If you have outliers - worry about why? (By the way - what is the multiplicity?}
Look at the AIMLESS list of rejections/ the scale factor for different batches/ reports of ice rings/ etc There has to be a reason, and with snsible reprocessing you can probably but much better resolution data (therefore a much better structure ] and reduce the noise. There are many reasons for data problems, and AIMLESS gives plots and output to help you identify how to sort these out. One common one is radiation damage - you can usually see this in the batch scaling - and you may get better data by omitting the last images. Then Ice rings are a constant problem - these are reported usually in the data processing.. Eleanor On 29 March 2017 at 17:58, Jeffrey, Philip D. <pjeff...@princeton.edu> wrote: > Juliana, > > I think if you compare otherwise equivalent refinement runs in > phenix.refine with > refinement.input.xray_data.outliers_rejection=True (the default) > and > refinement.input.xray_data.outliers_rejection=False > then this will tell you if there's any meaningful difference in the > refinement statistics (or map). This doesn't mean you have to use > phenix.refine on your structure beyond that point, just that this is likely > the cleanest comparison that I can think of. > > Phil Jeffrey > Princeton > ------------------------------ > *From:* CCP4 bulletin board [CCP4BB@JISCMAIL.AC.UK] on behalf of Juliana > Ferreira de Oliveira [juliana.olive...@lnbio.cnpem.br] > *Sent:* Wednesday, March 29, 2017 9:54 AM > *To:* CCP4BB@JISCMAIL.AC.UK > *Subject:* [ccp4bb] Large number of outliers in the dataset > > Hello, > > I have one dataset at 2.3 Å (probably it can be better, I/σ = 2.1 and > CC1/2 = 0.779, the summary data is below), but when I perform Xtriage > analysis it says that “There are a large number of outliers in the data”. > The space group is P212121. When I refine the MR solution the Rfree stops > around 30% and it doesn´t decrease (in fact if I continue refining it > starts to increase). > > The Wilson plot graph is not fitting very well between 2.3 and 2.6 Å: > > > > > > So I decided to cut the data at 2.6A and Xtriage analysis doesn’t notify > about outliers anymore. I could refine the MR solution very well, the final > Rwork is 0.2427 and Rfree = 0.2730 and validation on Phenix results in a > good structure. > > I run Zanuda to confirm the space group and it says that the space group > assignment seems to be correct. > > Do you think that I can improve my structure and solve it at 2.3 Å or > better? Or I can finish it with 2.6 Å? To publish at 2.6 Å I need to > justify the resolution cut, right? What should I say? > > Thank you for your help! > > Regards, > > Juliana > > > > Summary data: > > Overall InnerShell OuterShell > > Low resolution limit 51.51 51.51 > 2.42 > > High resolution limit 2.30 > 7.27 2.30 > > Rmerge 0.147 > 0.054 0.487 > > Rmerge in top intensity bin 0.080 > - - > > Rmeas (within I+/I-) 0.155 0.057 > 0.516 > > Rmeas (all I+ & I-) 0.155 0.057 > 0.516 > > Rpim (within I+/I-) 0.048 0.017 > 0.164 > > Rpim (all I+ & I-) 0.048 0.017 > 0.164 > > Fractional partial bias -0.006 -0.003 > 0.146 > > Total number of observations 83988 2907 > 11885 > > Total number unique 8145 307 > 1167 > > Mean((I)/sd(I)) 9.3 > 23.9 2.1 > > Mn(I) half-set correlation CC(1/2) 0.991 0.998 > 0.779 > > Completeness 99.9 99.5 > 100.0 > > Multiplicity 10.3 > 9.5 10.2 > > > > Average unit cell: 37.57 51.51 88.75 90.00 90.00 90.00 > > Space group: P212121 > > Average mosaicity: 1.90 > > > > > > Juliana Ferreira de Oliveira > > Brazilian Laboratory of Biosciences - LNBio > > Brazilian Center for Research in Energy and Materials - CNPEM > > Campinas-SP, Brazil > > >
