An Imbalanced Dataset with Multiple Feature Representations for Studying Quality Control of Next-Generation Sequencing
Next-generation sequencing (NGS) is a key technique for studying the DNA and RNA of organisms. However, identifying quality problems in NGS data across different experimental settings remains challenging. To develop automated quality-control tools, researchers require datasets with features that capture the characteristics of quality problems. Existing NGS repositories, however, offer only a limited number of quality-related features. To address this gap, we propose a dataset derived from 37,491 NGS samples with two types of quality-related feature representations. The first type consists of 34 features derived from quality control tools (QC-34 features). The second type has a variable nu...
| Authors | Philipp Röchner, Clarissa Krämer, Johannes U Mayer et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 4 April 2026 |
| Last updated at source | 21 April 2026 |
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