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I have observed something similar myself using Saint in a Bruker Smart6K detector and using denzo in lab and syncrotron detectors. First the I over sigma never really drops to zero, no mater how much over your real resolution limit you integrate. Second, if I integrate to the visual resolution limit of, say, 1.5A, I get nice dataset statistics. If I now re-integrate (and re-scale) to 1.2A, thus including mostly empty (background) pixels everywhere, then cut the dataset after scaling to the same 1.5A limit, the statistics are much worse, booth in I over sigma and Rint. (Sorry, no numbers here, I tried this sometime ago). I guess the integration is suffering at profile fitting level while the scaling suffers from general noise (those weak reflections between 1.5A and 1.2A will be half of your total data!). I would be careful to go much over the visual resolution limit. Jose. ************************************** Jose Antonio Cuesta-Seijo Cancer Genomics and Proteomics Ontario Cancer Institute, UHN MaRs TMDT Room 4-902M 101 College Street M5G 1L7 Toronto, On, Canada Phone: (416)581-7544 Fax: (416)581-7562 email: [log in to unmask] ************************************** On Mar 22, 2007, at 10:59 AM, Sue Roberts wrote: > I have a question about how the experimental sigmas are affected > when one includes resolution shells containing mostly unobserved > reflections. Does this vary with the data reduction software being > used? > > One thing I've noticed when scaling data (this with d*trek (Crystal > Clear) since it's the program I use most) is that I/sigma(I) of > reflections can change significantly when one changes the high > resolution cutoff. > > If I set the detector so that the edge is about where I stop seeing > reflections and integrate to the corner of the detector, I'll get a > dataset where I/sigma(I) is really compressed - there is a lot of > high resolution data with I/sigma(I) about 1, but for the lowest > resolution shell, the overall I/sigma(I) will be maybe 8-9. If the > data set is cutoff at a lower resolution (where I/sigma(I) in the > shell is about 2) and scaled, I/sigma(I) in the lowest resolution > shell will be maybe 20 or even higher (OK, there is a different > resolution cutoff for this shell, but if I look at individual > reflections, the trend holds). Since the maximum likelihood > refinements use sigmas for weighting this must affect the > refinement. My experience is that interpretation of the maps is > easier when the cut-off datasets are used. (Refinement is via > refmac5 or shelx). Also, I'm mostly talking about datasets from > well-diffracting crystals (better than 2 A). > > Sue > > > On Mar 22, 2007, at 2:29 AM, Eleanor Dodson wrote: > >> I feel that is rather severe for ML refinement - sometimes for >> instance it helps to use all the data from the images, integrating >> right into the corners, thus getting a very incomplete set for the >> highest resolution shell. But for exptl phasing it does not help >> to have many many weak reflections.. >> >> Is there any way of testing this though? Only way I can think of >> to refine against a poorer set with varying protocols, then >> improve crystals/data and see which protocol for the poorer data >> gave the best agreement for the model comparison? >> >> And even that is not decisive - presumably the data would have >> come from different crystals with maybe small diffs between the >> models.. >> Eleanor >> >> >> >> Shane Atwell wrote: >>> >>> Could someone point me to some standards for data quality, >>> especially for publishing structures? I'm wondering in particular >>> about highest shell completeness, multiplicity, sigma and Rmerge. >>> >>> A co-worker pointed me to a '97 article by Kleywegt and Jones: >>> >>> _http://xray.bmc.uu.se/gerard/gmrp/gmrp.html_ >>> >>> "To decide at which shell to cut off the resolution, we nowadays >>> tend to use the following criteria for the highest shell: >>> completeness > 80 %, multiplicity > 2, more than 60 % of the >>> reflections with I > 3 sigma(I), and Rmerge < 40 %. In our >>> opinion, it is better to have a good 1.8 Å structure, than a poor >>> 1.637 Å structure." >>> >>> Are these recommendations still valid with maximum likelihood >>> methods? We tend to use more data, especially in terms of the >>> Rmerge and sigma cuttoff. >>> >>> Thanks in advance, >>> >>> *Shane Atwell* >>> > > Sue Roberts > Biochemistry & Biopphysics > University of Arizona > > [log in to unmask]