XSCALE!!! ERROR

From: Muhammed bashir Khan
Date: 7 September 2011 20:39
Dear All;

I am trying to to xsale several data sets together, but it gives an error of

!!! ERROR !!! INSUFFICIENT NUMBER OF COMMON STRONG REFLECTIONS.
              PROGRAM IS UNABLE TO PRODUCE A SCALED DATA SET.

resolution of data sets is ranged from 3.25 to 5A

Any suggestion will be highly encouraged.

Thanks in adv.

Bashir

--
Muhammad Bashir Khan
**************************************************
Department for Structural and Computational Biology
Max F. Perutz Laboratories
University of Vienna
Campus Vienna Biocenter 5
A-1030 Vienna
Austria

Austria


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From: Nicolas Foos
Hello Bashir,

Maybe it's because your dataset present too much weak diffraction spots. You can try to change the setting for the strong pixel detection. You can Try to decrease a little bit de I/sigma. Have you define the resolution range?

I read on your message that you resolution start at 5 A. To my mind it's too high, you should have more low resolution. Perhaps your problem com from this specificity of you dataset.

Try first  to set your resolution range from inf. to 3.25

Hope to help you.

Nicolas

Le 07/09/11 21:39, Muhammed bashir Khan a écrit :

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From: Christian Roth
Am Mittwoch 07 September 2011 21:39:07 schrieb Muhammed bashir Khan:
Hi Bashir,

we had already such an error twice. In one case one of the integration failed
and the XDS_ASCII.HKL was rubbish. there was the wrong space group and so no
common reflections.
In an other case many overloaded reflection led to  too few reflections which
could be scaled together, because more than 50 % of the reflections were
rejected in the CORRECT step.  In this case we had to recollect the data.

Christian

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From: Kay Diederichs
Bashir,

the problem is what the error message tries to convey: scaling _requires_ common reflections, and there are two or more partitions of your "several data sets" which do not overlap in this sense. xscale prints out the number of common reflections ... inspect the list in XSCALE.LP !

Two ways to proceed:
- find out whether there are datasets that have no common reflections with any of the others. Remove those, and scale the others.
- add more datasets

HTH,

Kay
--
Kay Diederichs                http://strucbio.biologie.uni-konstanz.de
Fachbereich Biologie, Universität Konstanz, Box M647, D-78457 Konstanz