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Updated: Sep 14, 2025
07:30
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
Published on: June 8, 2020
12.3K
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)
|July 22, 2025
Summary
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.
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.
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