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]
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