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
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
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).