WGS Quality Control Standards

Describes a set of quality control metrics and their detailed definitions to facilitate exchange of results across initiatives

WGS Quality Control Standards describes a set of key quality control metrics and their detailed definitions that can be used as the basis for standardised guidelines for reporting quality control metrics for whole genome sequencing (WGS). Creating a common framework for QC of WGS results is needed to ensure that data generation adheres to published guidelines, and in turn, establishes confidence in the data quality and facilitates the exchange of results across initiatives.

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Benefits

  • Improved understanding of how metrics are calculated leading to accurate interpretation and comparison of results
  • Directly comparable QC results and assertion of data processing pipeline and workflow functional equivalence
  • Mitigate the need to re-do analysis on shared samples

Target users

Researchers, clinical laboratories, data generators, data custodians, and research institutes

Community resources

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For development and production samples, quality control (QC) of whole genome sequencing (WGS) results can be achieved through a range of tools that compute metrics from FASTQ, BAM, and/or VCF files. Recommendations on which metrics to include in routine QC have been discussed in published guidelines and continue to be actively developed, but standardised definitions and implementations of recommended QC metrics have yet to be addressed.

The WGS Quality Control Standards is a set of key quality control metrics and their detailed definitions. These can be used as the basis for standardised guidelines for reporting quality control metrics. Creating a common framework for QC of WGS results is needed to ensure that data generation adheres to published guidelines, and in turn, establishes confidence in the data quality and facilitates the exchange of results across initiatives. Work will complement existing guidelines by also providing a new file format/schema to make it easier to report QC metric outputs, tools for calculating them, and benchmarking resources that would aid in the interpretation and monitoring of results.


Don't see your name? Get in touch:

  • Nicolas Bertin
    Genome Institute of Singapore
  • Oliver Hofmann
    University of Melbourne Centre for Cancer Research