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miRetrieve is designed for microRNA text mining in abstracts. By extracting, counting, and analyzing miRNA names from literature, miRetrieve aims at providing biological insights from a large amount of text within a short period of time.
An online version with the most important functions of miRetrieve is available under https://miretrieve.shinyapps.io/miRetrieve/.
To install miRetrieve from CRAN, run
install.packages("miRetrieve")Alternatively, you can also install miRetrieve from GitHub by running
install.packages("devtools")
devtools::install_github("JulFriedrich/miRetrieve",
dependencies = TRUE,
repos = "https://cran.r-project.org/")miRetrieve is built around the idea of using field-specific PubMed abstracts from PubMed to characterize and analyze microRNAs in disease-related fields (e.g. “miRNAs in diabetes”).
To get started, download a microRNA-related abstract from PubMed via Save - Format: PMID - Create file and load it into R using
df <- miRetrieve::read_pubmed("PubMed_file.txt")and subsequently extract all microRNAs with
df <- extract_mir_df(df)An extensive Vignette with the underlying mechanism, functions, and a complete workflow is available under
https://julfriedrich.github.io/miRetrieve/articles/miRetrieve.html
Julian Friedrich, Hans-Peter Hammes, Guido Krenning
miRetrieve is published under the GPL-3 license.
miRetrieve and its functions are presented in a manuscript, currently under review.
Supplementary Files referenced in the manuscript are located in a different repository, freely available under
https://github.com/JulFriedrich/miRetrieve-paper
join_mirtarbase is based on the latest miRTarBase
version 8.0 (http://miRTarBase.cuhk.edu.cn/). If you use miRetrieve to
visualize miRNA-mRNA interactions based on miRTarBase, please make sure
to cite Hsi-Yuan Huang, Yang-Chi-Dung Lin, Jing Li, et al.,
miRTarBase 2020: updates to the experimentally validated microRNA–target
interaction database, Nucleic Acids Research, Volume 48, Issue D1, 08
January 2020, Pages D148–D154,
https://doi.org/10.1093/nar/gkz896.
compare_mir_terms_log2(),
compare_mir_count_log2(), and
compare_mir_terms_scatter() are greatly inspired by
“tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” by
Silge and Robinson (https://www.tidytextmining.com/). In addition,
“tidytext” provides a valuable resource of general text mining in
R.
Key packages for miRetrieve are tidytext, topicmodels, and the packages included in the tidyverse (see Vignette).
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.