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Last updated: Apr 27, 2018

Samples, data management and privacy

The first stages in a genomic study, sample collection and preparation, will have an impact on sequencing, analysis, returning results and publishing. Good sample collection and data management practices are key to preventing downstream issues and complications.

Sample source

The quality of the sample, DNA or RNA can greatly affect the robustness of the data and success of the study [1]. If the sample quality is poor, then the results will be poor.

Sample quality can be impacted by:

  • Sample source e.g. blood, saliva, tumour
  • How it was transported and stored
  • Preservation methods e.g. fresh, frozen or formalin-fixed tissue
  • Heterogeneity and contamination
  • Sample preparation by different people or kits

Not all methodologies, sequencing technologies, kits or analysis tools will be suitable for low quality samples and it is important to discuss options with your sequencing provider. 

For more information see - Wet laboratory.

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Have you thought about?

  • Are your methodologies, sequencing technologies or analysis tools suitable for your samples?
  • Can you access fresh samples instead of fixed or preserved samples?
  • Can you increase the power of your study by requesting access to samples from multiple family members?
Sample and data management

A complication that can have time-consuming and financial implications in genomics research is sample and metadata mix-ups in the laboratory or at sequencing providers. This can often be prevented by standardised sampling procedures, labelling samples consistently and good project oversight. 

Good data management practices implemented at the start of a project can also prevent mix-ups of samples, sequencing data and results. This is particularly important in genomic research where large volumes of data (including metadata that is stored separately) and files are generated and can easily be confused.

Sample mix-ups can sometimes be identified at the analysis stage but this can be costly and time consuming.

For more information see - Making yourself data capable

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Have you thought about?

  • How will you prevent samples mix-ups?
  • How you will manage the metadata so it will be easy to find and understand when it is time to analyse results or publish?

[1] E18 Genomic Sampling and Management of Genomic Data Guidance for Industry. U.S. (2018) Department of Health and Human Services Food and Drug Administration. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM504556.pdf