ArticleLife science alliance2021
Statistical guidelines for quality control of next-generation sequencing techniques.
Article in Life science alliance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- The Role of Next-Generation Sequencing in Cardiovascular Disease: A New Era of Precision Cardiology.Life (Basel, Switzerland) · 2026Review
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets.Genome biology · 2024Article
- Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.BMC bioinformatics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
More and more next-generation sequencing (NGS) data are made available every day. However, the quality of this data is not always guaranteed. Available quality control tools require profound knowledge to correctly interpret the multiplicity of quality features. Moreover, it is usually difficult to know if quality features are relevant in all experimental conditions. Therefore, the NGS community would highly benefit from condition-specific data-driven guidelines derived from many publicly available experiments, which reflect routinely generated NGS data. In this work, we have characterized well-known quality guidelines and related features in big datasets and concluded that they are too limited for assessing the quality of a given NGS file accurately. Therefore, we present new data-driven guidelines derived from the statistical analysis of many public datasets using quality features calculated by common bioinformatics tools. Thanks to this approach, we confirm the high relevance of genome mapping statistics to assess the quality of the data, and we demonstrate the limited scope of some quality features that are not relevant in all conditions. Our guidelines are available at https://cbdm.uni-mainz.de/ngs-guidelines.
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