Gene mapping, gene-set analysis, and genomic prediction of postpartum blood calcium in Holstein cows

Abstract Background Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

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Genome-wide association study and gene set analysis for understanding candidate genes involved in salt tolerance at the rice seedling stage

Molecular Genetics and Genomics ◽

10.1007/s00438-017-1354-9 ◽

2017 ◽

Vol 292 (6) ◽

pp. 1391-1403 ◽

Cited By ~ 13

Author(s):

Jie Yu ◽

Weiguo Zao ◽

Qiang He ◽

Tae-Sung Kim ◽

Yong-Jin Park

Keyword(s):

Salt Tolerance ◽

Association Study ◽

Candidate Genes ◽

Genome Wide Association Study ◽

Rice Seedling ◽

Seedling Stage ◽

Genome Wide Association ◽

Gene Set Analysis ◽

Gene Set ◽

Genome Wide

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Performance Comparison of Two Gene Set Analysis Methods for Genome-wide Association Study Results: GSA-SNP vs i-GSEA4GWAS

Genomics & Informatics ◽

10.5808/gi.2012.10.2.123 ◽

2012 ◽

Vol 10 (2) ◽

pp. 123 ◽

Cited By ~ 3

Author(s):

Ji-sun Kwon ◽

Jihye Kim ◽

Dougu Nam ◽

Sangsoo Kim

Keyword(s):

Association Study ◽

Genome Wide Association Study ◽

Performance Comparison ◽

Genome Wide Association ◽

Gene Set Analysis ◽

Gene Set ◽

Analysis Methods ◽

Genome Wide ◽

Study Results

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Enhancing gene set enrichment using networks

F1000Research ◽

10.12688/f1000research.17824.2 ◽

2019 ◽

Vol 8 ◽

pp. 129 ◽

Cited By ~ 1

Author(s):

Michael Prummer

Keyword(s):

Biological Function ◽

Automated Analysis ◽

Gene Set Analysis ◽

Molecular Pathways ◽

Human Intervention ◽

Gene Set Enrichment ◽

Topological Information ◽

Gene Set ◽

Gene Sets ◽

Differential Gene

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.

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