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Related Concept Videos

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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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Updated: Mar 14, 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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Single-Cell Transcriptomics Bioinformatics and Computational Challenges.

Olivier B Poirion1, Xun Zhu2, Travers Ching2

  • 1Epidemiology Program, University of Hawaii Cancer Center Honolulu, HI, USA.

Frontiers in Genetics
|October 7, 2016
PubMed
Summary
This summary is machine-generated.

Single-cell RNA sequencing (scRNA-Seq) offers high-resolution insights into diseases by revealing cell differences. This review covers bioinformatics tools and analytical challenges for interpreting complex scRNA-Seq data.

Keywords:
bioinformaticsheterogeneitymicroevolutionsingle-cell analysissingle-cell genomics

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-Seq) provides unprecedented resolution for studying cellular heterogeneity in biological processes and diseases.
  • Applications include cancer subpopulation characterization and understanding tumor resistance mechanisms.

Approach:

  • This review surveys current state-of-the-art bioinformatics tools and analytical methods for scRNA-Seq data.
  • It also addresses critical challenges in the interpretation of complex scRNA-Seq datasets.

Key Points:

  • scRNA-Seq technology is revolutionizing disease and biological process understanding.
  • Interpreting the complexity of scRNA-Seq data requires advanced bioinformatics approaches.
  • Addressing analytical challenges is crucial for maximizing the potential of scRNA-Seq.

Conclusions:

  • The field of scRNA-Seq analysis is rapidly evolving, necessitating continuous development of robust bioinformatics tools.
  • Effective interpretation of scRNA-Seq data is key to unlocking its full potential in biomedical research.
  • This review serves as a guide to current methods and future directions in scRNA-Seq data analysis.