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Updated: Sep 14, 2025

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Evaluation of sequencing reads at scale using rdeval.

Giulio Formenti1, Bonhwang Koo1, Marco Sollitto1,2

  • 1The Vertebrate Genome Laboratory, The Rockefeller University, 1230 York Ave, New York City, NY 10065, United States.

Bioinformatics (Oxford, England)
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This summary is machine-generated.

A new tool, rdeval, efficiently computes and visualizes sequencing read metrics for large datasets. It aids in evaluating sequence data quality and supports genome assembly projects.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Rapid growth in sequencing data necessitates efficient tools for analysis.
  • Evaluating sequence read quality and statistics is crucial for large-scale genome projects.

Purpose of the Study:

  • To develop a standalone tool, rdeval, for rapid computation and visualization of sequencing read metrics.
  • To address the need for efficient sequence data evaluation in genomics.

Main Methods:

  • Rdeval is implemented in C++ for processing and R for visualization.
  • It can compute metrics on-the-fly or store them in 'snapshot' files.
  • Supports conversion between formats like FASTA, BAM, and CRAM.

Main Results:

  • Rdeval provides interactive display and detailed visual reports of sequencing data analytics.
  • BAM format offers a balance between compression and access speed.
  • Analysis of PacBio long-read data shows improvements in read length, quality, and coverage over time.

Conclusions:

  • Rdeval enhances the evaluation of sequencing data for genome projects.
  • The tool supports various sequencing platforms and data types.
  • Its availability across multiple platforms and integration with Bioconda/Galaxy increases accessibility.