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Ivan Kuzmin

Researcher at University of Tartu

Publications -  8
Citations -  3331

Ivan Kuzmin is an academic researcher from University of Tartu. The author has contributed to research in topics: Genome-wide association study & Web server. The author has an hindex of 5, co-authored 8 publications receiving 1343 citations.

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g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).

TL;DR: G:Profiler is now capable of analysing data from any organism, including vertebrates, plants, fungi, insects and parasites, and the 2019 update introduces an extensive technical rewrite making the services faster and more flexible.
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gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler.

TL;DR: The accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API, provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R.
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A compendium of uniformly processed human gene expression and splicing quantitative trait loci.

TL;DR: The eQTL Catalogue as discussed by the authors is a set of gene expression quantitative trait locus (eQTL) studies published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization.
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Using bio.tools to generate and annotate workbench tool descriptions.

TL;DR: ToolDog (Tool DescriptiOn Generator) facilitates the integration of tools - which have been registered in the ELIXIR tools registry - into workbench environments by generating tool description templates using metadata provided by bio.tools.
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funcExplorer: a tool for fast data-driven functional characterisation of high-throughput expression data.

TL;DR: The open-source web tool funcExplorer enables scientists with high-throughput genomic data to obtain a preliminary interactive overview of the expression patterns, gene names, and shared functionalities in their dataset in a visually pleasing format.