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Should I QC my data and what tools to use?
Should I QC my data and what tools to use?
Updated over a week ago

It is important to understand what data a particular analysis requires (e.g. recommended read lengths or total sequencing).

Before beginning any analyses, performing QC assessment is a good way to understand if your data meets these requirements or matches your expectations from the sequencing run.

While live basecalling, MinKNOW can provide real-time feedback, such as read quality and length. This information, presented as a run report, can be exported.

Additional QC metrics can be generated post-sequencing using one of several tools.

Third party tools exist such as PycoQC which can also be used to generate QC metrics from the `sequencing_summary.txt` file.

If you wish to generate metrics from only the fastq files, you can run any of the EPI2ME workflows that take fastq or fastq.gz files as the input and view the QC results under the QC and barcoding tab. Alternatively, you can use third-party tools like NanoPlot.

For third-party tools, please see their GitHub pages for installation and usage. To address any issues with these and other third-party tools, please post to the Issues tab on the individual GitHub repositories as we cannot offer support for third-party software.

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