WASP: a versatile, web-accessible single cell RNA-Seq processing platform

Abstract

Background The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software. Results Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources. Conclusions With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP’s post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.

GXP: Analyze and Plot Plant Omics Data in Web Browsers

“…We revised platforms and software solutions that have been developed to provide the user with tools to quantify RNAseq raw data and contrast this quantified data in differential expression analysis. The revision includes tools that generate scientific plots, carry out clustering, principal component, and overrepresentation analysis [ 1 , 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 31 , 32 , 35 , 41 ]. However, the presented tools require either some programming expertise, or manual installation of software, or send potentially confidential data via the web to dedicated servers.…”

“…Among others, they provide means to plot the data, carry out clustering, and conduct principal component and overrepresentation analyses. A number of these tools specialize in RNAseq analysis [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], most of which consume the raw gene expression count data produced by standard gene expression quantifiers [ 22 , 23 , 24 , 25 ] and enable the user to identify differentially expressed genes [ 6 , 7 , 8 , 9 , 11 , 12 , 13 , 15 , 16 , 20 , 21 , 26 , 27 ] and review the results in form of comprehensive reports and/or plots [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 15 , 16 , 18 , 19 , 20 , 21 , 26 , 27 , 28 ]. Some [ 7 , 8 , 9 ,…”

SCALA: A web application for multimodal analysis of single cell next generation sequencing data

“…It handles both raw and pre-processed datasets, thus giving the users the flexibility to perform their analysis from-scratch, or to visualize and reanalyze already processed data. For comparison, we report a catalog of similar tools (e.g., pagoda2, SingleCAnalyzer (55), Bingle-seq (56), iCellR (57), cerebro (29), Is-CellR (58), SeuratWizard (31), ICARUS (59), SC1 (60), alona (61), WASP (62), CHIPSTER (63), Asc-Seurat (64), GenePattern (65), PIVOT (66) and we highlight their pros and cons along with their complementarity to SCALA (Supplementary Table 1). To this end, it is worth mentioning that to our knowledge, only icellR (67) offers scATAC seq analysis whereas only six applications are available as web server applications.…”

Bioinformatics roadmap for therapy selection in cancer genomics

“…Importantly, the generation of community‐maintained and versatile analysis pipelines could help in solving this issue. The bollito pipeline [ 179 ], the W eb ‐A ccessible S ingle C ell RNA‐S eq P rocessing P latform (WASP) [ 180 ] and the S ingle C ell I nteractive A pplication (SCiAp) [ 181 ] are some of the latest efforts in this direction. In general terms, current single‐cell analysis workflows can be subdivided into three main steps: the raw data processing steps or primary analysis; the normalisation and clustering steps, also known as secondary analysis, and the tertiary analysis that involves the functional interpretation of the results.…”