Hello everyone,
I am currently engaged in a genomic analysis project and am seeking the most effective method to colocalize signals derived from traits analyzed in my GWAS study with the summary statistics available on the OpenTargets platform.
Given the vast amount of data handled, including numerous signals and traits, my priority is to identify an approach that maximizes efficiency, minimizing both processing time and computational resources used.
Two strategies I am considering are:
- API Interaction: This method would allow direct and dynamic integration with OpenTargets data. However, I am concerned about the limit on the number of requests I could make before potentially facing server restrictions, such as being temporarily blocked for making too many queries.
- Data Download: Considering a more traditional approach, I could directly download the necessary databases. This would reduce dependency on the API and allow me the freedom to work offline, but it raises an important question: which specific database would be most useful to download for my needs? What are the best practices for updating and managing these large datasets locally?
I would greatly appreciate any advice or shared experiences, especially regarding the effectiveness and practical implications of the two methodologies mentioned above. Additionally, if anyone has experience with other methods or tools for handling this type of large-scale analysis, your insights would be extremely valuable.
Thank you in advance for your time and assistance.
Best regards,
Michele