Using full or smart profiling in cases where quant files are being aggregated from separate analyses #1246
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Cajun-data
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Hi Vadim - I am looking for guidance on library generation from DIA data. For speeding up analyses, I have found it beneficial to run datasets in parallel on different CPUs/instances of DIA-NN and then perform a DIA library generation step combined with MBR on the resulting quant files together. You state this in your manual:
My question is how does your guidance apply to the situation I've described above? Which type of library generation would you recommend in this case?
I ask because I have tested both full and ID/RT/IM profiling and observed a large increase in protein identifications for full profiling in the data I am working with, but it is not entirely clear to me why there would be such a difference - perhaps full profiling has a less conservative FDR estimation?
For context, the DIA library I am working with is fairly small and the datasets are relatively consistent in terms of precursor overlap
Library size:
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