The application of hierarchical cluster analysis to the selection of isomorphous crystals

Giordano, R.; Leal, Ricardo M. F.; Bourenkov, Gleb; McSweeney, Seán; Попов, А. Н.

doi:10.1107/s0907444912006841

Cited by 83 publications

(103 citation statements)

References 25 publications

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“…Obviously, further research is needed to obtain meaningful prediction of the model quality resulting from the merging of slightly nonisomorphous data sets (Giordano et al, 2012).…”

Section: Application Of the Theory To The Specific Data-filtering Tesmentioning

confidence: 99%

Better models by discarding data?

Diederichs

Karplus

2013

Acta Crystallogr D Biol Cryst

215

179

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In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC 1/2 , that can be used for this purpose were characterized and it was shown that CC 1/2 has superior properties compared with 'merging' R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC 1/2 and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled 'pairedrefinement' tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC 1/2 is the one data-quality indicator for which the behaviour accurately reflects which of the alternative datahandling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.

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“…Obviously, further research is needed to obtain meaningful prediction of the model quality resulting from the merging of slightly nonisomorphous data sets (Giordano et al, 2012).…”

Section: Application Of the Theory To The Specific Data-filtering Tesmentioning

confidence: 99%

Better models by discarding data?

Diederichs

Karplus

2013

Acta Crystallogr D Biol Cryst

215

179

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“…Some of the current projects include efforts to explicitly link PDB entries (Protein Data Bank; wwPDB; Berman et al, 2000) to specific datasets, a detailed study of the effect of advanced data collection strategy software on data quality (Bourenkov & Popov, 2010;Incardona et al, 2009;Paithankar & Garman, 2010), the incorporation of hierarchical cluster analysis (Giordano et al, 2012) into the grouped data merging (x2.5), more extensive reporting of results, particularly those pertaining to phasing, in ISPyB, and, most importantly, an improved and unified method for the user to provide information about their samples (i.e. sequence information, anomalous scatterer type and number) that can be used to improve the success rate of automatic structure determination.…”

Section: Future Perspectivesmentioning

confidence: 99%

Automatic processing of macromolecular crystallography X-ray diffraction data at the ESRF

et al. 2013

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The development of automated high-intensity macromolecular crystallography (MX) beamlines at synchrotron facilities has resulted in a remarkable increase in sample throughput. Developments in X-ray detector technology now mean that complete X-ray diffraction datasets can be collected in less than one minute. Such high-speed collection, and the volumes of data that it produces, often make it difficult for even the most experienced users to cope with the deluge. However, the careful reduction of data during experimental sessions is often necessary for the success of a particular project or as an aid in decision making for subsequent experiments. Automated data reduction pipelines provide a fast and reliable alternative to user-initiated processing at the beamline. In order to provide such a pipeline for the MX user community of the European Synchrotron Radiation Facility (ESRF), a system for the rapid automatic processing of MX diffraction data from single and multiple positions on a single or multiple crystals has been developed. Standard integration and data analysis programs have been incorporated into the ESRF data collection, storage and computing environment, with the final results stored and displayed in an intuitive manner in the ISPyB (information system for protein crystallography beamlines) database, from which they are also available for download. In some cases, experimental phase information can be automatically determined from the processed data. Here, the system is described in detail.

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“…The statistical treatment of SSX data based on the hierarchical cluster analysis [24] allows readily matching complementary patterns from many different crystallites into groups [25]. A potential target for this approach is exploring experimentally the mosaic block (called here: microdomain) model ( Figure 4A) [26].…”

Section: Synchrotron Radiation Serial Crystallography (Ssx)mentioning

confidence: 99%