Xds nonisomorphism: Difference between revisions

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	[ftp://turn5.biologie.uni-konstanz.de/pub/linux_bin/xds_nonisomorphism xds_nonisomorphism][ftp://turn5.biologie.uni-konstanz.de/pub/sources/xds_nonisomorphism.f90] is a program that analyzes data sets stored in unmerged reflection files (typically called XDS_ASCII.HKL) as written by [[XDS]]. It implements equation 2 of the theory of [https://doi.org/10.1107/S2059798317000699 Diederichs (2017)]. Its purpose is the identification of non-isomorphous (i.e. dissimilar or less well related) data sets among other, more similar data sets. As a consequence of running xds_nonisomorphism, the user may choose to only merge the most isomorphous (similar) data sets, and to discard the non-isomorphous ones - or to analyze these separately. That choice is not done automatically by the program; rather it is assumed that the user will choose the isomorphous data sets based on the program output, and scale these e.g. with [[XSCALE]].		[ftp://turn5.biologie.uni-konstanz.de/pub/linux_bin/xds_nonisomorphism xds_nonisomorphism][ftp://turn5.biologie.uni-konstanz.de/pub/sources/xds_nonisomorphism.f90] is a program that analyzes data sets (typically, less than 10) stored in unmerged reflection files (typically called XDS_ASCII.HKL) as written by [[XDS]]. It implements equation 2 of the theory of [https://doi.org/10.1107/S2059798317000699 Diederichs (2017)]. Its purpose is the identification of non-isomorphous (i.e. dissimilar or less well related) data sets among other, more similar data sets. As a consequence of running xds_nonisomorphism, the user may choose to only merge the most isomorphous (similar) data sets, and to discard the non-isomorphous ones - or to analyze these separately. That choice is not done automatically by the program; rather it is assumed that the user will choose the isomorphous data sets based on the program output, and scale these e.g. with [[XSCALE]].

	It should be noted that the result of the analyis does not depend on the amount of random error, which means it does not depend on the strengths of data sets - it works just as well for weakly or strongly exposed crystals, and for tiny or big ones.		It should be noted that the result of the analyis does not depend on the amount of random error, which means it does not depend on the strengths of data sets - it works just as well for weakly or strongly exposed crystals, and for tiny or big ones.