All, We have recently acquired an Agilent 6520 QTOF and are finding that the performance of different search engines (with post-analysis by PeptideProphet / ProteinProphet), and effects of using different fragment mass tolerances are more complicated than for our other instruments (Orbtrap / Bruker HCT / Waters QTOF / Bruker HCT / ABI 4800).
Example: On a yeast lysate sample converted to mzXML with Trapper, the number of spectrum matches at 1% FDR with fragment mass tol. set to extremes of 0.01Da and 0.5 Da respectively is: Mascot: 759 | 2182 Tandem K-Score: 954 | 954 Tandem Native: 3879 | N/A (PeptideProphet cannot fit models) OMSSA: 3762 | 3119 Mascot performs very poorly with a very tight MS/MS tolerance... we believe low scores are being caused by unmatched relatively intense ions in the low m/z range. If the tolerance is widened then these ions are often matched but with very large mass errors (so the matches are unlikely to be correct given the instruments accuracy). K-Score usually outperforms native Tandem scoring on other data, but the opposite seems true here. In fact, the PeptideProphet models for the K- Score data fit very poorly. That said, the tandem native f-val distributions at 0.5Da fragment tol. are strange, and PeptideProphet cannot fit to them. I just wondered whether anyone else has had similar experiences with Agilent QTOF data, and has a workflow that is working well? OMSSA and Tandem native results with tight tolerances look good, but we don't won't to abandon the other search engines. Many Thanks, DT --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "spctools-discuss" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [email protected] For more options, visit this group at http://groups.google.com/group/spctools-discuss?hl=en -~----------~----~----~----~------~----~------~--~---
