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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. 
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Updated: Dec 29, 2025

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Opportunities and challenges in long-read sequencing data analysis.

Shanika L Amarasinghe1,2, Shian Su1,2, Xueyi Dong1,2

  • 1Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, Australia.

Genome Biology
|February 9, 2020
PubMed
Summary
This summary is machine-generated.

Long-read sequencing technologies are advancing, requiring specialized bioinformatics tools for genomic analysis. This review and database (long-read-tools.org) help researchers navigate these essential tools for genomics projects.

Keywords:
Data analysisLong-read sequencingOxford NanoporePacBio

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long-read sequencing technologies are rapidly improving in accuracy and throughput.
  • The expanding applications of long-read sequencing necessitate dedicated bioinformatics analysis tools.
  • The swift evolution of these tools can be overwhelming for researchers.

Purpose of the Study:

  • To review the current landscape of bioinformatics tools for long-read sequencing data.
  • To provide an interactive online database (long-read-tools.org) for tool discovery and browsing.
  • To highlight key areas such as error correction, base modification detection, and transcriptomics analysis.

Main Methods:

  • Systematic review of existing long-read sequencing analysis tools.
  • Development and curation of an online interactive database.
  • Focused discussion on specific analytical principles and challenges.

Main Results:

  • A comprehensive overview of available long-read analysis tools is presented.
  • The interactive database long-read-tools.org is launched to aid tool selection.
  • Key challenges in error correction, base modification detection, and long-read transcriptomics are identified.

Conclusions:

  • Navigating the rapidly evolving field of long-read sequencing analysis is facilitated by curated resources.
  • The developed database serves as a valuable aid for designing and analyzing long-read sequencing projects.
  • Further advancements are needed to address remaining challenges in long-read data analysis.