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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: Mar 1, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.

Vincent Gardeux1,2, Fabrice P A David2,3, Adrian Shajkofci1

  • 1Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.

Bioinformatics (Oxford, England)
|May 26, 2017
PubMed
Summary

Researchers can now easily analyze single-cell RNA sequencing (scRNA-seq) data with ASAP, a new web-based platform. This tool simplifies complex data analysis for all researchers, regardless of computational expertise.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) offers high-resolution transcriptome profiling of individual cells.
  • Many research groups lack the computational expertise for complex scRNA-seq data analysis.

Purpose of the Study:

  • To develop an integrated, web-based platform for comprehensive scRNA-seq data analysis.
  • To provide a user-friendly tool for researchers lacking computational expertise.

Main Methods:

  • Developed the Automated Single-cell Analysis Pipeline (ASAP), a web-based platform.
  • Integrated common algorithms for parsing, filtering, normalization, visualization, and gene set enrichment.
  • Ensured straightforward, real-time data interaction for users.

Main Results:

  • ASAP enables complete scRNA-seq data analysis post-genome alignment.
  • The platform facilitates identification of cell clusters and differentially expressed genes.
  • ASAP successfully reproduced results from a published single-cell study.

Conclusions:

  • ASAP provides an accessible solution for scRNA-seq data analysis.
  • The platform's design is broadly applicable to various RNA-seq datasets.
  • ASAP empowers researchers without computational backgrounds to analyze scRNA-seq data.