The START App: a web-based RNAseq analysis and visualization resource.
Transcriptional profiling using RNA sequencing (RNAseq) has emerged as a powerful methodology to quantify global gene expression patterns in various contexts from single cells to whole tissues. The tremendous amount of data generated by this profiling technology presents a daunting challenge in terms of effectively visualizing and interpreting results. Convenient and intuitive data interfaces are critical for researchers to easily upload, analyze and visualize their RNAseq data. We designed the START (Shiny Transcriptome Analysis Resource Tool) App with these requirements in mind. This application has the power and flexibility to be resident on a local computer or serve as a web-based environment, enabling easy sharing of data between researchers and collaborators.



Source Code for the START App is written entirely in R and can be freely available to download at https://github.com/jminnier/STARTapp with the code licensed under GPLv3. It can be launched on any system that has R installed. The START App is also hosted on https://kcvi.shinyapps.io/START for researchers to temporarily upload their data.



minnier@ohsu.edu
The Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.
Jonathan W
JW
Nelson
The Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.
Jiri
J
Sklenar
Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA.
Anthony P
AP
Barnes
The Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.|School of Public Health, Oregon Health & Science University, Portland, OR, USA.
Jessica
J
Minnier
D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D020407:Internet; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software