Identification of rogue datasets in serial crystallography


Dateien zu dieser Ressource

Prüfsumme: MD5:ad1ce98487506e6029036406868648d5

ASSMANN, Greta, Wolfgang BREHM, Kay DIEDERICHS, 2016. Identification of rogue datasets in serial crystallography. In: Journal of Applied Crystallography. 49(3), pp. 1021-1028. ISSN 0021-8898. eISSN 1600-5767. Available under: doi: 10.1107/S1600576716005471

@article{Assmann2016-06-01Ident-34844, title={Identification of rogue datasets in serial crystallography}, year={2016}, doi={10.1107/S1600576716005471}, number={3}, volume={49}, issn={0021-8898}, journal={Journal of Applied Crystallography}, pages={1021--1028}, author={Assmann, Greta and Brehm, Wolfgang and Diederichs, Kay} }

eng Assmann, Greta terms-of-use Identification of rogue datasets in serial crystallography Brehm, Wolfgang Brehm, Wolfgang 2016-07-20T08:41:54Z 2016-06-01 2016-07-20T08:41:54Z 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. Assmann, Greta Diederichs, Kay Diederichs, Kay

Dateiabrufe seit 20.07.2016 (Informationen über die Zugriffsstatistik)

Assmann_0-346654.pdf 68

Das Dokument erscheint in:

KOPS Suche


Mein Benutzerkonto