Research Topic

Quality assessment and control of high-throughput sequencing data

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About this Research Topic

Initial data analysis and quality control of next-generation sequencing (NGS) data is a fascinating yet under-published research area. Every NGS machine, whether benchtop or high-throughput, whether for single lab-use or in a large genome centre, will be associated with a data QC pipeline. The aim of this ...

Initial data analysis and quality control of next-generation sequencing (NGS) data is a fascinating yet under-published research area. Every NGS machine, whether benchtop or high-throughput, whether for single lab-use or in a large genome centre, will be associated with a data QC pipeline. The aim of this research topic is to unveil those pipelines, to reveal the quality metrics that scientists throughout the world apply to NGS data, and to reveal the common artefacts seen and how they can be managed. There are a number of software applications specifically written to QC NGS data, and this research topic will also cover those – including initial publication, but also reviews and updates to software that is already published. The research topic aims to give a platform to bioinformaticians to publish important work (good data QC underlies every area of science) that may otherwise go unpublished, to reveal common themes and metrics used by sequencing facilities throughout the world to assess the success or failure of sequencing runs, and to reveal and discuss common artefacts in all NGS technologies.


Important Note: All contributions to this Research Topic must be in line with the scope of the specialty and field to which they are submitted, as defined in their mission statements. Manuscripts discovered during any stage of peer review to be outside of the scope may be transferred to a suitable section or field, or withdrawn from review.

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