1,330
edits
Data quality: Difference between revisions
Jump to navigation
Jump to search
no edit summary
No edit summary |
No edit summary |
||
| Line 5: | Line 5: | ||
This limit is commonly based on average <math>I/\sigma</math>. Examples of such choices are: | This limit is commonly based on average <math>I/\sigma</math>. Examples of such choices are: | ||
* <math>I/\sigma=1</math> in the highest resolution shell | |||
* <math>I/\sigma=2</math> in the highest resolution shell | |||
* at least 50% of reflections in the highest resolution shell have <math>I/\sigma</math> > 2 | |||
* ... | |||
Some of these choices are more liberal than others (and so will result in higher resolution values). It is probably not worthwhile to argue which choice is the best, since it is indeed a matter of personal preference. | |||
There is probably not much reason to limit resolution by R<sub>merge</sub>. When the resolution limit is selected based on R<sub>merge</sub> being less than a certain cutoff, the argument is that in higher resolution shells the variation among independent measurements of the intensity of the same reflection is too high. But such variation is indeed bound to be high for weak reflections. R<sub>merge</sub> may and should be used as the measure of the overall data consistency (e.g. of two independent datasets the one that has higher R<sub>merge</sub> probably is noisier). | |||
- | Of course you can achieve lower R-factors in refinement by setting the resolution limit based on some cutoff value of R<sub>merge</sub>. It is perfectly OK to aspire low R-factors, but to achieve this by throwing away good data isn't. The better strategy probably is to choose a generous high resolution limit early during structure solution, and to decide near the end of the refinement, by inspecting maps and comparing model R-factors at different resolutions, at which resolution the useful signal vanishes in the noise. | ||
== Improved indicators for data quality == | |||
R<sub>merge</sub> is the wrong quantity to look at altogether, because | |||
* it depends on the multiplicity (unfortunately often called redundancy): the higher the multiplicity, the higher R<sub>merge</sub> becomes | * it depends on the multiplicity (unfortunately often called redundancy): the higher the multiplicity, the higher R<sub>merge</sub> becomes | ||
* it assesses data consistency, not the quality of the reduced data | * it assesses data consistency, not the quality of the reduced data | ||
This has been discussed by Diederichs and Karplus | This has been discussed by Diederichs and Karplus<ref name="DiKa97">K. Diederichs and P.A. Karplus (1997). Improved R-factors for diffraction data analysis in macromolecular crystallography. Nature Struct. Biol. 4, 269-275 [http://strucbio.biologie.uni-konstanz.de/strucbio/files/nsb-1997.pdf]</ref>), who suggest a multiplicity-independant version called R<sub>meas</sub>, which unfortunately is not used by everyone because the formula gives higher values than R<sub>merge</sub>. R-factors for data quality assessment were also suggested by Diederichs and Karplus, and Weiss and Hilgenfeld <ref name="WeHi97">M.S. Weiss and R. Hilgenfeld (1997) On the use of the merging R-factor as a quality indicator for X-ray data. J. Appl. Crystallogr. 30, 203-205 [http://dx.doi.org/10.1107/S0021889897003907]</ref>. Weiss <ref name="We01">M.S. Weiss (2001) Global indicators of X-ray data quality. J. Appl. Cryst. 34, 130-135 [http://dx.doi.org/10.1107/S0021889800018227]</ref> showed that these R-factors are indeed strongly correlated with the quality of the data. | ||
== References == | == References == | ||
<references/> | <references/> | ||