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As leveling involves measuring vertical distances relative to a horizontal line of sight, it requires a graduated rod, called a level rod, for vertical measurements and an instrument called a level for a horizontal sight line. A level includes a high-powered telescope with a mechanism for leveling to ensure the line of sight is horizontal when the bubble in the spirit level is centered. Leveling rods, made of wood, metal, or fiberglass, are graduated in feet or meters and commonly used in two-...
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Updated: Feb 15, 2026

Sequencing of mRNA from Whole Blood using Nanopore Sequencing
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NanoPrePro: a fully equipped, fast, and memory-efficient preprocessor for nanopore transcriptomic sequencing.

Chia-Chen Chu1,2, Jhong-He Yu1, Shang-Che Kuo2,3

  • 1Department of Life Science and Institute of Plant Biology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Daan Dist., Taipei 10617, Taiwan.

Briefings in Bioinformatics
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

NanoPrePro accurately identifies full-length Oxford Nanopore Technology (ONT) reads by precisely locating adapters and primers. This new tool is faster and more memory-efficient than existing methods, setting a new standard for ONT data preprocessing.

Keywords:
Oxford nanopore technologiespreprocessingtranscriptome sequencing

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Preprocessing Oxford Nanopore Technology (ONT) sequencing reads for transcriptomics is crucial but lacks systematic investigation.
  • Current methods for ONT read preprocessing are ambiguous and require improvement for high precision.

Purpose of the Study:

  • To develop a streamlined and highly precise read preprocessor for ONT transcriptomic data.
  • To systematically investigate and improve the preprocessing of ONT sequencing reads.

Main Methods:

  • Developed NanoPrePro, a novel bioinformatics tool for identifying full-length ONT reads.
  • Utilized sequence similarity, adapter/primer location, and length for precise adapter/primer identification.
  • Implemented a self-optimizing parameter extraction function for customized user analysis.

Main Results:

  • NanoPrePro demonstrated superior performance compared to Pychopper on both simulated and real datasets.
  • Achieved a 38-fold increase in speed and reduced memory consumption.
  • Successfully identified full-length reads with high precision through accurate adapter/primer detection.

Conclusions:

  • NanoPrePro represents a state-of-the-art solution for ONT read preprocessing.
  • Offers enhanced speed, efficiency, and adaptability for ONT sequencing data analysis.
  • Provides a robust and customizable tool for transcriptomic studies using ONT technology.