How to avoid the pitfalls and get trustable results?

In the video, Sharon Glaysher, Sam Robson and Angela Beckett from the University of Portsmouth discuss some technical challenges and tips about testing during a pandemic

What are the major pitfalls to getting trustable results and how to avoid them?

Producing sequencing data at a large scale can be complex, mainly if you consider the range of interactions amongst different institutes, as well as the variables that may affect the quality of the data all the way from sample collection until the data reaches a server.

• Can you highlight problems that may affect the quality of the data produced during this journey?
• Did you face any pitfalls in your country/lab, you would like to share?
• In the UK, residual RNA samples from the testing labs were frequently used by other labs for sequencing. What if the testing labs changed the protocols used to extract the RNA? Can you think of problems arising from this?
• When dealing with large numbers of samples, these may have to be cherry-picked from extraction plates, as not all samples tested are positive or important for sequencing. What challenges can you identify here?
• Samples had to be transported across different institutes. Have you experienced problems associated with sample transport?
• Have you experienced fridge and freezer capacity problems during the pandemic and if so how were samples affected?
• As the demand worldwide was large there were problems with supply chains. What can labs do to cope with this?
• During a pandemic or an outbreak, labs process more samples than what is normal, and contaminations will be more likely to happen. How do you think this could lead to data misinterpretation?
• How can the lack of a sampling strategy cause biases in the resulting data analysis?