources are required: the site can (for example) be served directly from Amazon S3 or GitHub Pages. A C++11 implementation yielding identical results runs roughly twice as fast as the JavaScript version. WTFgenes is available from https://github.com/evoldoers/wtfgenes under the BSD3 license. A demonstration for the Gene Ontology is usable at https://evoldoers.github.io/wtfgo.

scholarly journals GOing Bayesian: model-based gene set analysis of genome-scale data

2010 ◽  
Vol 38 (11) ◽  
pp. 3523-3532 ◽  
Author(s):  
Sebastian Bauer ◽  
Julien Gagneur ◽  
Peter N. Robinson
Keyword(s):  
Bayesian Model ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Model Based ◽  
Genome Scale ◽  
Scale Data

scholarly journals Model-based gene set analysis for Bioconductor

2011 ◽  
Vol 27 (13) ◽  
pp. 1882-1883 ◽  
Author(s):  
S. Bauer ◽  
P. N. Robinson ◽  
J. Gagneur
Keyword(s):  
Gene Set Analysis ◽  
Gene Set ◽  
Model Based

scholarly journals A multi-functional analyzer uses parameter constraints to improve the efficiency of model-based gene-set analysis

2015 ◽  
Vol 9 (1) ◽  
pp. 225-246 ◽  
Author(s):  
Zhishi Wang ◽  
Qiuling He ◽  
Bret Larget ◽  
Michael A. Newton
Keyword(s):  
Gene Set Analysis ◽  
Gene Set ◽  
Model Based ◽  
Parameter Constraints

scholarly journals Functional-Network-Based Gene Set Analysis Using Gene-Ontology

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e55635 ◽  
Author(s):  
Billy Chang ◽  
Rafal Kustra ◽  
Weidong Tian
Keyword(s):  
Gene Ontology ◽  
Functional Network ◽  
Gene Set Analysis ◽  
Gene Set

scholarly journals Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yi Chen ◽  
Fons. J. Verbeek ◽  
Katherine Wolstencroft
Keyword(s):  
Gene Ontology ◽  
Enrichment Analysis ◽  
Biological Data ◽  
Hallmarks Of Cancer ◽  
High Throughput Analysis ◽  
Knowledge Resources ◽  
Gene Set ◽  
Cancer Hallmarks ◽  
Starting Point ◽  
High Level

Abstract Background The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus. Results Here we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis. Conclusions Reaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

scholarly journals DSigDB: drug signatures database for gene set analysis: Fig. 1.

2015 ◽  
Vol 31 (18) ◽  
pp. 3069-3071 ◽  
Author(s):  
Minjae Yoo ◽  
Jimin Shin ◽  
Jihye Kim ◽  
Karen A. Ryall ◽  
Kyubum Lee ◽  
...  
Keyword(s):  
Gene Set Analysis ◽  
Gene Set
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