Yes. But we have not published this. Mark Robien and I did a "systematic" study on about 30 data sets while we were at SGPP. The easy cases can be processed with anything the difficult cases worked only with XDS. This was mostly SeMet data or HA data, so de novo phasing no MR stuff. If you compare the signal of the SeMet peaks between the different processing options you got the strongest peaks via XDS. Success was assessed using ShelxD for finding HA sites.
Jürgen - Jürgen Bosch Johns Hopkins Bloomberg School of Public Health Department of Biochemistry & Molecular Biology Johns Hopkins Malaria Research Institute 615 North Wolfe Street, W8708 Baltimore, MD 21205 Phone: +1-410-614-4742 Lab: +1-410-614-4894 Fax: +1-410-955-3655 http://web.mac.com/bosch_lab/<http://web.me.com/bosch_lab/> On Jan 28, 2011, at 8:37 AM, Van Den Berg, Bert wrote: I have heard this before. I’m wondering though, does anybody know of a systematic study where different data processing programs are compared with real-life, non-lysozyme data? Bert On 1/28/11 7:58 AM, "Bosch, Juergen" <jubo...@jhsph.edu<x-msg://257/jubo...@jhsph.edu>> wrote: I was a bit reductive with my statement (iPhone....) The equation below is suppose to read: If you have bad data, then you need to process with XDS in order to get the maximum out of your data. Thanks Tim, Jürgen - Jürgen Bosch Johns Hopkins Bloomberg School of Public Health Department of Biochemistry & Molecular Biology Johns Hopkins Malaria Research Institute 615 North Wolfe Street, W8708 Baltimore, MD 21205 Phone: +1-410-614-4742 Lab: +1-410-614-4894 Fax: +1-410-955-3655 http://web.mac.com/bosch_lab/ <http://web.me.com/bosch_lab/> On Jan 28, 2011, at 7:44 AM, Tim Gruene wrote: Dear Jürgen, is this an assignment operator or an equal sign? For if it's the latter it could read that the result of processing data with XDS are bad data, which is rather rude and probably not what you meant. Tim On Fri, Jan 28, 2011 at 06:55:43AM -0500, Jürgen Bosch wrote: Bad data = processing with XDS Jürgen ...................... Jürgen Bosch Johns Hopkins Bloomberg School of Public Health Department of Biochemistry & Molecular Biology Johns Hopkins Malaria Research Institute 615 North Wolfe Street, W8708 Baltimore, MD 21205 Phone: +1-410-614-4742 Lab: +1-410-614-4894 Fax: +1-410-955-3655 http://web.mac.com/bosch_lab/ On Jan 28, 2011, at 6:46, José Trincão <trin...@dq.fct.unl.pt<x-msg://257/trin...@dq.fct.unl.pt>> wrote: Hello all, I have been trying to squeeze the most out of a bad data set (P1, anisotropic, crystals not reproducible). I had very incomplete data due to high mosaicity and lots of overlaps. The completeness was about 80% overall to ~3A. Yesterday I noticed that I could process the data much better fixing the mosaicity to 0.5 in imosflm. I got about 95% complete up to 2.5A but with a multiplicity of 1.7. I tried to integrate the same data fixing the mosaicity at different values ranging from 0.2 to 0.6 and saw the trend in completeness, Rmerge and multiplicity. Now, is there any reason why I should not just merge all these together and feed them to scala in order to increase multiplicity? Am I missing something? Thanks for any comments! Jose José Trincão, PhD CQFB@FCT-UNL 2829-516 Caparica, Portugal "It's very hard to make predictions... especially about the future" - Niels Bohr
