Benchmarking Data for SARS-CoV-2 Subgenomic RNA Detection Workflows

25 synthetic Illumina datasets and a real-world wastewater dataset form a case study for benchmarking bioinformatics tools. The data, created by Gabriele Leoni and last updated in April 2026, simulates shotgun and amplicon sequencing to assess variables like mutation profiles and aligner choice. Results revealed substantial performance variability, highlighting the need for systematic benchmarking to inform workflow selection in public health contexts.

Use Cases

• Benchmarking bioinformatics tool performance based on simulated sequencing data mentioned in the description
• Evaluating the impact of mutation profiles and read lengths on detection sensitivity based on the case study variables
• Comparing shotgun versus amplicon sequencing strategies for subgenomic RNA detection based on the described methods
• Informing aligner and primer design choices for targeted sequencing workflows based on the reported results

Strengths

• Includes 25 synthetic datasets with controlled variables for systematic comparison
• Contains a real-world wastewater dataset for validation against empirical data
• Released under a permissive CC-BY-4.0 license facilitating reuse
• Explicitly designed to address reproducibility and reliability in bioinformatics analysis

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• The primary file format is DOCX, which may require conversion for computational analysis

Provenance

Source

figshare

Collection Method

Data generated synthetically and collected from a real-world wastewater source for a benchmarking study.

Time Range

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Freshness

Last updated 2026-04-22 05:35:14; freshness should be verified

Geography

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