Identification of rogue datasets in serial crystallography

Advances in beamline optics, detectors and X-ray sources allow new techniques of crystallographic data collection. In serial crystallography, a large number of partial datasets from crystals of small volume are measured. Merging of datasets from different crystals in order to enhance data completene...

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Bibliographische Detailangaben
Veröffentlicht in:Journal of applied crystallography. - 1998. - 49(2016), Pt 3 vom: 01. Juni, Seite 1021-1028
1. Verfasser: Assmann, Greta (VerfasserIn)
Weitere Verfasser: Brehm, Wolfgang, Diederichs, Kay
Format: Aufsatz
Sprache:English
Veröffentlicht: 2016
Zugriff auf das übergeordnete Werk:Journal of applied crystallography
Schlagworte:Journal Article CC1/2 isomorphism model bias non-isomorphism outlier identification precision serial crystallography
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520 |a Advances in beamline optics, detectors and X-ray sources allow new techniques of crystallographic data collection. In serial crystallography, a large number of partial datasets from crystals of small volume are measured. Merging of datasets from different crystals in order to enhance data completeness and accuracy is only valid if the crystals are isomorphous, i.e. sufficiently similar in cell parameters, unit-cell contents and molecular structure. Identification and exclusion of non-isomorphous datasets is therefore indispensable and must be done by means of suitable indicators. To identify rogue datasets, the influence of each dataset on CC1/2 [Karplus & Diederichs (2012 ▸). Science, 336, 1030-1033], the correlation coefficient between pairs of intensities averaged in two randomly assigned subsets of observations, is evaluated. The presented method employs a precise calculation of CC1/2 that avoids the random assignment, and instead of using an overall CC1/2, an average over resolution shells is employed to obtain sensible results. The selection procedure was verified by measuring the correlation of observed (merged) intensities and intensities calculated from a model. It is found that inclusion and merging of non-isomorphous datasets may bias the refined model towards those datasets, and measures to reduce this effect are suggested 
650 4 |a Journal Article 
650 4 |a CC1/2 
650 4 |a isomorphism 
650 4 |a model bias 
650 4 |a non-isomorphism 
650 4 |a outlier identification 
650 4 |a precision 
650 4 |a serial crystallography 
700 1 |a Brehm, Wolfgang  |e verfasserin  |4 aut 
700 1 |a Diederichs, Kay  |e verfasserin  |4 aut 
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