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: [EMAIL PROTECTED]
**************************************
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
[EMAIL PROTECTED]
