We develop software packages and provide complete analysis workflows for our studies. The complete collection of software packages are available on the RBI Github page.
raer facilitates analysis of RNA adenosine editing in the Bioconductor ecosystem, enabling rigorous identification of editing events from both bulk and single-cell mRNA sequencing experiments.
djvdj
djvdj provides a range of tools to analyze and manipulate single cell V(D)J sequencing data, with a particular focus on a facile user experience, providing publication-quality plots exploring the VDJ repertoire and associated gene expression patterns.
clustifyr
clustifyr classifies cells and clusters in single-cell RNA sequencing experiments using reference bulk RNA-seq data sets, sorted microarray expression data, single-cell gene signatures, or lists of marker genes.
clustifyr: an R package for automated single-cell RNA sequencing cluster classification
Rui Fu, Austin E Gillen, Ryan M Sheridan, Chengzhe Tian, Michelle Daya, Yue Hao, Jay R Hesselberth, and Kent A Riemondy
Assignment of cell types from single-cell RNA sequencing (scRNA-seq) data remains a time-consuming and error-prone process. Current packages for identity assignment use limited types of reference data and often have rigid data structure requirements. We developed the clustifyr R package to leverage several external data types, including gene expression profiles to assign likely cell types using data from scRNA-seq, bulk RNA-seq, microarray expression data, or signature gene lists. We benchmark various parameters of a correlation-based approach and implement gene list enrichment methods. clustifyr is a lightweight and effective cell-type assignment tool developed for compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at https://github.com/rnabioco/clustifyr.
valr
valr provides tools to read and manipulate genome intervals and signals, similar to the BEDtools suite. valr enables analysis in the R/RStudio environment, leveraging modern R tools in the tidyverse for a terse, expressive syntax.
valr: Reproducible genome interval analysis in R
Kent A Riemondy, Ryan M Sheridan, Austin Gillen, Yinni Yu, Christopher G Bennett, and Jay R Hesselberth
New tools for reproducible exploratory data analysis of large datasets are important to address the rising size and complexity of genomic data. We developed the valr R package to enable flexible and efficient genomic interval analysis. valr leverages new tools available in the ``tidyverse'', including dplyr. Benchmarks of valr show it performs similar to BEDtools and can be used for interactive analyses and incorporated into existing analysis pipelines.
