Re: [ccp4bb] merging anisotropic datasets

2014-11-13 Thread Harry Powell
Hi It's possible (though Garib has told me you shouldn't do it...) to integrate using anisotropic limits with Mosflm - it's pretty straightforward. Some users have told me it really helped. On 12 Nov 2014, at 21:26, Robert Keenan wrote: > > > I have three datasets of varying quality collecte

Re: [ccp4bb] merging anisotropic datasets

2014-11-13 Thread Tim Gruene
Dear Robert, the data sets don't seem too anisotropic, and the week reflections in poorly reflecting direction of dataset 1 that you include by setting the resolution range to 3.5A may not deteriorate the overall scaling that much. They may actually be kicked out as outliers and not used for scal

Re: [ccp4bb] merging anisotropic datasets

2014-11-13 Thread James Foadi
BLEND is mainly applicable to cases with many more than 3 data sets. So I would say it does notadd anything useful to what already suggested by Matthias. I have had positive results when scaling and merging several (unmerged) anisotropic data togetherwith POINTLESS / AIMLESS. The main reason fo

Re: [ccp4bb] merging anisotropic datasets

2014-11-12 Thread Antony Oliver
Would the CCP4 program BLEND be a suitable initial option? And then the anisotropic server? Tony. --- sent from my mobile account --- On 12 Nov 2014, at 21:29, Robert Keenan mailto:bkee...@uchicago.edu>> wrote: I have three datasets of varying quality collected from different regions of a

Re: [ccp4bb] merging anisotropic datasets

2014-11-12 Thread Matthias Zebisch
Hi Bob, we have done elliptic truncation before merging/scaling as lined out in 2012 Zebisch (JMB). However, I see no reason not to scale the 3 runs together with aimless (to lets say 3.4A) and then subject the resulting merged file to the sawata server. Best, Matthias -

[ccp4bb] merging anisotropic datasets

2014-11-12 Thread Robert Keenan
I have three datasets of varying quality collected from different regions of a single crystal. In each case, the data are anisotropic (from Aimless using CC1/2>0.5): Dataset 1: 3.5, 3.5 5.5 A Dataset 2: 4.2, 4.3, 4.8 A Dataset 3: 3.7, 3.9, 4.4 A I initially took a simple-minded approach an