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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 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
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scDrug: From single-cell RNA-seq to drug response prediction.

Chiao-Yu Hsieh1, Jian-Hung Wen1,2, Shih-Ming Lin1,3,4

  • 1Taiwan AI Labs, Taipei 10351, Taiwan.

Computational and Structural Biotechnology Journal
|December 22, 2022
PubMed
Summary

scDrug is a new bioinformatics workflow for analyzing single-cell RNA sequencing data. It helps identify tumor cell subpopulations and predict drug responses, aiding drug discovery and repurposing.

Keywords:
BioinformaticsDrug repositioningSingle-cell RNA-seqTumor cell subpopulations

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data for thousands of cells.
  • scRNA-seq is crucial for understanding the tumor microenvironment and its clinical implications.
  • Existing methods lack an integrated approach from scRNA-seq analysis to drug discovery.

Purpose of the Study:

  • To present scDrug, an integrated bioinformatics workflow for scRNA-seq data analysis.
  • To facilitate the identification of tumor cell subpopulations and predict drug responses.
  • To streamline the drug discovery and repurposing process using scRNA-seq data.

Main Methods:

  • scDrug employs a one-step pipeline for cell clustering of scRNA-seq data.
  • The workflow includes modules for scRNA-seq analysis, functional annotation, and drug response prediction.
  • Two distinct methods are incorporated for predicting drug treatments.

Main Results:

  • scDrug enables efficient identification of tumor cell subpopulations.
  • The workflow accurately predicts drug responses based on scRNA-seq data.
  • scDrug facilitates exploration of scRNA-seq data for drug repurposing.

Conclusions:

  • scDrug offers a comprehensive solution for scRNA-seq data analysis in cancer research.
  • The tool simplifies and accelerates the drug discovery pipeline.
  • scDrug is freely available, promoting wider accessibility and application.