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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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Related Experiment Video

Updated: Mar 16, 2026

Sequencing of mRNA from Whole Blood using Nanopore Sequencing
11:26

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Published on: June 3, 2019

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INC-Seq: accurate single molecule reads using nanopore sequencing.

Chenhao Li1,2, Kern Rei Chng1, Esther Jia Hui Boey1

  • 1Genome Institute of Singapore, Singapore, 138672, Singapore.

Gigascience
|August 4, 2016
PubMed
Summary
This summary is machine-generated.

Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq) improves nanopore sequencing accuracy for 16S rRNA profiling. This method achieves high-accuracy, full-length reads for reliable bacterial identification and microbiome analysis.

Keywords:
Barcode sequencingConsensus algorithmsNanopore sequencingRolling circle amplification

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Last Updated: Mar 16, 2026

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Nanopore sequencing offers rapid, portable, real-time genomic data.
  • High single-read error rates (>10%) limit applications like RNA-seq, haplotype, and 16S sequencing.

Purpose of the Study:

  • To develop a method for long and accurate nanopore reads from low DNA input.
  • To enhance the utility of nanopore sequencing for applications requiring high accuracy.

Main Methods:

  • Development of Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq).
  • Application of INC-Seq for 16S rRNA-based bacterial profiling using the MinION platform.

Main Results:

  • Generation of full-length 16S rRNA amplicon sequences with a median accuracy exceeding 97%.
  • Achieved accurate species-level classification and identification of low-abundance species (0.1%).

Conclusions:

  • INC-Seq enables robust quantification of relative microbial abundances.
  • Provides a cost-effective and efficient approach for pathogen detection and microbiome profiling on the MinION.