BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis

Abstract

Background RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both of these approaches include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that they require programing expertise. Although some effort has been directed toward the development of user-friendly RNA-Seq analysis analysis tools, few have the flexibility to explore both Bulk and single-cell RNA sequencing. Implementation BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface which incorporates three state-of-the-art software packages for each type of the aforementioned analyses. Furthermore, BingleSeq includes additional features such as visualization techniques, extensive functional annotation analysis and rank-based consensus for differential gene analysis results. As a result, BingleSeq puts some of the best reviewed and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programing experience. Availability BingleSeq is as an easy-to-install R package available on GitHub at https://github.com/dbdimitrov/BingleSeq/.

OmicVerse: A single pipeline for exploring the entire transcriptome universe

“…OmicVerse integrates an extensive collection of Bulk RNA-seq analysis algorithms, previously developed mostly in R but now increasingly in Python, to promote their use and interconnectivity 33 , to promote their use and interconnectivity. Our integration enhances the existing repertoire of analysis algorithms catering to single-cell, spatial transcriptomics, as well as machine learning and deep learning models 34 .…”

Interactive analysis of single-cell data using flexible workflows with SCTK2.0

“…Comparison to other tools with GUI for scRNA-seq analysis Some other tools and packages are available that provide graphical user interface to scRNA-seq data analysis. We compared the availability of supported methods between SCTK and Pegasus [5], ASAP [6][7], and BingleSeq [8] (Table S1). Generally, SCTK supports more methods and options for the various stages of a typical scRNAseq analysis.…”

“…Moreover, many researchers without strong computational backgrounds are generating scRNA-seq data but do not have necessary training for analysis and interpretation. Currently, there are limited options for frameworks that allows for interoperability of tools across environments and contains a graphical user interface (GUI) for non-computational users to perform flexible end-to-end analysis [5][6] [7] [8]. While some web applications are available for the analysis of scRNA-seq data, there are no online tools that can import data from a variety of formats, perform comprehensive quality control and filtering, run flexible clustering and trajectory workflows, and apply a series of downstream analysis and visualization tools within an interactive interface amiable to users without a strong programming background.…”

Asc-Seurat: analytical single-cell Seurat-based web application

“…Tools that simplify the iterative scRNA-seq analysis process and the integration across analysis platforms are essential for biologists. Recently, several such tools have been developed for scRNA-seq [7][8][9][10][11][12][13][14]. While these tools aim to solve similar issues-providing a graphical user interface as an alternative to command-line-they often address different aspects of the analysis, contain different underlying software, and may not encompass all analytical steps.…”

“…Still, its reliance on the Galaxy framework [15] makes its installation and use difficult for scientists without a computational background. ASAP [7], BingleSeq [10], and Seurat Wizard (now part of NASQAR [11]) offer Seurat's capabilities, but lack TI analysis support. Others, such as SC1 [14] and PIVOT [8], provide tools for many of the analytical steps but are based on methods that are less widely adopted by the community than the Seurat toolkit and only offer one model for TI.…”