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Related Concept Videos

RNA-seq03:21

RNA-seq

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 microarray-based...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Updated: May 9, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

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Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data.

Darragh G McArt1, Philip D Dunne, Jaine K Blayney

  • 1Centre for Cancer Research and Cell Biology(CCRCB), Queen's University Belfast, Belfast, United Kingdom.

Plos One
|July 11, 2013
PubMed
Summary
This summary is machine-generated.

Next generation sequencing (NGS) data can be integrated into connectivity mapping to identify potential drug candidates. This approach successfully identified cotinine as a compound that inhibits LNCaP cell proliferation.

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Last Updated: May 9, 2026

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Genomics
  • Pharmacology
  • Bioinformatics

Background:

  • Next generation sequencing (NGS) offers enhanced genomic research capabilities over microarrays.
  • Connectivity mapping uses gene expression profiles to link diseases, genes, and drugs.
  • Current connectivity mapping relies on microarray data, limiting integration with newer NGS technologies.

Purpose of the Study:

  • To assess the feasibility of incorporating NGS RNA-Seq data into the connectivity mapping framework.
  • To leverage existing microarray-based reference profiles with new NGS datasets.
  • To identify compounds inhibiting LNCaP cell proliferation using integrated NGS and microarray data.

Main Methods:

  • Constructed a differentially expressed gene signature from an NGS dataset.
  • Integrated the NGS gene signature with a microarray-based reference profile database.
  • Applied connectivity mapping to identify candidate compounds targeting LNCaP cell proliferation.
  • Validated candidate compounds through laboratory experiments.

Main Results:

  • Achieved high concordance between gene signatures derived from NGS and microarray datasets.
  • Identified nicotine derivative cotinine as a top candidate compound.
  • Demonstrated that cotinine inhibits LNCaP cell proliferation in an androgen-dependent manner.
  • Validated the effectiveness of integrating NGS data into connectivity mapping.

Conclusions:

  • Integrating NGS RNA-Seq data with connectivity mapping is feasible and effective.
  • This approach can identify novel therapeutic compounds and reduce costs associated with drug profiling.
  • The study highlights cotinine's potential for suppressing androgen-dependent LNCaP cell proliferation.