The START App: A web-based RNAseq analysis and visualization resource

Jonathan W. Nelson, Jiri Sklenar, Anthony Barnes, Jessica Minnier

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)447-449
Number of pages3
JournalBioinformatics
Volume33
Issue number3
DOIs
StatePublished - Jan 1 2017

Fingerprint

RNA Sequence Analysis
Gene Expression Profiling
RNA
Application programs
Web-based
Sequencing
Visualization
Research Personnel
Resources
Information Dissemination
Profiling
Gene expression
Tissue
Technology
Gene Expression
Intuitive
Sharing
Quantify
Flexibility
Methodology

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

The START App : A web-based RNAseq analysis and visualization resource. / Nelson, Jonathan W.; Sklenar, Jiri; Barnes, Anthony; Minnier, Jessica.

In: Bioinformatics, Vol. 33, No. 3, 01.01.2017, p. 447-449.

Research output: Contribution to journalArticle

Nelson, Jonathan W. ; Sklenar, Jiri ; Barnes, Anthony ; Minnier, Jessica. / The START App : A web-based RNAseq analysis and visualization resource. In: Bioinformatics. 2017 ; Vol. 33, No. 3. pp. 447-449.
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