ArticleMethods in molecular biology (Clifton, N.J.)2017
A Suite of Tools for Biologists That Improve Accessibility and Visualization of Large Systems Genetics Datasets: Applications to the Hybrid Mouse Diversity Panel.
Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Impact of trimethylamine N-oxide (TMAO) metaorganismal pathway on cardiovascular disease.Journal of laboratory and precision medicine · 2020Article
- Using BXD mouse strains in vision research: A systems genetics approach.Molecular vision · 2020Review
- Relevance of Multi-Omics Studies in Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2019Review
- The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases.Journal of lipid research · 2017Article
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4 authors.
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Abstract
In this chapter we address the recent explosion in large multilevel population studies such as the METSIM study in humans as well as large panels of animal models such as the Hybrid Mouse Diversity Panel or the BXD set of recombinant inbred strains. These studies have harnessed the increasing affordability of large-scale high-throughput profiling to gather massive quantities of data. These datasets, spread across different -omics levels (genome, transcriptome, etc.), different tissues (e.g. heart, plasma, bone) and different environmental factors (e.g. diet, drugs) each individually have led to a number of novel findings relevant to a variety of complex diseases and other phenotypes. The analysis of these results, however, is often limited to individuals with a comprehensive understanding of database languages such as SQL. In this chapter, we describe the development of a GUI-based database analysis suite, using the Hybrid Mouse Diversity Panel as an example to lay out a series of methods for visualization and integration of large systems genetics datasets. The database is based on the Shiny suite of tools in R, and is transferrable to other SQL-based datasets.
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