Evaluation of sequencing reads at scale using rdeval.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The rapid increase in sequencing data necessitates efficient tools for managing and analyzing summary statistics.
• Evaluating sequencing data quality and characteristics is crucial for large-scale genome projects like the Vertebrate Genomes Project (VGP).

Purpose of the Study:

• To develop a standalone tool, rdeval, for rapid computation and dynamic display of sequencing read metrics.
• To address the community need for efficient sequencing data evaluation and storage.

Main Methods:

• Rdeval computes sequencing read metrics and can store them in compressed 'sketches' for efficient recall.
• The tool supports conversion between various file formats (e.g., FASTA, BAM, CRAM) for optimized compression and access.
• Rdeval generates detailed visual reports with exportable data analytics.

Main Results:

• Rdeval provides significant data compression gains through read sketches.
• BAM format offers a good balance between compression and access speed, while CRAM provides maximum compression.
• Analysis of human and VGP data demonstrated rdeval's utility, revealing improvements in long-read sequencing quality and assembly benefits from increased coverage.

Conclusions:

• Rdeval is an efficient, standalone tool for evaluating sequencing read metrics, crucial for large-scale genomics.
• The tool enhances data management through compression and provides valuable insights via visualization, supporting genome assembly efforts.