Statistical guidelines for quality control of next-generation sequencing techniques.

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 g

SUBMITTER: Sprang M 

PROVIDER: S-EPMC8408346 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature