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Related Concept Videos

RNA-seq03:21

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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. 
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Related Experiment Video

Updated: Jun 26, 2026

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

A Lightweight Workflow for Targeted Long-Read Transcriptomic Profiling Using Oxford Nanopore Sequencing.

Mariya Levkova1,2

  • 1Department of Medical Genetics, Medical University Varna, Marin Drinov Str. 55, 9000 Varna, Bulgaria.

Methods and Protocols
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

We developed a lightweight pipeline for targeted long-read RNA sequencing analysis using Oxford Nanopore Technologies (ONT) data. This reproducible workflow enables efficient gene expression profiling, even with limited computational resources.

Keywords:
FFPE transcriptomicsOxford Nanopore sequencingbioinformatics pipelinelong-read transcriptomicstargeted RNA sequencing

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Last Updated: Jun 26, 2026

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Long-read sequencing offers portability for small-scale studies.
  • Existing RNA-seq frameworks are resource-intensive and designed for whole-transcriptome analysis.

Purpose of the Study:

  • To present a lightweight, reproducible computational pipeline for targeted long-read transcriptomic profiling.
  • To enable efficient analysis of Oxford Nanopore Technologies (ONT) cDNA sequencing data.

Main Methods:

  • Development of a computational pipeline for targeted alignment of ONT cDNA sequencing data.
  • Utilized minimap2 for alignment to a custom transcript reference panel.
  • Implemented reads-per-million (RPM) for gene-level read counting and normalization.

Main Results:

  • Successfully evaluated the pipeline on FFPE colorectal carcinoma samples (MSI-high).
  • Enabled rapid quantification of mismatch repair gene expression and detection of immune markers (CD8A, PDCD1, HAVCR2).
  • Demonstrated applicability across multiplexed barcode samples.

Conclusions:

  • The pipeline offers a simple, reproducible, and computationally efficient framework for targeted transcriptomic analysis.
  • Facilitates the adoption of ONT-based transcriptomic profiling in resource-constrained environments.